Creator · wanshuiyin
Last updated · Sep 7, 2026
Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed
Creator · wanshuiyin
Last updated · Sep 7, 2026
Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed
Creator · wanshuiyin
Last updated · Sep 7, 2026
Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed
Creator · wanshuiyin
Last updated · Sep 7, 2026
Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed
Sandbox only
Install targets
Codex install prompt
Install the "formula-derivation" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/formula-derivation. 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: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. 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":"wanshuiyin-formula-derivation","task":"Install formula-derivation","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
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
Maintenance
fresh
1d since push
Risk
Safe to try
The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure.
GitHub quality
16K
89/100 Quality · 81/100 Trust
Coverage tags
Review notes
The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure. · The documented excerpt does not show an explicit setup or limitations section, though the skill appears to need no setup.
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
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA 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
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
1d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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 wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivationDo not use when
Alternative
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Alternative
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npx skills add mvanhorn/last30days-skill -g
Alternative
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npx skills add Imbad0202/academic-research-skills
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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%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wanshuiyin-formula-derivation/install
Agent should check
Copy prompt
Task: Use formula-derivation in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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/wanshuiyin-formula-derivation/install
LLM text format
/api/skills/wanshuiyin-formula-derivation/install?format=text
Find alternatives
/api/skills/search?q=formula-derivation&limit=3
Agent prompt
Use formula-derivation for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivationRegistry 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/wanshuiyin-formula-derivation
LLM text
/api/registry/manifest/wanshuiyin-formula-derivation?format=text
Install alias
/api/registry/install/wanshuiyin-formula-derivation
Recommend
/api/registry/recommend?task=Use%20formula-derivation%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
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
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d 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
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
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--- name: formula-derivation description: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. argument-hint: "[problem-goal-current-formulas-or-notes]" allowed-tools: Read, Write, Edit, Grep, Glob ---
# Formula Derivation: Research Theory Line Construction
Build an honest derivation package, not a fake polished theorem story.
## Constants
- DEFAULT_DERIVATION_DOC = `DERIVATION_PACKAGE.md` in project root - STATUS = `COHERENT AS STATED | COHERENT AFTER REFRAMING / EXTRA ASSUMPTION | NOT YET COHERENT`
## Context: $ARGUMENTS
## Goal
Produce exactly one of: 1. a coherent derivation package for the original target 2. a reframed derivation package with corrected object / assumptions / scope 3. a blocker report explaining why the current notes cannot yet support a coherent derivation
## Inputs
Extract and normalize: - the target phenomenon, formula, relation, or theory line - the intended role of the derivation: - exact identity / algebra - proposition / local theorem - approximation - mechanism interpretation - explicit assumptions - notation and definitions - any user-provided formula chain, sketch, messy notes, or current draft - nearby local theory files if the request points to them - desired output style if specified: - internal alignment note - paper-style theory draft - blocker report
If the target, object, notation, or assumptions are ambiguous, state the exact interpretation you are using before deriving anything.
## Workflow
### Step 1: Gather Derivation Context Determine the target derivation file with this priority: 1. a file path explicitly specified by the user 2. a derivation draft already referenced in local notes 3. `DERIVATION_PACKAGE.md` in project root as the default target
Read the relevant local context: - the chosen target derivation file, if it already exists - any local theory notes, formula drafts, appendix notes, or files explicitly mentioned by the user
Extract: - target formula / theory goal - current formula chain - assumptions - notation - known blockers - desired output mode
### Step 2: Freeze the Target State explicitly: - what is being explained, derived, or supported - whether the immediate goal is: - identity / algebra - proposition - approximation - interpretation - what the derivation is expected to output in the end
Do not start symbolic manipulation before this is fixed.
### Step 3: Choose the Invariant Object Identify the single quantity or conceptual object that should organize the derivation.
Typical possibilities include: - objective / utility / loss - total cost / energy / welfare - conserved quantity / state variable - expected metric / effective rate / effective cost
If the current notes start from a narrower quantity, decide explicitly whether it is: - the true top-level object - a proxy - a local slice - an approximation
Do not let a convenient proxy silently replace the actual conceptual object.
### Step 4: Normalize Assumptions and Notation Restate: - all assumptions - all symbols - regime boundaries or special cases - which quantities are fixed, adaptive, or state dependent
Identify: - hidden assumptions - undefined notation - scope ambiguities - whether the current formula chain already mixes exact steps with approximations
Preserve the user's original notation unless a cleanup is necessary for coherence. If you adopt a cleaner internal formulation, keep that as a derivation device rather than silently replacing the user's target.
### Step 5: Classify the Derivation Steps For every nontrivial step, determine whether it is: - **identity**: exact algebraic reformulation - **proposition**: a claim requiring conditions - **approximation**: model simplification or surrogate - **interpretation**: prose-level meaning of a formula
Never merge these categories without signaling the transition. If one part is only interpretive, do not present it as if it were mathematically proved.
### Step 6: Build a Derivation Map Choose a derivation strategy, for example: - definition -> substitution -> simplification - primitive law -> intermediate variable -> target expression - global quantity -> perturbation -> decomposition - exact model -> approximation -> interpretable closed form - general dynamic object -> simplified slice -> local theorem -> return to general case
Then write a derivation map: - target formula or theory line - required intermediate identities or lemmas - which assumptions each nontrivial step uses - where approximations enter - where special-case and general-case regimes diverge or collapse
If the derivation needs a decomposition, derive it from the chosen global quantity. Do not make a split appear magically from one local variable itself.
### Step 7: Write the Derivation Document Write to the chosen target derivation file.
If the target derivation file already exists: - read it first - update the relevant section - do not blindly duplicate prior content
If the user does not specify a target, default to `DERIVATION_PACKAGE.md` in project root.
Do NOT write directly into paper sections or appendix `.tex` files unless the user explicitly asks for that target.
The derivation package must include: - target - status - invariant object - assumptions - notation - derivation strategy - derivation map - main derivation steps - remarks / interpretations - boundaries and non-claims
Writing rules: - do not hide gaps with words like "clearly", "obviously", or "similarly" - define every symbol before use - mark approximations explicitly - separate derivation body from remarks - if the true object is dynamic or state dependent but a simpler slice is analyzed, say so explicitly - if a formula line is only heuristic, label it honestly
### Step 8: Final Verification Before finishing the target derivation file, verify: - the target is explicit - the invariant object is stable across the derivation - every assumption used is stated - each formula step is correctly labeled as identity / proposition / approximation / interpretation - the derivation does not silently switch objects - special cases and general cases still belong to one theory line - boundaries and non-claims are stated
If the derivation still lacks a coherent object, stable assumptions, or an honest path from premises to result, downgrade the status and write a blocker report instead of forcing a clean story.
## Required File Structure
Write the target derivation file using this structure:
```md # Derivation Package
## Target [what is being derived or explained]
## Status COHERENT AS STATED / COHERENT AFTER REFRAMING / NOT YET COHERENT
## Invariant Object [top-level quantity organizing the derivation]
## Assumptions - ...
## Notation - ...
## Derivation Strategy [chosen route and why]
## Derivation Map 1. Target depends on ... 2. Intermediate step A uses ... 3. Approximation enters at ...
## Main Derivation Step 1. ... Step 2. ... ...
## Remarks and Interpretation - ...
## Boundaries and Non-Claims - ...
## Open Risks - ... ```
## Output Modes
### If the derivation is coherent as stated Write the full structure above with a clean derivation package.
### If the notes are close but not coherent yet Write: - the exact mismatch - the corrected invariant object, assumption, or scope - the reframed derivation package
### If the derivation cannot be made coherent honestly Write: - `Status: NOT YET COHERENT` - the exact blocker: - missing object - unstable assumptions - notation conflict - unsupported approximation - theorem-level claim without enough conditions - what extra assumption, reframe, or intermediate derivation would be needed
## Relationship to `proof-writer`
Use `formula-derivation` when the user says things like: - “我不知道怎么起这条推导主线” - “这个公式到底该从哪个量出发” - “帮我把理论搭顺” - “把说明文档变成可写进论文的公式文档” - “这几段公式之间逻辑不通”
Use `proof-writer` only after: - the exact claim is fixed - the assumptions are stable - the notation is settled - and the task is now to prove or refute that claim rigorously
## Chat Response
After writing the target derivation file, respond briefly with: - status - whether the target survived unchanged or had to be reframed - what file was updated
## Key Rules
- Never fabricate a coherent derivation if the object, assumptions, or scope do not support one. - Prefer reframing the derivation over overclaiming. - Separate assumptions, identities, propositions, approximations, and interpretations. - Keep one invariant object across special and general cases whenever possible. - Treat simplified constant-parameter cases as analysis slices, not as the conceptual main object. - If uncertainty remains, mark it explicitly in `Open Risks`; do not hide it in polished prose. - Coherence matters more than elegance.
Source provenance
Decision snapshot
15,840 GitHub stars
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 formula-derivation, ready for a manual X post.
formula-derivation: Structures and derives research formulas when the user wants to 推导公式, build a theory line, or... 15.8K stars https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x
Listing + install path for formula-derivation: https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x Install: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-deri...
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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Install targets
Codex install prompt
Install the "formula-derivation" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/formula-derivation. 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: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. 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":"wanshuiyin-formula-derivation","task":"Install formula-derivation","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
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
Maintenance
fresh
1d since push
Risk
Safe to try
The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure.
GitHub quality
16K
89/100 Quality · 81/100 Trust
Coverage tags
Review notes
The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure. · The documented excerpt does not show an explicit setup or limitations section, though the skill appears to need no setup.
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
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA 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
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
1d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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 wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivationDo not use when
Alternative
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Alternative
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Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
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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%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wanshuiyin-formula-derivation/install
Agent should check
Copy prompt
Task: Use formula-derivation in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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/wanshuiyin-formula-derivation/install
LLM text format
/api/skills/wanshuiyin-formula-derivation/install?format=text
Find alternatives
/api/skills/search?q=formula-derivation&limit=3
Agent prompt
Use formula-derivation for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivationRegistry 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/wanshuiyin-formula-derivation
LLM text
/api/registry/manifest/wanshuiyin-formula-derivation?format=text
Install alias
/api/registry/install/wanshuiyin-formula-derivation
Recommend
/api/registry/recommend?task=Use%20formula-derivation%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
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
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d 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
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
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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: formula-derivation description: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. argument-hint: "[problem-goal-current-formulas-or-notes]" allowed-tools: Read, Write, Edit, Grep, Glob ---
# Formula Derivation: Research Theory Line Construction
Build an honest derivation package, not a fake polished theorem story.
## Constants
- DEFAULT_DERIVATION_DOC = `DERIVATION_PACKAGE.md` in project root - STATUS = `COHERENT AS STATED | COHERENT AFTER REFRAMING / EXTRA ASSUMPTION | NOT YET COHERENT`
## Context: $ARGUMENTS
## Goal
Produce exactly one of: 1. a coherent derivation package for the original target 2. a reframed derivation package with corrected object / assumptions / scope 3. a blocker report explaining why the current notes cannot yet support a coherent derivation
## Inputs
Extract and normalize: - the target phenomenon, formula, relation, or theory line - the intended role of the derivation: - exact identity / algebra - proposition / local theorem - approximation - mechanism interpretation - explicit assumptions - notation and definitions - any user-provided formula chain, sketch, messy notes, or current draft - nearby local theory files if the request points to them - desired output style if specified: - internal alignment note - paper-style theory draft - blocker report
If the target, object, notation, or assumptions are ambiguous, state the exact interpretation you are using before deriving anything.
## Workflow
### Step 1: Gather Derivation Context Determine the target derivation file with this priority: 1. a file path explicitly specified by the user 2. a derivation draft already referenced in local notes 3. `DERIVATION_PACKAGE.md` in project root as the default target
Read the relevant local context: - the chosen target derivation file, if it already exists - any local theory notes, formula drafts, appendix notes, or files explicitly mentioned by the user
Extract: - target formula / theory goal - current formula chain - assumptions - notation - known blockers - desired output mode
### Step 2: Freeze the Target State explicitly: - what is being explained, derived, or supported - whether the immediate goal is: - identity / algebra - proposition - approximation - interpretation - what the derivation is expected to output in the end
Do not start symbolic manipulation before this is fixed.
### Step 3: Choose the Invariant Object Identify the single quantity or conceptual object that should organize the derivation.
Typical possibilities include: - objective / utility / loss - total cost / energy / welfare - conserved quantity / state variable - expected metric / effective rate / effective cost
If the current notes start from a narrower quantity, decide explicitly whether it is: - the true top-level object - a proxy - a local slice - an approximation
Do not let a convenient proxy silently replace the actual conceptual object.
### Step 4: Normalize Assumptions and Notation Restate: - all assumptions - all symbols - regime boundaries or special cases - which quantities are fixed, adaptive, or state dependent
Identify: - hidden assumptions - undefined notation - scope ambiguities - whether the current formula chain already mixes exact steps with approximations
Preserve the user's original notation unless a cleanup is necessary for coherence. If you adopt a cleaner internal formulation, keep that as a derivation device rather than silently replacing the user's target.
### Step 5: Classify the Derivation Steps For every nontrivial step, determine whether it is: - **identity**: exact algebraic reformulation - **proposition**: a claim requiring conditions - **approximation**: model simplification or surrogate - **interpretation**: prose-level meaning of a formula
Never merge these categories without signaling the transition. If one part is only interpretive, do not present it as if it were mathematically proved.
### Step 6: Build a Derivation Map Choose a derivation strategy, for example: - definition -> substitution -> simplification - primitive law -> intermediate variable -> target expression - global quantity -> perturbation -> decomposition - exact model -> approximation -> interpretable closed form - general dynamic object -> simplified slice -> local theorem -> return to general case
Then write a derivation map: - target formula or theory line - required intermediate identities or lemmas - which assumptions each nontrivial step uses - where approximations enter - where special-case and general-case regimes diverge or collapse
If the derivation needs a decomposition, derive it from the chosen global quantity. Do not make a split appear magically from one local variable itself.
### Step 7: Write the Derivation Document Write to the chosen target derivation file.
If the target derivation file already exists: - read it first - update the relevant section - do not blindly duplicate prior content
If the user does not specify a target, default to `DERIVATION_PACKAGE.md` in project root.
Do NOT write directly into paper sections or appendix `.tex` files unless the user explicitly asks for that target.
The derivation package must include: - target - status - invariant object - assumptions - notation - derivation strategy - derivation map - main derivation steps - remarks / interpretations - boundaries and non-claims
Writing rules: - do not hide gaps with words like "clearly", "obviously", or "similarly" - define every symbol before use - mark approximations explicitly - separate derivation body from remarks - if the true object is dynamic or state dependent but a simpler slice is analyzed, say so explicitly - if a formula line is only heuristic, label it honestly
### Step 8: Final Verification Before finishing the target derivation file, verify: - the target is explicit - the invariant object is stable across the derivation - every assumption used is stated - each formula step is correctly labeled as identity / proposition / approximation / interpretation - the derivation does not silently switch objects - special cases and general cases still belong to one theory line - boundaries and non-claims are stated
If the derivation still lacks a coherent object, stable assumptions, or an honest path from premises to result, downgrade the status and write a blocker report instead of forcing a clean story.
## Required File Structure
Write the target derivation file using this structure:
```md # Derivation Package
## Target [what is being derived or explained]
## Status COHERENT AS STATED / COHERENT AFTER REFRAMING / NOT YET COHERENT
## Invariant Object [top-level quantity organizing the derivation]
## Assumptions - ...
## Notation - ...
## Derivation Strategy [chosen route and why]
## Derivation Map 1. Target depends on ... 2. Intermediate step A uses ... 3. Approximation enters at ...
## Main Derivation Step 1. ... Step 2. ... ...
## Remarks and Interpretation - ...
## Boundaries and Non-Claims - ...
## Open Risks - ... ```
## Output Modes
### If the derivation is coherent as stated Write the full structure above with a clean derivation package.
### If the notes are close but not coherent yet Write: - the exact mismatch - the corrected invariant object, assumption, or scope - the reframed derivation package
### If the derivation cannot be made coherent honestly Write: - `Status: NOT YET COHERENT` - the exact blocker: - missing object - unstable assumptions - notation conflict - unsupported approximation - theorem-level claim without enough conditions - what extra assumption, reframe, or intermediate derivation would be needed
## Relationship to `proof-writer`
Use `formula-derivation` when the user says things like: - “我不知道怎么起这条推导主线” - “这个公式到底该从哪个量出发” - “帮我把理论搭顺” - “把说明文档变成可写进论文的公式文档” - “这几段公式之间逻辑不通”
Use `proof-writer` only after: - the exact claim is fixed - the assumptions are stable - the notation is settled - and the task is now to prove or refute that claim rigorously
## Chat Response
After writing the target derivation file, respond briefly with: - status - whether the target survived unchanged or had to be reframed - what file was updated
## Key Rules
- Never fabricate a coherent derivation if the object, assumptions, or scope do not support one. - Prefer reframing the derivation over overclaiming. - Separate assumptions, identities, propositions, approximations, and interpretations. - Keep one invariant object across special and general cases whenever possible. - Treat simplified constant-parameter cases as analysis slices, not as the conceptual main object. - If uncertainty remains, mark it explicitly in `Open Risks`; do not hide it in polished prose. - Coherence matters more than elegance.
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Scenario-led draft for formula-derivation, ready for a manual X post.
formula-derivation: Structures and derives research formulas when the user wants to 推导公式, build a theory line, or... 15.8K stars https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x
Listing + install path for formula-derivation: https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x Install: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-deri...
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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.
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Install targets
Codex install prompt
Install the "formula-derivation" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/formula-derivation. 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: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. 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":"wanshuiyin-formula-derivation","task":"Install formula-derivation","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.
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Research agents
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Claude Code + CLI + Codex
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The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure. · The documented excerpt does not show an explicit setup or limitations section, though the skill appears to need no setup.
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Task: Use formula-derivation in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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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: formula-derivation description: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. argument-hint: "[problem-goal-current-formulas-or-notes]" allowed-tools: Read, Write, Edit, Grep, Glob ---
# Formula Derivation: Research Theory Line Construction
Build an honest derivation package, not a fake polished theorem story.
## Constants
- DEFAULT_DERIVATION_DOC = `DERIVATION_PACKAGE.md` in project root - STATUS = `COHERENT AS STATED | COHERENT AFTER REFRAMING / EXTRA ASSUMPTION | NOT YET COHERENT`
## Context: $ARGUMENTS
## Goal
Produce exactly one of: 1. a coherent derivation package for the original target 2. a reframed derivation package with corrected object / assumptions / scope 3. a blocker report explaining why the current notes cannot yet support a coherent derivation
## Inputs
Extract and normalize: - the target phenomenon, formula, relation, or theory line - the intended role of the derivation: - exact identity / algebra - proposition / local theorem - approximation - mechanism interpretation - explicit assumptions - notation and definitions - any user-provided formula chain, sketch, messy notes, or current draft - nearby local theory files if the request points to them - desired output style if specified: - internal alignment note - paper-style theory draft - blocker report
If the target, object, notation, or assumptions are ambiguous, state the exact interpretation you are using before deriving anything.
## Workflow
### Step 1: Gather Derivation Context Determine the target derivation file with this priority: 1. a file path explicitly specified by the user 2. a derivation draft already referenced in local notes 3. `DERIVATION_PACKAGE.md` in project root as the default target
Read the relevant local context: - the chosen target derivation file, if it already exists - any local theory notes, formula drafts, appendix notes, or files explicitly mentioned by the user
Extract: - target formula / theory goal - current formula chain - assumptions - notation - known blockers - desired output mode
### Step 2: Freeze the Target State explicitly: - what is being explained, derived, or supported - whether the immediate goal is: - identity / algebra - proposition - approximation - interpretation - what the derivation is expected to output in the end
Do not start symbolic manipulation before this is fixed.
### Step 3: Choose the Invariant Object Identify the single quantity or conceptual object that should organize the derivation.
Typical possibilities include: - objective / utility / loss - total cost / energy / welfare - conserved quantity / state variable - expected metric / effective rate / effective cost
If the current notes start from a narrower quantity, decide explicitly whether it is: - the true top-level object - a proxy - a local slice - an approximation
Do not let a convenient proxy silently replace the actual conceptual object.
### Step 4: Normalize Assumptions and Notation Restate: - all assumptions - all symbols - regime boundaries or special cases - which quantities are fixed, adaptive, or state dependent
Identify: - hidden assumptions - undefined notation - scope ambiguities - whether the current formula chain already mixes exact steps with approximations
Preserve the user's original notation unless a cleanup is necessary for coherence. If you adopt a cleaner internal formulation, keep that as a derivation device rather than silently replacing the user's target.
### Step 5: Classify the Derivation Steps For every nontrivial step, determine whether it is: - **identity**: exact algebraic reformulation - **proposition**: a claim requiring conditions - **approximation**: model simplification or surrogate - **interpretation**: prose-level meaning of a formula
Never merge these categories without signaling the transition. If one part is only interpretive, do not present it as if it were mathematically proved.
### Step 6: Build a Derivation Map Choose a derivation strategy, for example: - definition -> substitution -> simplification - primitive law -> intermediate variable -> target expression - global quantity -> perturbation -> decomposition - exact model -> approximation -> interpretable closed form - general dynamic object -> simplified slice -> local theorem -> return to general case
Then write a derivation map: - target formula or theory line - required intermediate identities or lemmas - which assumptions each nontrivial step uses - where approximations enter - where special-case and general-case regimes diverge or collapse
If the derivation needs a decomposition, derive it from the chosen global quantity. Do not make a split appear magically from one local variable itself.
### Step 7: Write the Derivation Document Write to the chosen target derivation file.
If the target derivation file already exists: - read it first - update the relevant section - do not blindly duplicate prior content
If the user does not specify a target, default to `DERIVATION_PACKAGE.md` in project root.
Do NOT write directly into paper sections or appendix `.tex` files unless the user explicitly asks for that target.
The derivation package must include: - target - status - invariant object - assumptions - notation - derivation strategy - derivation map - main derivation steps - remarks / interpretations - boundaries and non-claims
Writing rules: - do not hide gaps with words like "clearly", "obviously", or "similarly" - define every symbol before use - mark approximations explicitly - separate derivation body from remarks - if the true object is dynamic or state dependent but a simpler slice is analyzed, say so explicitly - if a formula line is only heuristic, label it honestly
### Step 8: Final Verification Before finishing the target derivation file, verify: - the target is explicit - the invariant object is stable across the derivation - every assumption used is stated - each formula step is correctly labeled as identity / proposition / approximation / interpretation - the derivation does not silently switch objects - special cases and general cases still belong to one theory line - boundaries and non-claims are stated
If the derivation still lacks a coherent object, stable assumptions, or an honest path from premises to result, downgrade the status and write a blocker report instead of forcing a clean story.
## Required File Structure
Write the target derivation file using this structure:
```md # Derivation Package
## Target [what is being derived or explained]
## Status COHERENT AS STATED / COHERENT AFTER REFRAMING / NOT YET COHERENT
## Invariant Object [top-level quantity organizing the derivation]
## Assumptions - ...
## Notation - ...
## Derivation Strategy [chosen route and why]
## Derivation Map 1. Target depends on ... 2. Intermediate step A uses ... 3. Approximation enters at ...
## Main Derivation Step 1. ... Step 2. ... ...
## Remarks and Interpretation - ...
## Boundaries and Non-Claims - ...
## Open Risks - ... ```
## Output Modes
### If the derivation is coherent as stated Write the full structure above with a clean derivation package.
### If the notes are close but not coherent yet Write: - the exact mismatch - the corrected invariant object, assumption, or scope - the reframed derivation package
### If the derivation cannot be made coherent honestly Write: - `Status: NOT YET COHERENT` - the exact blocker: - missing object - unstable assumptions - notation conflict - unsupported approximation - theorem-level claim without enough conditions - what extra assumption, reframe, or intermediate derivation would be needed
## Relationship to `proof-writer`
Use `formula-derivation` when the user says things like: - “我不知道怎么起这条推导主线” - “这个公式到底该从哪个量出发” - “帮我把理论搭顺” - “把说明文档变成可写进论文的公式文档” - “这几段公式之间逻辑不通”
Use `proof-writer` only after: - the exact claim is fixed - the assumptions are stable - the notation is settled - and the task is now to prove or refute that claim rigorously
## Chat Response
After writing the target derivation file, respond briefly with: - status - whether the target survived unchanged or had to be reframed - what file was updated
## Key Rules
- Never fabricate a coherent derivation if the object, assumptions, or scope do not support one. - Prefer reframing the derivation over overclaiming. - Separate assumptions, identities, propositions, approximations, and interpretations. - Keep one invariant object across special and general cases whenever possible. - Treat simplified constant-parameter cases as analysis slices, not as the conceptual main object. - If uncertainty remains, mark it explicitly in `Open Risks`; do not hide it in polished prose. - Coherence matters more than elegance.
Source provenance
Decision snapshot
15,840 GitHub stars
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 formula-derivation, ready for a manual X post.
formula-derivation: Structures and derives research formulas when the user wants to 推导公式, build a theory line, or... 15.8K stars https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x
Listing + install path for formula-derivation: https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x Install: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-deri...
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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.
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Academic Research Skills for Claude Code: research → write → review → revise → finalize
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256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "formula-derivation" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/formula-derivation. 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: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. 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":"wanshuiyin-formula-derivation","task":"Install formula-derivation","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
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
Maintenance
fresh
1d since push
Risk
Safe to try
The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure.
GitHub quality
16K
89/100 Quality · 81/100 Trust
Coverage tags
Review notes
The provided SKILL.md excerpt appears to be cut off near the end of Step 6; if the actual file is incomplete, the workflow would lack a final output template and closure. · The documented excerpt does not show an explicit setup or limitations section, though the skill appears to need no setup.
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
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA 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
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
1d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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 wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivationDo 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%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wanshuiyin-formula-derivation/install
Agent should check
Copy prompt
Task: Use formula-derivation in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20formula-derivation%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivation
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/wanshuiyin-formula-derivation/install
LLM text format
/api/skills/wanshuiyin-formula-derivation/install?format=text
Find alternatives
/api/skills/search?q=formula-derivation&limit=3
Agent prompt
Use formula-derivation for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-formula-derivation/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-derivationRegistry 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/wanshuiyin-formula-derivation
LLM text
/api/registry/manifest/wanshuiyin-formula-derivation?format=text
Install alias
/api/registry/install/wanshuiyin-formula-derivation
Recommend
/api/registry/recommend?task=Use%20formula-derivation%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
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
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d 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
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
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: formula-derivation description: Structures and derives research formulas when the user wants to 推导公式, build a theory line, organize assumptions, turn scattered equations into a coherent derivation, or rewrite theory notes into a paper-ready formula document. Use when the derivation target is not yet fully fixed, the main object still needs to be chosen, or the user needs a coherent derivation package rather than a finished theorem proof. argument-hint: "[problem-goal-current-formulas-or-notes]" allowed-tools: Read, Write, Edit, Grep, Glob ---
# Formula Derivation: Research Theory Line Construction
Build an honest derivation package, not a fake polished theorem story.
## Constants
- DEFAULT_DERIVATION_DOC = `DERIVATION_PACKAGE.md` in project root - STATUS = `COHERENT AS STATED | COHERENT AFTER REFRAMING / EXTRA ASSUMPTION | NOT YET COHERENT`
## Context: $ARGUMENTS
## Goal
Produce exactly one of: 1. a coherent derivation package for the original target 2. a reframed derivation package with corrected object / assumptions / scope 3. a blocker report explaining why the current notes cannot yet support a coherent derivation
## Inputs
Extract and normalize: - the target phenomenon, formula, relation, or theory line - the intended role of the derivation: - exact identity / algebra - proposition / local theorem - approximation - mechanism interpretation - explicit assumptions - notation and definitions - any user-provided formula chain, sketch, messy notes, or current draft - nearby local theory files if the request points to them - desired output style if specified: - internal alignment note - paper-style theory draft - blocker report
If the target, object, notation, or assumptions are ambiguous, state the exact interpretation you are using before deriving anything.
## Workflow
### Step 1: Gather Derivation Context Determine the target derivation file with this priority: 1. a file path explicitly specified by the user 2. a derivation draft already referenced in local notes 3. `DERIVATION_PACKAGE.md` in project root as the default target
Read the relevant local context: - the chosen target derivation file, if it already exists - any local theory notes, formula drafts, appendix notes, or files explicitly mentioned by the user
Extract: - target formula / theory goal - current formula chain - assumptions - notation - known blockers - desired output mode
### Step 2: Freeze the Target State explicitly: - what is being explained, derived, or supported - whether the immediate goal is: - identity / algebra - proposition - approximation - interpretation - what the derivation is expected to output in the end
Do not start symbolic manipulation before this is fixed.
### Step 3: Choose the Invariant Object Identify the single quantity or conceptual object that should organize the derivation.
Typical possibilities include: - objective / utility / loss - total cost / energy / welfare - conserved quantity / state variable - expected metric / effective rate / effective cost
If the current notes start from a narrower quantity, decide explicitly whether it is: - the true top-level object - a proxy - a local slice - an approximation
Do not let a convenient proxy silently replace the actual conceptual object.
### Step 4: Normalize Assumptions and Notation Restate: - all assumptions - all symbols - regime boundaries or special cases - which quantities are fixed, adaptive, or state dependent
Identify: - hidden assumptions - undefined notation - scope ambiguities - whether the current formula chain already mixes exact steps with approximations
Preserve the user's original notation unless a cleanup is necessary for coherence. If you adopt a cleaner internal formulation, keep that as a derivation device rather than silently replacing the user's target.
### Step 5: Classify the Derivation Steps For every nontrivial step, determine whether it is: - **identity**: exact algebraic reformulation - **proposition**: a claim requiring conditions - **approximation**: model simplification or surrogate - **interpretation**: prose-level meaning of a formula
Never merge these categories without signaling the transition. If one part is only interpretive, do not present it as if it were mathematically proved.
### Step 6: Build a Derivation Map Choose a derivation strategy, for example: - definition -> substitution -> simplification - primitive law -> intermediate variable -> target expression - global quantity -> perturbation -> decomposition - exact model -> approximation -> interpretable closed form - general dynamic object -> simplified slice -> local theorem -> return to general case
Then write a derivation map: - target formula or theory line - required intermediate identities or lemmas - which assumptions each nontrivial step uses - where approximations enter - where special-case and general-case regimes diverge or collapse
If the derivation needs a decomposition, derive it from the chosen global quantity. Do not make a split appear magically from one local variable itself.
### Step 7: Write the Derivation Document Write to the chosen target derivation file.
If the target derivation file already exists: - read it first - update the relevant section - do not blindly duplicate prior content
If the user does not specify a target, default to `DERIVATION_PACKAGE.md` in project root.
Do NOT write directly into paper sections or appendix `.tex` files unless the user explicitly asks for that target.
The derivation package must include: - target - status - invariant object - assumptions - notation - derivation strategy - derivation map - main derivation steps - remarks / interpretations - boundaries and non-claims
Writing rules: - do not hide gaps with words like "clearly", "obviously", or "similarly" - define every symbol before use - mark approximations explicitly - separate derivation body from remarks - if the true object is dynamic or state dependent but a simpler slice is analyzed, say so explicitly - if a formula line is only heuristic, label it honestly
### Step 8: Final Verification Before finishing the target derivation file, verify: - the target is explicit - the invariant object is stable across the derivation - every assumption used is stated - each formula step is correctly labeled as identity / proposition / approximation / interpretation - the derivation does not silently switch objects - special cases and general cases still belong to one theory line - boundaries and non-claims are stated
If the derivation still lacks a coherent object, stable assumptions, or an honest path from premises to result, downgrade the status and write a blocker report instead of forcing a clean story.
## Required File Structure
Write the target derivation file using this structure:
```md # Derivation Package
## Target [what is being derived or explained]
## Status COHERENT AS STATED / COHERENT AFTER REFRAMING / NOT YET COHERENT
## Invariant Object [top-level quantity organizing the derivation]
## Assumptions - ...
## Notation - ...
## Derivation Strategy [chosen route and why]
## Derivation Map 1. Target depends on ... 2. Intermediate step A uses ... 3. Approximation enters at ...
## Main Derivation Step 1. ... Step 2. ... ...
## Remarks and Interpretation - ...
## Boundaries and Non-Claims - ...
## Open Risks - ... ```
## Output Modes
### If the derivation is coherent as stated Write the full structure above with a clean derivation package.
### If the notes are close but not coherent yet Write: - the exact mismatch - the corrected invariant object, assumption, or scope - the reframed derivation package
### If the derivation cannot be made coherent honestly Write: - `Status: NOT YET COHERENT` - the exact blocker: - missing object - unstable assumptions - notation conflict - unsupported approximation - theorem-level claim without enough conditions - what extra assumption, reframe, or intermediate derivation would be needed
## Relationship to `proof-writer`
Use `formula-derivation` when the user says things like: - “我不知道怎么起这条推导主线” - “这个公式到底该从哪个量出发” - “帮我把理论搭顺” - “把说明文档变成可写进论文的公式文档” - “这几段公式之间逻辑不通”
Use `proof-writer` only after: - the exact claim is fixed - the assumptions are stable - the notation is settled - and the task is now to prove or refute that claim rigorously
## Chat Response
After writing the target derivation file, respond briefly with: - status - whether the target survived unchanged or had to be reframed - what file was updated
## Key Rules
- Never fabricate a coherent derivation if the object, assumptions, or scope do not support one. - Prefer reframing the derivation over overclaiming. - Separate assumptions, identities, propositions, approximations, and interpretations. - Keep one invariant object across special and general cases whenever possible. - Treat simplified constant-parameter cases as analysis slices, not as the conceptual main object. - If uncertainty remains, mark it explicitly in `Open Risks`; do not hide it in polished prose. - Coherence matters more than elegance.
Source provenance
Decision snapshot
15,840 GitHub stars
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 formula-derivation, ready for a manual X post.
formula-derivation: Structures and derives research formulas when the user wants to 推导公式, build a theory line, or... 15.8K stars https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x
Listing + install path for formula-derivation: https://www.openagentskill.com/skills/wanshuiyin-formula-derivation?ref=x Install: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill formula-deri...
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@wanshuiyin
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
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 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