Creator · mathbullet
Last updated · Sep 6, 2026
Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citat
Creator · mathbullet
Last updated · Sep 6, 2026
Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citat
Creator · mathbullet
Last updated · Sep 6, 2026
Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citat
Creator · mathbullet
Last updated · Sep 6, 2026
Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citat
Sandbox only
Install targets
Codex install prompt
Install the "paper-details" agent skill from https://github.com/mathbullet/skills/tree/main/plugins/paper-details/skills/paper-details. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"mathbullet-paper-details","task":"Install paper-details","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add mathbullet/skills --skill paper-details
Maintenance
fresh
4d since push
Risk
Needs review
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment.
GitHub quality
119
68/100 Quality · 74/100 Trust
Coverage tags
Review notes
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment. · The script uses `uv run`, which requires the `uv` tool to be installed; this is not mentioned in SKILL.md.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
119 GitHub stars
Repo activity
119 stars, 3 forks
Maintenance
4d since push
License
MIT
Install
npx skills add mathbullet/skills --skill paper-details
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add mathbullet/skills --skill paper-detailsDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mathbullet-paper-details/install
Agent should check
Copy prompt
Task: Use paper-details in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mathbullet-paper-details/install
Install command: npx skills add mathbullet/skills --skill paper-details
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mathbullet-paper-details/install
LLM text format
/api/skills/mathbullet-paper-details/install?format=text
Find alternatives
/api/skills/search?q=paper-details&limit=3
Agent prompt
Use paper-details for this task. Review https://www.openagentskill.com/api/skills/mathbullet-paper-details/install, then install with: npx skills add mathbullet/skills --skill paper-detailsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mathbullet-paper-details
LLM text
/api/registry/manifest/mathbullet-paper-details?format=text
Install alias
/api/registry/install/mathbullet-paper-details
Recommend
/api/registry/recommend?task=Use%20paper-details%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
RAG and knowledge
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO119 GitHub stars
Stars/forks activity
CHECK119 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A relentless interview to sharpen a plan or design.
--- name: paper-details description: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. ---
# Paper Details
Conventions for producing a detailed Markdown explainer of an academic paper. The aim of this skill is to describe the paper accurately, not to critique it.
This skill follows the shared sourced-writing conventions defined in `documenting-with-sources`. Read `documenting-with-sources` before drafting.
## 1. Deliverable structure
### 1.0 Output location
Write the explainer as a `.md` file under the project's `reports/` directory, where "the project" is the root that contains the source paper PDF. Create the directory if it does not exist.
- Path: `{project-root}/reports/{paper-filename-base}.md` - Example: if the PDF is at `/path/to/project/papers/foo.pdf`, the output is `/path/to/project/reports/foo.md`
Do not write next to the PDF, and do not write at the project root. Do not ask the user for the output path — determine it mechanically by the rule above.
### 1.1 Opening
In this order:
1. Title (`# {paper title} — Detailed Explainer`) 2. Bibliographic info (authors, affiliations, venue, year, arXiv/DOI, URL) 3. Full abstract — quote the original in a code block per `writing-quotation`; if the original is in a non-working language, place the translation alongside as a separate paragraph in the same block.
### 1.2 Body section structure
The body's section structure follows the paper's. If the paper has Section 1 Introduction, Section 2 Method, Section 3 Results, ..., the explainer uses the same order and the same headings.
Add explainer-only sections (e.g. "Strengths of the paper", "Limitations of the paper", "Source list") *after* the paper's own section structure.
### 1.3 Bullet lists vs prose
Pick the form by the nature of the content.
- Bullet lists fit enumerations of parallel items — variable lists, definitions of evaluation metrics, table-column descriptions, comparison points between methods, etc. When the content is genuinely list-shaped, the prose form blurs the boundaries between items. - Prose fits relationships, causal flow, contextual explanation. When the reader needs to understand why items appear together or how they form a single argument, prose is what holds it together.
### 1.4 Figures and tables: extract as images
Never reconstruct a figure or table from scratch (no hand-written HTML tables, no redrawn SVG figures). Extract them as images from the source paper and insert those images into the deliverable (`.md` or HTML). Hand reconstruction introduces transcription errors and loses the original layout — bold, underline, colour-coded legends — so it is forbidden.
Use the script bundled with this skill:
``` uv run {this-skill-dir}/scripts/extract_images.py <PDF path> ```
- The script finds `Figure N` / `Table N` captions in the PDF and clip-renders the figure/table region directly above each caption — the bounding box of vector drawings, rules, and embedded images — at 300 dpi. Figures and tables are usually drawn as vectors with no embedded raster, so extraction is region rendering, not pulling out an embedded image. - Output defaults to `{project-root}/images-from-papers/` as `{paper-filename-base}-fig{N}.png` / `{paper-filename-base}-table{N}.png`. The extraction list is recorded in `{base}-manifest.json` in the same directory. - A multi-panel float (e.g. one Table float that contains panels (a)–(f)) is extracted as a single image, matching the single float in the paper. - After extraction, insert the images from `images-from-papers/` into the deliverable. In `.md`, reference them by relative path, e.g. ``. In an HTML deliverable created with `html`, embedding as a base64 data URI is acceptable. - Add the translated caption as a separate paragraph below the image. The caption text inside the image stays in the original language (it is not redrawn), so the translation goes outside the image. - If the script occasionally drops a figure/table or includes too much margin, raise `--dpi` or inspect the output and adjust the pymupdf clip rectangle for that item. Either way, do not abandon the image-extraction approach.
## 2. Quotation and source reference
### 2.0 Output is built around translated quotation blocks
Build the explainer primarily out of "original quotation + translation" blocks that cover the paper's body in full. Do not make summarised, paraphrased prose the main act. Keep the reader able to check the original against the translation throughout the document.
Prose — the writer's own text — is a complement to the quotation-led flow, added only when one of the following holds:
- The connection between quotation blocks is unclear, and the relationship or logical flow between sections or paragraphs needs bridging. - A supplementary explanation — variable definitions, prerequisite knowledge, how to read a figure or table, the first-occurrence definition of a term — is genuinely useful to the reader.
Where neither holds, do not re-summarise the original in prose; let the quotation block speak for itself. Do not settle into a "quotes carry the gist, prose summarises" split.
To convey the paper's claims accurately, include direct quotations from the original. A summary alone does not let the reader judge whether the writer's interpretation is correct.
Within the `[label (YYYY/MM), location]` structure defined in `documenting-with-sources`, the `paper-details` skill fills the label slot differently depending on which work is referenced.
### Reference to the paper under review
When referring to the paper under review, omit the author and year and cite the position only, in the form `[p.X, Section Y.Z]`. Since the entire explainer is about a single paper, the author does not need to be repeated each time.
Examples: `[p.4, Section 1]`, `[p.21, (15)]`, `[p.31, Figure 2]`.
### Reference to other works
For other works that the paper cites, use `[author-short (YYYY)]` inline.
- Pin location with a section, page, table, or figure number. - Do not abbreviate the list of cited works with phrases like "...and others". List each work individually with its author and year. The reader of the explainer depends on this list — citing the explainer's host paper alone does not substitute for naming the works the paper references.
## 3. Equations
### 3.1 Syntax
- Equations are written in LaTeX. Inline as `$...$`, display as `$$...$$`. - Forbidden: putting equations inside a code block. Forbidden: writing equations in plain-text form, pseudo-code form, or anything other than LaTeX syntax.
### 3.2 Variable definitions
Every symbol in an equation is unknown to the reader until it is defined. Before presenting an equation, define every variable and symbol that appears in it. Do not place an equation without its variable definitions in scope.
In equation-heavy sections (theory, method formalisation), put a variable table at the top of the section. Group variables by role. Each entry includes:
- The symbol (in LaTeX). - What it means (one sentence). - A note that helps intuition (concrete example, value range, behaviour in special cases).
Example:
``` Inputs:
- $n$: number of characters in the input text. The length of the user's prompt. - $K$: the model's context-window length in characters. Inputs longer than this cannot be passed to the model directly.
Planner outputs:
- $k^*$: branching factor at each level — how many chunks to split into. With $k^*=5$ the input is split into five chunks at each level. - $\tau^*$: threshold below which the chunk is sent to the LLM as-is, without further splitting. With $\tau^*=26{,}000$, chunks of 26,000 characters or fewer go straight to the LLM. ```
In sections with few equations, defining variables in-line before and after each equation is acceptable — but the principle that no equation appears with undefined symbols still holds.
## 4. Numerical results
- Present experimental results in tables when possible. - Place the proposed method and baselines side by side. - Transcribe numbers exactly as they appear in the paper. Do not round or approximate. - Before showing the table, define each column and each metric in a preceding bullet list. By the time the reader sees the numbers, the meaning of each column is clear. - Do not use generic words like "accuracy" or "performance" loosely. Use the metric name the paper itself defines (classification accuracy, pass rate, pass@1, etc.). - If the same word is used in different senses across the paper (e.g. a method name that means "the best single result" in a table but "the entire procedure" in the body), call out the polysemy explicitly.
## 5. Describing experiments
### 5.1 Spell out the procedure
In experiment sections, the reader must be able to follow what was actually done. Do not omit:
- The concrete task steps (what the input is, what each step produces, what the final output is). - The data-split structure (search set / validation set / test set — what each is for, and which result corresponds to which split). - The search or optimisation procedure (initial state, number of iterations, what each iteration generates, the criterion under which the final result is selected).
### 5.2 Independence between sections
Each experiment section reads on its own. Do not refer back to "the method defined in Section X" with an unspecified abbreviation. Even when two experiments share a search procedure, restate it with the experiment-specific parameters in each section.
### 5.3 Consistent granularity
When the paper has multiple experiment sections, keep the level of detail consistent across them. Do not write the search procedure thoroughly in one section and dismiss it in one sentence in another.
### 5.4 Define concepts before use
Define every concept the first time it appears in the explainer, before using it. Do not omit concepts the paper itself defines. In particular, before presenting a table or a number, make sure every concept needed to read that number has already been defined.
## 6. Source list
Place the source list at the end of the explainer, formatted per `documenting-with-sources`. In addition to the paper under review, include every other work the explainer mentions.
## 7. Section-by-section subagent audit
A first-pass draft typically contains errors that a single re-read misses: numbers transcribed off by a digit, citation numbers mapped to the wrong reference, sentences whose translated meaning drifts from the original, citations from the paper that never made it into the explainer. Before treating the draft as done, audit it section by section with subagents.
### 7.1 Output location
Write audit results to `{cwd}/subagent-reviews/{NN-section-name}.md`, one file per section. Create the directory if it does not exist. Audits are separate artifacts from the explainer; do not put them under `reports/`.
### 7.2 Sectioning
Split the explainer into independent units that align with the paper's section structure:
- Abstract and bibliographic info (one unit) - Each top-level section of the paper body (Introduction, Background/Related Work, Method, Experiments, Limitations, Conclusion, etc.) - The source list at the end of the explainer
Larger sections (e.g. an Experiments section with multiple sub-experiments and tables) can be split further if a single auditor would face too much material. Keep one auditor per file.
### 7.3 Auditor brief
Spawn one general-purpose subagent per section, in parallel. The brief tells each subagent to:
1.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for paper-details, ready for a manual X post.
paper-details: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful descr... 119 stars https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x
Listing + install path for paper-details: https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x Install: npx skills add mathbullet/skills --skill paper-details
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to mathbullet but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
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[](https://www.openagentskill.com/skills/mathbullet-paper-details?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)mathbullet
@mathbullet
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
mono-color
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256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "paper-details" agent skill from https://github.com/mathbullet/skills/tree/main/plugins/paper-details/skills/paper-details. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"mathbullet-paper-details","task":"Install paper-details","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add mathbullet/skills --skill paper-details
Maintenance
fresh
4d since push
Risk
Needs review
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment.
GitHub quality
119
68/100 Quality · 74/100 Trust
Coverage tags
Review notes
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment. · The script uses `uv run`, which requires the `uv` tool to be installed; this is not mentioned in SKILL.md.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
119 GitHub stars
Repo activity
119 stars, 3 forks
Maintenance
4d since push
License
MIT
Install
npx skills add mathbullet/skills --skill paper-details
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add mathbullet/skills --skill paper-detailsDo not use when
Alternative
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Alternative
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npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mathbullet-paper-details/install
Agent should check
Copy prompt
Task: Use paper-details in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mathbullet-paper-details/install
Install command: npx skills add mathbullet/skills --skill paper-details
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mathbullet-paper-details/install
LLM text format
/api/skills/mathbullet-paper-details/install?format=text
Find alternatives
/api/skills/search?q=paper-details&limit=3
Agent prompt
Use paper-details for this task. Review https://www.openagentskill.com/api/skills/mathbullet-paper-details/install, then install with: npx skills add mathbullet/skills --skill paper-detailsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mathbullet-paper-details
LLM text
/api/registry/manifest/mathbullet-paper-details?format=text
Install alias
/api/registry/install/mathbullet-paper-details
Recommend
/api/registry/recommend?task=Use%20paper-details%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code
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A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
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Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
RAG and knowledge
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO119 GitHub stars
Stars/forks activity
CHECK119 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A relentless interview to sharpen a plan or design.
--- name: paper-details description: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. ---
# Paper Details
Conventions for producing a detailed Markdown explainer of an academic paper. The aim of this skill is to describe the paper accurately, not to critique it.
This skill follows the shared sourced-writing conventions defined in `documenting-with-sources`. Read `documenting-with-sources` before drafting.
## 1. Deliverable structure
### 1.0 Output location
Write the explainer as a `.md` file under the project's `reports/` directory, where "the project" is the root that contains the source paper PDF. Create the directory if it does not exist.
- Path: `{project-root}/reports/{paper-filename-base}.md` - Example: if the PDF is at `/path/to/project/papers/foo.pdf`, the output is `/path/to/project/reports/foo.md`
Do not write next to the PDF, and do not write at the project root. Do not ask the user for the output path — determine it mechanically by the rule above.
### 1.1 Opening
In this order:
1. Title (`# {paper title} — Detailed Explainer`) 2. Bibliographic info (authors, affiliations, venue, year, arXiv/DOI, URL) 3. Full abstract — quote the original in a code block per `writing-quotation`; if the original is in a non-working language, place the translation alongside as a separate paragraph in the same block.
### 1.2 Body section structure
The body's section structure follows the paper's. If the paper has Section 1 Introduction, Section 2 Method, Section 3 Results, ..., the explainer uses the same order and the same headings.
Add explainer-only sections (e.g. "Strengths of the paper", "Limitations of the paper", "Source list") *after* the paper's own section structure.
### 1.3 Bullet lists vs prose
Pick the form by the nature of the content.
- Bullet lists fit enumerations of parallel items — variable lists, definitions of evaluation metrics, table-column descriptions, comparison points between methods, etc. When the content is genuinely list-shaped, the prose form blurs the boundaries between items. - Prose fits relationships, causal flow, contextual explanation. When the reader needs to understand why items appear together or how they form a single argument, prose is what holds it together.
### 1.4 Figures and tables: extract as images
Never reconstruct a figure or table from scratch (no hand-written HTML tables, no redrawn SVG figures). Extract them as images from the source paper and insert those images into the deliverable (`.md` or HTML). Hand reconstruction introduces transcription errors and loses the original layout — bold, underline, colour-coded legends — so it is forbidden.
Use the script bundled with this skill:
``` uv run {this-skill-dir}/scripts/extract_images.py <PDF path> ```
- The script finds `Figure N` / `Table N` captions in the PDF and clip-renders the figure/table region directly above each caption — the bounding box of vector drawings, rules, and embedded images — at 300 dpi. Figures and tables are usually drawn as vectors with no embedded raster, so extraction is region rendering, not pulling out an embedded image. - Output defaults to `{project-root}/images-from-papers/` as `{paper-filename-base}-fig{N}.png` / `{paper-filename-base}-table{N}.png`. The extraction list is recorded in `{base}-manifest.json` in the same directory. - A multi-panel float (e.g. one Table float that contains panels (a)–(f)) is extracted as a single image, matching the single float in the paper. - After extraction, insert the images from `images-from-papers/` into the deliverable. In `.md`, reference them by relative path, e.g. ``. In an HTML deliverable created with `html`, embedding as a base64 data URI is acceptable. - Add the translated caption as a separate paragraph below the image. The caption text inside the image stays in the original language (it is not redrawn), so the translation goes outside the image. - If the script occasionally drops a figure/table or includes too much margin, raise `--dpi` or inspect the output and adjust the pymupdf clip rectangle for that item. Either way, do not abandon the image-extraction approach.
## 2. Quotation and source reference
### 2.0 Output is built around translated quotation blocks
Build the explainer primarily out of "original quotation + translation" blocks that cover the paper's body in full. Do not make summarised, paraphrased prose the main act. Keep the reader able to check the original against the translation throughout the document.
Prose — the writer's own text — is a complement to the quotation-led flow, added only when one of the following holds:
- The connection between quotation blocks is unclear, and the relationship or logical flow between sections or paragraphs needs bridging. - A supplementary explanation — variable definitions, prerequisite knowledge, how to read a figure or table, the first-occurrence definition of a term — is genuinely useful to the reader.
Where neither holds, do not re-summarise the original in prose; let the quotation block speak for itself. Do not settle into a "quotes carry the gist, prose summarises" split.
To convey the paper's claims accurately, include direct quotations from the original. A summary alone does not let the reader judge whether the writer's interpretation is correct.
Within the `[label (YYYY/MM), location]` structure defined in `documenting-with-sources`, the `paper-details` skill fills the label slot differently depending on which work is referenced.
### Reference to the paper under review
When referring to the paper under review, omit the author and year and cite the position only, in the form `[p.X, Section Y.Z]`. Since the entire explainer is about a single paper, the author does not need to be repeated each time.
Examples: `[p.4, Section 1]`, `[p.21, (15)]`, `[p.31, Figure 2]`.
### Reference to other works
For other works that the paper cites, use `[author-short (YYYY)]` inline.
- Pin location with a section, page, table, or figure number. - Do not abbreviate the list of cited works with phrases like "...and others". List each work individually with its author and year. The reader of the explainer depends on this list — citing the explainer's host paper alone does not substitute for naming the works the paper references.
## 3. Equations
### 3.1 Syntax
- Equations are written in LaTeX. Inline as `$...$`, display as `$$...$$`. - Forbidden: putting equations inside a code block. Forbidden: writing equations in plain-text form, pseudo-code form, or anything other than LaTeX syntax.
### 3.2 Variable definitions
Every symbol in an equation is unknown to the reader until it is defined. Before presenting an equation, define every variable and symbol that appears in it. Do not place an equation without its variable definitions in scope.
In equation-heavy sections (theory, method formalisation), put a variable table at the top of the section. Group variables by role. Each entry includes:
- The symbol (in LaTeX). - What it means (one sentence). - A note that helps intuition (concrete example, value range, behaviour in special cases).
Example:
``` Inputs:
- $n$: number of characters in the input text. The length of the user's prompt. - $K$: the model's context-window length in characters. Inputs longer than this cannot be passed to the model directly.
Planner outputs:
- $k^*$: branching factor at each level — how many chunks to split into. With $k^*=5$ the input is split into five chunks at each level. - $\tau^*$: threshold below which the chunk is sent to the LLM as-is, without further splitting. With $\tau^*=26{,}000$, chunks of 26,000 characters or fewer go straight to the LLM. ```
In sections with few equations, defining variables in-line before and after each equation is acceptable — but the principle that no equation appears with undefined symbols still holds.
## 4. Numerical results
- Present experimental results in tables when possible. - Place the proposed method and baselines side by side. - Transcribe numbers exactly as they appear in the paper. Do not round or approximate. - Before showing the table, define each column and each metric in a preceding bullet list. By the time the reader sees the numbers, the meaning of each column is clear. - Do not use generic words like "accuracy" or "performance" loosely. Use the metric name the paper itself defines (classification accuracy, pass rate, pass@1, etc.). - If the same word is used in different senses across the paper (e.g. a method name that means "the best single result" in a table but "the entire procedure" in the body), call out the polysemy explicitly.
## 5. Describing experiments
### 5.1 Spell out the procedure
In experiment sections, the reader must be able to follow what was actually done. Do not omit:
- The concrete task steps (what the input is, what each step produces, what the final output is). - The data-split structure (search set / validation set / test set — what each is for, and which result corresponds to which split). - The search or optimisation procedure (initial state, number of iterations, what each iteration generates, the criterion under which the final result is selected).
### 5.2 Independence between sections
Each experiment section reads on its own. Do not refer back to "the method defined in Section X" with an unspecified abbreviation. Even when two experiments share a search procedure, restate it with the experiment-specific parameters in each section.
### 5.3 Consistent granularity
When the paper has multiple experiment sections, keep the level of detail consistent across them. Do not write the search procedure thoroughly in one section and dismiss it in one sentence in another.
### 5.4 Define concepts before use
Define every concept the first time it appears in the explainer, before using it. Do not omit concepts the paper itself defines. In particular, before presenting a table or a number, make sure every concept needed to read that number has already been defined.
## 6. Source list
Place the source list at the end of the explainer, formatted per `documenting-with-sources`. In addition to the paper under review, include every other work the explainer mentions.
## 7. Section-by-section subagent audit
A first-pass draft typically contains errors that a single re-read misses: numbers transcribed off by a digit, citation numbers mapped to the wrong reference, sentences whose translated meaning drifts from the original, citations from the paper that never made it into the explainer. Before treating the draft as done, audit it section by section with subagents.
### 7.1 Output location
Write audit results to `{cwd}/subagent-reviews/{NN-section-name}.md`, one file per section. Create the directory if it does not exist. Audits are separate artifacts from the explainer; do not put them under `reports/`.
### 7.2 Sectioning
Split the explainer into independent units that align with the paper's section structure:
- Abstract and bibliographic info (one unit) - Each top-level section of the paper body (Introduction, Background/Related Work, Method, Experiments, Limitations, Conclusion, etc.) - The source list at the end of the explainer
Larger sections (e.g. an Experiments section with multiple sub-experiments and tables) can be split further if a single auditor would face too much material. Keep one auditor per file.
### 7.3 Auditor brief
Spawn one general-purpose subagent per section, in parallel. The brief tells each subagent to:
1.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for paper-details, ready for a manual X post.
paper-details: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful descr... 119 stars https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x
Listing + install path for paper-details: https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x Install: npx skills add mathbullet/skills --skill paper-details
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[](https://www.openagentskill.com/skills/mathbullet-paper-details?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)mathbullet
@mathbullet
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Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "paper-details" agent skill from https://github.com/mathbullet/skills/tree/main/plugins/paper-details/skills/paper-details. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"mathbullet-paper-details","task":"Install paper-details","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add mathbullet/skills --skill paper-details
Maintenance
fresh
4d since push
Risk
Needs review
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment.
GitHub quality
119
68/100 Quality · 74/100 Trust
Coverage tags
Review notes
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment. · The script uses `uv run`, which requires the `uv` tool to be installed; this is not mentioned in SKILL.md.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
119 GitHub stars
Repo activity
119 stars, 3 forks
Maintenance
4d since push
License
MIT
Install
npx skills add mathbullet/skills --skill paper-details
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add mathbullet/skills --skill paper-detailsDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mathbullet-paper-details/install
Agent should check
Copy prompt
Task: Use paper-details in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mathbullet-paper-details/install
Install command: npx skills add mathbullet/skills --skill paper-details
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mathbullet-paper-details/install
LLM text format
/api/skills/mathbullet-paper-details/install?format=text
Find alternatives
/api/skills/search?q=paper-details&limit=3
Agent prompt
Use paper-details for this task. Review https://www.openagentskill.com/api/skills/mathbullet-paper-details/install, then install with: npx skills add mathbullet/skills --skill paper-detailsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mathbullet-paper-details
LLM text
/api/registry/manifest/mathbullet-paper-details?format=text
Install alias
/api/registry/install/mathbullet-paper-details
Recommend
/api/registry/recommend?task=Use%20paper-details%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
RAG and knowledge
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO119 GitHub stars
Stars/forks activity
CHECK119 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A relentless interview to sharpen a plan or design.
--- name: paper-details description: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. ---
# Paper Details
Conventions for producing a detailed Markdown explainer of an academic paper. The aim of this skill is to describe the paper accurately, not to critique it.
This skill follows the shared sourced-writing conventions defined in `documenting-with-sources`. Read `documenting-with-sources` before drafting.
## 1. Deliverable structure
### 1.0 Output location
Write the explainer as a `.md` file under the project's `reports/` directory, where "the project" is the root that contains the source paper PDF. Create the directory if it does not exist.
- Path: `{project-root}/reports/{paper-filename-base}.md` - Example: if the PDF is at `/path/to/project/papers/foo.pdf`, the output is `/path/to/project/reports/foo.md`
Do not write next to the PDF, and do not write at the project root. Do not ask the user for the output path — determine it mechanically by the rule above.
### 1.1 Opening
In this order:
1. Title (`# {paper title} — Detailed Explainer`) 2. Bibliographic info (authors, affiliations, venue, year, arXiv/DOI, URL) 3. Full abstract — quote the original in a code block per `writing-quotation`; if the original is in a non-working language, place the translation alongside as a separate paragraph in the same block.
### 1.2 Body section structure
The body's section structure follows the paper's. If the paper has Section 1 Introduction, Section 2 Method, Section 3 Results, ..., the explainer uses the same order and the same headings.
Add explainer-only sections (e.g. "Strengths of the paper", "Limitations of the paper", "Source list") *after* the paper's own section structure.
### 1.3 Bullet lists vs prose
Pick the form by the nature of the content.
- Bullet lists fit enumerations of parallel items — variable lists, definitions of evaluation metrics, table-column descriptions, comparison points between methods, etc. When the content is genuinely list-shaped, the prose form blurs the boundaries between items. - Prose fits relationships, causal flow, contextual explanation. When the reader needs to understand why items appear together or how they form a single argument, prose is what holds it together.
### 1.4 Figures and tables: extract as images
Never reconstruct a figure or table from scratch (no hand-written HTML tables, no redrawn SVG figures). Extract them as images from the source paper and insert those images into the deliverable (`.md` or HTML). Hand reconstruction introduces transcription errors and loses the original layout — bold, underline, colour-coded legends — so it is forbidden.
Use the script bundled with this skill:
``` uv run {this-skill-dir}/scripts/extract_images.py <PDF path> ```
- The script finds `Figure N` / `Table N` captions in the PDF and clip-renders the figure/table region directly above each caption — the bounding box of vector drawings, rules, and embedded images — at 300 dpi. Figures and tables are usually drawn as vectors with no embedded raster, so extraction is region rendering, not pulling out an embedded image. - Output defaults to `{project-root}/images-from-papers/` as `{paper-filename-base}-fig{N}.png` / `{paper-filename-base}-table{N}.png`. The extraction list is recorded in `{base}-manifest.json` in the same directory. - A multi-panel float (e.g. one Table float that contains panels (a)–(f)) is extracted as a single image, matching the single float in the paper. - After extraction, insert the images from `images-from-papers/` into the deliverable. In `.md`, reference them by relative path, e.g. ``. In an HTML deliverable created with `html`, embedding as a base64 data URI is acceptable. - Add the translated caption as a separate paragraph below the image. The caption text inside the image stays in the original language (it is not redrawn), so the translation goes outside the image. - If the script occasionally drops a figure/table or includes too much margin, raise `--dpi` or inspect the output and adjust the pymupdf clip rectangle for that item. Either way, do not abandon the image-extraction approach.
## 2. Quotation and source reference
### 2.0 Output is built around translated quotation blocks
Build the explainer primarily out of "original quotation + translation" blocks that cover the paper's body in full. Do not make summarised, paraphrased prose the main act. Keep the reader able to check the original against the translation throughout the document.
Prose — the writer's own text — is a complement to the quotation-led flow, added only when one of the following holds:
- The connection between quotation blocks is unclear, and the relationship or logical flow between sections or paragraphs needs bridging. - A supplementary explanation — variable definitions, prerequisite knowledge, how to read a figure or table, the first-occurrence definition of a term — is genuinely useful to the reader.
Where neither holds, do not re-summarise the original in prose; let the quotation block speak for itself. Do not settle into a "quotes carry the gist, prose summarises" split.
To convey the paper's claims accurately, include direct quotations from the original. A summary alone does not let the reader judge whether the writer's interpretation is correct.
Within the `[label (YYYY/MM), location]` structure defined in `documenting-with-sources`, the `paper-details` skill fills the label slot differently depending on which work is referenced.
### Reference to the paper under review
When referring to the paper under review, omit the author and year and cite the position only, in the form `[p.X, Section Y.Z]`. Since the entire explainer is about a single paper, the author does not need to be repeated each time.
Examples: `[p.4, Section 1]`, `[p.21, (15)]`, `[p.31, Figure 2]`.
### Reference to other works
For other works that the paper cites, use `[author-short (YYYY)]` inline.
- Pin location with a section, page, table, or figure number. - Do not abbreviate the list of cited works with phrases like "...and others". List each work individually with its author and year. The reader of the explainer depends on this list — citing the explainer's host paper alone does not substitute for naming the works the paper references.
## 3. Equations
### 3.1 Syntax
- Equations are written in LaTeX. Inline as `$...$`, display as `$$...$$`. - Forbidden: putting equations inside a code block. Forbidden: writing equations in plain-text form, pseudo-code form, or anything other than LaTeX syntax.
### 3.2 Variable definitions
Every symbol in an equation is unknown to the reader until it is defined. Before presenting an equation, define every variable and symbol that appears in it. Do not place an equation without its variable definitions in scope.
In equation-heavy sections (theory, method formalisation), put a variable table at the top of the section. Group variables by role. Each entry includes:
- The symbol (in LaTeX). - What it means (one sentence). - A note that helps intuition (concrete example, value range, behaviour in special cases).
Example:
``` Inputs:
- $n$: number of characters in the input text. The length of the user's prompt. - $K$: the model's context-window length in characters. Inputs longer than this cannot be passed to the model directly.
Planner outputs:
- $k^*$: branching factor at each level — how many chunks to split into. With $k^*=5$ the input is split into five chunks at each level. - $\tau^*$: threshold below which the chunk is sent to the LLM as-is, without further splitting. With $\tau^*=26{,}000$, chunks of 26,000 characters or fewer go straight to the LLM. ```
In sections with few equations, defining variables in-line before and after each equation is acceptable — but the principle that no equation appears with undefined symbols still holds.
## 4. Numerical results
- Present experimental results in tables when possible. - Place the proposed method and baselines side by side. - Transcribe numbers exactly as they appear in the paper. Do not round or approximate. - Before showing the table, define each column and each metric in a preceding bullet list. By the time the reader sees the numbers, the meaning of each column is clear. - Do not use generic words like "accuracy" or "performance" loosely. Use the metric name the paper itself defines (classification accuracy, pass rate, pass@1, etc.). - If the same word is used in different senses across the paper (e.g. a method name that means "the best single result" in a table but "the entire procedure" in the body), call out the polysemy explicitly.
## 5. Describing experiments
### 5.1 Spell out the procedure
In experiment sections, the reader must be able to follow what was actually done. Do not omit:
- The concrete task steps (what the input is, what each step produces, what the final output is). - The data-split structure (search set / validation set / test set — what each is for, and which result corresponds to which split). - The search or optimisation procedure (initial state, number of iterations, what each iteration generates, the criterion under which the final result is selected).
### 5.2 Independence between sections
Each experiment section reads on its own. Do not refer back to "the method defined in Section X" with an unspecified abbreviation. Even when two experiments share a search procedure, restate it with the experiment-specific parameters in each section.
### 5.3 Consistent granularity
When the paper has multiple experiment sections, keep the level of detail consistent across them. Do not write the search procedure thoroughly in one section and dismiss it in one sentence in another.
### 5.4 Define concepts before use
Define every concept the first time it appears in the explainer, before using it. Do not omit concepts the paper itself defines. In particular, before presenting a table or a number, make sure every concept needed to read that number has already been defined.
## 6. Source list
Place the source list at the end of the explainer, formatted per `documenting-with-sources`. In addition to the paper under review, include every other work the explainer mentions.
## 7. Section-by-section subagent audit
A first-pass draft typically contains errors that a single re-read misses: numbers transcribed off by a digit, citation numbers mapped to the wrong reference, sentences whose translated meaning drifts from the original, citations from the paper that never made it into the explainer. Before treating the draft as done, audit it section by section with subagents.
### 7.1 Output location
Write audit results to `{cwd}/subagent-reviews/{NN-section-name}.md`, one file per section. Create the directory if it does not exist. Audits are separate artifacts from the explainer; do not put them under `reports/`.
### 7.2 Sectioning
Split the explainer into independent units that align with the paper's section structure:
- Abstract and bibliographic info (one unit) - Each top-level section of the paper body (Introduction, Background/Related Work, Method, Experiments, Limitations, Conclusion, etc.) - The source list at the end of the explainer
Larger sections (e.g. an Experiments section with multiple sub-experiments and tables) can be split further if a single auditor would face too much material. Keep one auditor per file.
### 7.3 Auditor brief
Spawn one general-purpose subagent per section, in parallel. The brief tells each subagent to:
1.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for paper-details, ready for a manual X post.
paper-details: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful descr... 119 stars https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x
Listing + install path for paper-details: https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x Install: npx skills add mathbullet/skills --skill paper-details
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@mathbullet
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "paper-details" agent skill from https://github.com/mathbullet/skills/tree/main/plugins/paper-details/skills/paper-details. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"mathbullet-paper-details","task":"Install paper-details","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add mathbullet/skills --skill paper-details
Maintenance
fresh
4d since push
Risk
Needs review
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment.
GitHub quality
119
68/100 Quality · 74/100 Trust
Coverage tags
Review notes
The skill depends on external skills (documenting-with-sources and writing-quotation) which are not included in this submission; ensure they are available in the agent environment. · The script uses `uv run`, which requires the `uv` tool to be installed; this is not mentioned in SKILL.md.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
119 GitHub stars
Repo activity
119 stars, 3 forks
Maintenance
4d since push
License
MIT
Install
npx skills add mathbullet/skills --skill paper-details
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add mathbullet/skills --skill paper-detailsDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mathbullet-paper-details/install
Agent should check
Copy prompt
Task: Use paper-details in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-details%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mathbullet-paper-details/install
Install command: npx skills add mathbullet/skills --skill paper-details
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mathbullet-paper-details/install
LLM text format
/api/skills/mathbullet-paper-details/install?format=text
Find alternatives
/api/skills/search?q=paper-details&limit=3
Agent prompt
Use paper-details for this task. Review https://www.openagentskill.com/api/skills/mathbullet-paper-details/install, then install with: npx skills add mathbullet/skills --skill paper-detailsRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mathbullet-paper-details
LLM text
/api/registry/manifest/mathbullet-paper-details?format=text
Install alias
/api/registry/install/mathbullet-paper-details
Recommend
/api/registry/recommend?task=Use%20paper-details%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
RAG and knowledge
Trust label
Prototype first
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO119 GitHub stars
Stars/forks activity
CHECK119 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
A relentless interview to sharpen a plan or design.
--- name: paper-details description: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful description, not critical review. Section structure mirrors the original paper, equations render as LaTeX with variable tables, citations to the paper under review use position only, and citations to other works use author-short form. Use when the user asks for a detailed paper write-up, a thorough paper explainer, or invokes "paper details". Depends on documenting-with-sources and writing-quotation. ---
# Paper Details
Conventions for producing a detailed Markdown explainer of an academic paper. The aim of this skill is to describe the paper accurately, not to critique it.
This skill follows the shared sourced-writing conventions defined in `documenting-with-sources`. Read `documenting-with-sources` before drafting.
## 1. Deliverable structure
### 1.0 Output location
Write the explainer as a `.md` file under the project's `reports/` directory, where "the project" is the root that contains the source paper PDF. Create the directory if it does not exist.
- Path: `{project-root}/reports/{paper-filename-base}.md` - Example: if the PDF is at `/path/to/project/papers/foo.pdf`, the output is `/path/to/project/reports/foo.md`
Do not write next to the PDF, and do not write at the project root. Do not ask the user for the output path — determine it mechanically by the rule above.
### 1.1 Opening
In this order:
1. Title (`# {paper title} — Detailed Explainer`) 2. Bibliographic info (authors, affiliations, venue, year, arXiv/DOI, URL) 3. Full abstract — quote the original in a code block per `writing-quotation`; if the original is in a non-working language, place the translation alongside as a separate paragraph in the same block.
### 1.2 Body section structure
The body's section structure follows the paper's. If the paper has Section 1 Introduction, Section 2 Method, Section 3 Results, ..., the explainer uses the same order and the same headings.
Add explainer-only sections (e.g. "Strengths of the paper", "Limitations of the paper", "Source list") *after* the paper's own section structure.
### 1.3 Bullet lists vs prose
Pick the form by the nature of the content.
- Bullet lists fit enumerations of parallel items — variable lists, definitions of evaluation metrics, table-column descriptions, comparison points between methods, etc. When the content is genuinely list-shaped, the prose form blurs the boundaries between items. - Prose fits relationships, causal flow, contextual explanation. When the reader needs to understand why items appear together or how they form a single argument, prose is what holds it together.
### 1.4 Figures and tables: extract as images
Never reconstruct a figure or table from scratch (no hand-written HTML tables, no redrawn SVG figures). Extract them as images from the source paper and insert those images into the deliverable (`.md` or HTML). Hand reconstruction introduces transcription errors and loses the original layout — bold, underline, colour-coded legends — so it is forbidden.
Use the script bundled with this skill:
``` uv run {this-skill-dir}/scripts/extract_images.py <PDF path> ```
- The script finds `Figure N` / `Table N` captions in the PDF and clip-renders the figure/table region directly above each caption — the bounding box of vector drawings, rules, and embedded images — at 300 dpi. Figures and tables are usually drawn as vectors with no embedded raster, so extraction is region rendering, not pulling out an embedded image. - Output defaults to `{project-root}/images-from-papers/` as `{paper-filename-base}-fig{N}.png` / `{paper-filename-base}-table{N}.png`. The extraction list is recorded in `{base}-manifest.json` in the same directory. - A multi-panel float (e.g. one Table float that contains panels (a)–(f)) is extracted as a single image, matching the single float in the paper. - After extraction, insert the images from `images-from-papers/` into the deliverable. In `.md`, reference them by relative path, e.g. ``. In an HTML deliverable created with `html`, embedding as a base64 data URI is acceptable. - Add the translated caption as a separate paragraph below the image. The caption text inside the image stays in the original language (it is not redrawn), so the translation goes outside the image. - If the script occasionally drops a figure/table or includes too much margin, raise `--dpi` or inspect the output and adjust the pymupdf clip rectangle for that item. Either way, do not abandon the image-extraction approach.
## 2. Quotation and source reference
### 2.0 Output is built around translated quotation blocks
Build the explainer primarily out of "original quotation + translation" blocks that cover the paper's body in full. Do not make summarised, paraphrased prose the main act. Keep the reader able to check the original against the translation throughout the document.
Prose — the writer's own text — is a complement to the quotation-led flow, added only when one of the following holds:
- The connection between quotation blocks is unclear, and the relationship or logical flow between sections or paragraphs needs bridging. - A supplementary explanation — variable definitions, prerequisite knowledge, how to read a figure or table, the first-occurrence definition of a term — is genuinely useful to the reader.
Where neither holds, do not re-summarise the original in prose; let the quotation block speak for itself. Do not settle into a "quotes carry the gist, prose summarises" split.
To convey the paper's claims accurately, include direct quotations from the original. A summary alone does not let the reader judge whether the writer's interpretation is correct.
Within the `[label (YYYY/MM), location]` structure defined in `documenting-with-sources`, the `paper-details` skill fills the label slot differently depending on which work is referenced.
### Reference to the paper under review
When referring to the paper under review, omit the author and year and cite the position only, in the form `[p.X, Section Y.Z]`. Since the entire explainer is about a single paper, the author does not need to be repeated each time.
Examples: `[p.4, Section 1]`, `[p.21, (15)]`, `[p.31, Figure 2]`.
### Reference to other works
For other works that the paper cites, use `[author-short (YYYY)]` inline.
- Pin location with a section, page, table, or figure number. - Do not abbreviate the list of cited works with phrases like "...and others". List each work individually with its author and year. The reader of the explainer depends on this list — citing the explainer's host paper alone does not substitute for naming the works the paper references.
## 3. Equations
### 3.1 Syntax
- Equations are written in LaTeX. Inline as `$...$`, display as `$$...$$`. - Forbidden: putting equations inside a code block. Forbidden: writing equations in plain-text form, pseudo-code form, or anything other than LaTeX syntax.
### 3.2 Variable definitions
Every symbol in an equation is unknown to the reader until it is defined. Before presenting an equation, define every variable and symbol that appears in it. Do not place an equation without its variable definitions in scope.
In equation-heavy sections (theory, method formalisation), put a variable table at the top of the section. Group variables by role. Each entry includes:
- The symbol (in LaTeX). - What it means (one sentence). - A note that helps intuition (concrete example, value range, behaviour in special cases).
Example:
``` Inputs:
- $n$: number of characters in the input text. The length of the user's prompt. - $K$: the model's context-window length in characters. Inputs longer than this cannot be passed to the model directly.
Planner outputs:
- $k^*$: branching factor at each level — how many chunks to split into. With $k^*=5$ the input is split into five chunks at each level. - $\tau^*$: threshold below which the chunk is sent to the LLM as-is, without further splitting. With $\tau^*=26{,}000$, chunks of 26,000 characters or fewer go straight to the LLM. ```
In sections with few equations, defining variables in-line before and after each equation is acceptable — but the principle that no equation appears with undefined symbols still holds.
## 4. Numerical results
- Present experimental results in tables when possible. - Place the proposed method and baselines side by side. - Transcribe numbers exactly as they appear in the paper. Do not round or approximate. - Before showing the table, define each column and each metric in a preceding bullet list. By the time the reader sees the numbers, the meaning of each column is clear. - Do not use generic words like "accuracy" or "performance" loosely. Use the metric name the paper itself defines (classification accuracy, pass rate, pass@1, etc.). - If the same word is used in different senses across the paper (e.g. a method name that means "the best single result" in a table but "the entire procedure" in the body), call out the polysemy explicitly.
## 5. Describing experiments
### 5.1 Spell out the procedure
In experiment sections, the reader must be able to follow what was actually done. Do not omit:
- The concrete task steps (what the input is, what each step produces, what the final output is). - The data-split structure (search set / validation set / test set — what each is for, and which result corresponds to which split). - The search or optimisation procedure (initial state, number of iterations, what each iteration generates, the criterion under which the final result is selected).
### 5.2 Independence between sections
Each experiment section reads on its own. Do not refer back to "the method defined in Section X" with an unspecified abbreviation. Even when two experiments share a search procedure, restate it with the experiment-specific parameters in each section.
### 5.3 Consistent granularity
When the paper has multiple experiment sections, keep the level of detail consistent across them. Do not write the search procedure thoroughly in one section and dismiss it in one sentence in another.
### 5.4 Define concepts before use
Define every concept the first time it appears in the explainer, before using it. Do not omit concepts the paper itself defines. In particular, before presenting a table or a number, make sure every concept needed to read that number has already been defined.
## 6. Source list
Place the source list at the end of the explainer, formatted per `documenting-with-sources`. In addition to the paper under review, include every other work the explainer mentions.
## 7. Section-by-section subagent audit
A first-pass draft typically contains errors that a single re-read misses: numbers transcribed off by a digit, citation numbers mapped to the wrong reference, sentences whose translated meaning drifts from the original, citations from the paper that never made it into the explainer. Before treating the draft as done, audit it section by section with subagents.
### 7.1 Output location
Write audit results to `{cwd}/subagent-reviews/{NN-section-name}.md`, one file per section. Create the directory if it does not exist. Audits are separate artifacts from the explainer; do not put them under `reports/`.
### 7.2 Sectioning
Split the explainer into independent units that align with the paper's section structure:
- Abstract and bibliographic info (one unit) - Each top-level section of the paper body (Introduction, Background/Related Work, Method, Experiments, Limitations, Conclusion, etc.) - The source list at the end of the explainer
Larger sections (e.g. an Experiments section with multiple sub-experiments and tables) can be split further if a single auditor would face too much material. Keep one auditor per file.
### 7.3 Auditor brief
Spawn one general-purpose subagent per section, in parallel. The brief tells each subagent to:
1.
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for paper-details, ready for a manual X post.
paper-details: Produce a detailed Markdown explainer of an academic paper. The skill aims for faithful descr... 119 stars https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x
Listing + install path for paper-details: https://www.openagentskill.com/skills/mathbullet-paper-details?ref=x Install: npx skills add mathbullet/skills --skill paper-details
Listing source
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@mathbullet
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsPermission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness