Creator ยท zhnnky329
Last updated ยท Sep 2, 2026
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
Creator ยท zhnnky329
Last updated ยท Sep 2, 2026
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
Creator ยท zhnnky329
Last updated ยท Sep 2, 2026
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
Creator ยท zhnnky329
Last updated ยท Sep 2, 2026
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
Review then install
Install targets
Codex install prompt
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/paper-polisher. 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: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. 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":"zhnnky329-paper-polisher","task":"Install paper-polisher","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 zhnnky329/MathModeling-skills --skill paper-polisher
Maintenance
fresh
12d since push
Risk
Safe to try
Quality score needs review
GitHub quality
695
75/100 Quality ยท 83/100 Trust
Coverage tags
Review notes
Quality score needs review
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
695 GitHub stars
Repo activity
695 stars, 31 forks
Maintenance
12d since push
License
MIT
Install
npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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 zhnnky329/MathModeling-skills --skill paper-polisherDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/zhnnky329-paper-polisher/install
Agent should check
Copy prompt
Task: Use paper-polisher in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install
Install command: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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/zhnnky329-paper-polisher/install
LLM text format
/api/skills/zhnnky329-paper-polisher/install?format=text
Find alternatives
/api/skills/search?q=paper-polisher&limit=3
Agent prompt
Use paper-polisher for this task. Review https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install, then install with: npx skills add zhnnky329/MathModeling-skills --skill paper-polisherRegistry 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/zhnnky329-paper-polisher
LLM text
/api/registry/manifest/zhnnky329-paper-polisher?format=text
Install alias
/api/registry/install/zhnnky329-paper-polisher
Recommend
/api/registry/recommend?task=Use%20paper-polisher%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
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO695 GitHub stars
Stars/forks activity
INFO695 stars, 31 forks; issue activity unavailable in current metadata
Recent maintenance
PASS12d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
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Infisical is the open-source platform for secrets, certificates, and privileged access management.
--- name: paper-polisher description: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. license: MIT ---
# Purpose
Polish mathematical modeling contest paper sections for language quality, logical clarity, formula consistency, and claim calibration.
This skill operates on already-drafted paper sections. It improves wording, fixes grammar, checks formulas, calibrates hedging to match evidence strength, detects overclaims, and ensures formatting compliance. It does not invent new content, add unsupported claims, or rewrite the paper's scientific argument.
Adapted from [nature-polishing](https://github.com/Yuan1z0825/nature-skills) design principles: language serves the argument, polish should not hide weak reasoning, and claims must be proportional to evidence.
This skill does not write new paper sections, run experiments, generate figures, or perform final QA.
# When to use
Use this skill:
- After `paper-section-writer` has drafted one or more paper sections. - Before `quality-assurance-auditor`. - When the user says: "polish the paper", "check the English", "fix the grammar", "improve the writing", "calibrate the claims", "check for overclaims", "proofread Q1 section". - When Chinese-to-English translation has produced rough drafts that need smoothing. - When formulas, notation, or terminology are inconsistent across sections.
# Preconditions
The following should already exist or be provided:
- Paper section drafts under `paper/sections/`. - Final method explanations (for formula and notation verification). - Final result analyses (for claim verification). - The global symbol table at `planning/symbol_table.md` (if available). - Contest formatting requirements (if available).
If paper sections do not exist, hand back to `paper-section-writer`.
# Inputs
Use or request:
- `paper/sections/*.md` or `paper/sections/*.tex` โ the drafted sections. - `methods/Qx/qx_final_method_explanation.md` โ for formula and notation verification. - `results/Qx/reports/qx_final_result_analysis.md` โ for claim verification. - `planning/symbol_table.md` โ for notation consistency. - Contest formatting requirements.
# Workflow
1. Identify the paper type and section. - Mathematical modeling contest papers follow a standard structure: Abstract โ Problem Restatement โ Problem Analysis โ Assumptions โ Symbols โ Model Construction (per Q) โ Model Solution โ Results Analysis โ Robustness โ Strengths & Limitations โ Conclusion. - Each section has different polishing priorities (see section-specific rules below).
2. Run the 12-point polish checklist (see below).
3. Calibrate claims against evidence. - For each numerical or comparative claim, verify it is supported by the final result analysis or robustness report. - If a claim overstates the evidence, downgrade the language. - If a claim is unsupported, flag it as a blocker (do not silently remove โ the writer needs to decide).
4. Check formula and notation consistency. - Every symbol must appear in the global symbol table or be defined locally. - Same concept must use the same symbol across all sections. - Subscripts, superscripts, and indices must be consistent. - Formula numbering must be sequential and match references in text.
5. Check terminology consistency. - Same concept must use the same term throughout. - Method names must match the final method explanation. - "Baseline", "main model", "improved model" must be used consistently.
6. Produce polished sections. - Show a diff or change summary. - Mark any claims that were downgraded and why. - Flag any remaining issues that need author attention.
# 12-Point Polish Checklist
## 1. Sentence Length - Split sentences longer than 30 words. - Vary sentence length: mix short (8-15 words) and medium (15-25 words). - The first and last sentences of each paragraph should be the clearest.
## 2. Paragraph Structure - Each paragraph should have one main point. - Topic sentence first, support following, transition at end (or beginning of next). - Paragraphs longer than 5-6 sentences should be split or tightened.
## 3. Tense Consistency - **Problem restatement / Assumptions / Symbols**: Present tense. - **Model construction**: Present tense for model description. - **Model solution / Results analysis**: Past tense for what was done and found. - **Conclusion**: Present tense for final findings, past tense for what was done. - Do not mix tenses within a single paragraph without reason.
## 4. Hedging Calibration
Match claim strength to evidence:
| Evidence Level | Appropriate Hedging | Example | |---------------|-------------------|---------| | Robust, multiple checks | Strong claim, no hedge | "The entropy-TOPSIS method produces stable rankings." | | Single check, moderate perturbation | Moderate hedge | "The rankings appear stable under moderate weight changes." | | Limited check, narrow range | Weak hedge | "The results suggest that rankings may be stable within the tested range." | | No check, extrapolation | No claim allowed | Flag as unsupported. Do not write. |
Hedging phrases (strongest to weakest): - `demonstrates` / `shows` / `establishes` โ strongest - `indicates` / `suggests` / `supports` โ moderate - `may indicate` / `appears to` / `is consistent with` โ weak - `could potentially` / `might possibly` โ weakest (use sparingly)
## 5. Overclaim Detection
Flag and downgrade or remove: - Absolute claims: "always", "never", "proves", "guarantees", "optimal" (unless proven). - Unwarranted causation: "A causes B" when only correlation is shown. - Scope expansion: "All models benefit from..." when only one model was tested. - Unverified "first" or "novel" claims. - "Significantly" without statistical test or defined threshold. - "Our model outperforms all existing methods" when only 1-2 baselines were compared. - Numerical precision beyond data support: "The score is 0.883214" โ "The score is approximately 0.88".
## 6. Formula Formatting - Formulas in display math mode (`$$...$$` or `\begin{equation}...\end{equation}`) for important equations. - Inline math (`$...$`) for variable references and short expressions. - Consistent subscript/superscript style. - Units after numerical values. - Variable definitions immediately after first use in a formula.
## 7. Notation Consistency - Cross-check every symbol against `planning/symbol_table.md`. - Decision variables vs state variables vs parameters must be distinguished. - Vector/matrix notation must be consistent (bold, arrow, or neither โ pick one).
## 8. Figure and Table References - Every `\ref{fig:...}` or "Figure X" must correspond to an actual figure file. - Figure references must be in order (Fig.1 before Fig.2 in text). - Every table reference must correspond to an actual table. - Captions must include the main takeaway, not just a description.
## 9. Transition and Flow - Between sections: one bridging sentence connecting to the next section. - Between paragraphs: logical flow (therefore, however, in contrast, furthermore, specifically). - Avoid "As mentioned above" / "As discussed previously" โ restate briefly instead. - Avoid "It is worth noting that..." / "It should be mentioned that..." โ just state it.
## 10. Word Choice - Prefer specific over vague: "RMSE improved by 35%" not "the error got better". - Prefer simple over ornate: "use" not "utilize", "show" not "elucidate", "about" not "approximately" (unless precision matters). - Remove filler: "It is important to note that", "Interestingly", "Remarkably". - Remove redundant pairs: "various different", "basic fundamentals", "advance planning".
## 11. Voice - Prefer active voice for clarity: "We applied TOPSIS to the indicator matrix" not "TOPSIS was applied to the indicator matrix". - Use passive voice sparingly, mainly in Methods/Model Solution: "The weights were computed using the entropy method". - Use "we" consistently (not "the authors", "this paper", "the research team"). - In ChineseโEnglish translation: avoid literal translation of Chinese academic conventions.
## 12. Formatting Compliance - Check contest-specific formatting: word count, page limit, font size, margin requirements. - Section numbering is consistent. - Reference format is consistent. - Appendix materials are properly labeled.
# Section-Specific Polish Priorities
| Section | Top Priority | |---------|-------------| | Abstract | Claim calibration, numerical precision, word count | | Problem Restatement | Clarity, no added requirements | | Assumptions | Necessity check, impact statements | | Symbols | Completeness, consistency, distinction of variable types | | Model Construction | Formula correctness, notation consistency, assumption traceability | | Model Solution | Procedural clarity, reproducibility | | Results Analysis | Claim-evidence alignment, figure/table references | | Robustness | Stable vs fragile separation, boundary conditions | | Strengths & Limitations | Specificity, honesty | | Conclusion | Subquestion coverage, claim calibration |
# Chinese-to-English Translation Notes
When the source text is in Chinese and needs translation to English: - Do not translate literally. Translate the MEANING. - Chinese academic writing often uses more hedging; keep only what the evidence supports. - Chinese sentences tend to be longer; split into shorter English sentences. - "ๆฌๆ" โ "This paper" or "We" depending on context. - "ๆพ็ถ" / "ๆพ่ๆ่ง" โ avoid "obviously" unless truly obvious; use "clearly" only with strong justification. - "ไธๅฎ็" โ drop or replace with specific quantifier. - "่พๅฅฝ็ๆๆ" โ must be quantified: "improved RMSE by X%" not "good results".
# Rules
- Polish language and structure; do not invent new content. - Downgrade overclaims; do not upgrade weak claims to sound stronger. - Flag unsupported claims as issues; do not silently remove or modify them. - Do not change formulas without checking against the final method explanation. - Do not add new references, experiments, figures, or numerical values. - Do not remove limitations or uncertainty statements. - Keep changes traceable โ show what was changed and why. - If the underlying argument is broken, flag it rather than polishing over it.
# Verification
Before handing off, verify:
- Every modified sentence is grammatically correct. - Every formula cross-checked against the final method explanation. - Every claim calibrated to match available evidence. - Overclaims are flagged or downgraded. - Notation is consistent across all sections. - Figure/table references are in order and correspond to existing files. - Contest formatting requirements are met. - A change summary is produced.
# Failure modes
Stop and report a blocker if:
- A claim in the paper has no supporting evidence at all (not just weak evidence โ NO evidence). - A formula in the paper contradicts the final method explanation. - A referenced figure or table does not exist. - A numerical value in the paper cannot be found in any result file. - The paper claims a result for a subquestion that has no final result analysis.
# Stop conditions
This skill must stop instead of guessing when:
- Fixing language would require changing the scientific meaning. - The evidence for a claim is entirely absent. - Multiple contradictory claims exist in the same section. - A referenced artifact cannot be found. - Continuing would hide a fundamental logical flaw under polished prose.
When stopping, output: - the blocker - the affected sentence or paragraph - the missing or contradictory evidence - recommended action
# Handoff
After polishing: โ `quality-assurance-auditor`
With: - polished section paths - change summary (what was modified and why) - flagged overclaims (downgraded or awaiting author decision) - remaining issues needing author attention
# Examples
## Example 1:
Source provenance
Decision snapshot
695 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 paper-polisher, ready for a manual X post.
paper-polisher: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging... 695 stars https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x
Listing + install path for paper-polisher: https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x Install: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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 zhnnky329 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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@zhnnky329
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Review then install
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Install targets
Codex install prompt
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/paper-polisher. 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: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. 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":"zhnnky329-paper-polisher","task":"Install paper-polisher","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 zhnnky329/MathModeling-skills --skill paper-polisher
Maintenance
fresh
12d since push
Risk
Safe to try
Quality score needs review
GitHub quality
695
75/100 Quality ยท 83/100 Trust
Coverage tags
Review notes
Quality score needs review
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
695 GitHub stars
Repo activity
695 stars, 31 forks
Maintenance
12d since push
License
MIT
Install
npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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 zhnnky329/MathModeling-skills --skill paper-polisherDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/zhnnky329-paper-polisher/install
Agent should check
Copy prompt
Task: Use paper-polisher in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install
Install command: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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/zhnnky329-paper-polisher/install
LLM text format
/api/skills/zhnnky329-paper-polisher/install?format=text
Find alternatives
/api/skills/search?q=paper-polisher&limit=3
Agent prompt
Use paper-polisher for this task. Review https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install, then install with: npx skills add zhnnky329/MathModeling-skills --skill paper-polisherRegistry 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/zhnnky329-paper-polisher
LLM text
/api/registry/manifest/zhnnky329-paper-polisher?format=text
Install alias
/api/registry/install/zhnnky329-paper-polisher
Recommend
/api/registry/recommend?task=Use%20paper-polisher%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
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO695 GitHub stars
Stars/forks activity
INFO695 stars, 31 forks; issue activity unavailable in current metadata
Recent maintenance
PASS12d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
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Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
Infisical is the open-source platform for secrets, certificates, and privileged access management.
--- name: paper-polisher description: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. license: MIT ---
# Purpose
Polish mathematical modeling contest paper sections for language quality, logical clarity, formula consistency, and claim calibration.
This skill operates on already-drafted paper sections. It improves wording, fixes grammar, checks formulas, calibrates hedging to match evidence strength, detects overclaims, and ensures formatting compliance. It does not invent new content, add unsupported claims, or rewrite the paper's scientific argument.
Adapted from [nature-polishing](https://github.com/Yuan1z0825/nature-skills) design principles: language serves the argument, polish should not hide weak reasoning, and claims must be proportional to evidence.
This skill does not write new paper sections, run experiments, generate figures, or perform final QA.
# When to use
Use this skill:
- After `paper-section-writer` has drafted one or more paper sections. - Before `quality-assurance-auditor`. - When the user says: "polish the paper", "check the English", "fix the grammar", "improve the writing", "calibrate the claims", "check for overclaims", "proofread Q1 section". - When Chinese-to-English translation has produced rough drafts that need smoothing. - When formulas, notation, or terminology are inconsistent across sections.
# Preconditions
The following should already exist or be provided:
- Paper section drafts under `paper/sections/`. - Final method explanations (for formula and notation verification). - Final result analyses (for claim verification). - The global symbol table at `planning/symbol_table.md` (if available). - Contest formatting requirements (if available).
If paper sections do not exist, hand back to `paper-section-writer`.
# Inputs
Use or request:
- `paper/sections/*.md` or `paper/sections/*.tex` โ the drafted sections. - `methods/Qx/qx_final_method_explanation.md` โ for formula and notation verification. - `results/Qx/reports/qx_final_result_analysis.md` โ for claim verification. - `planning/symbol_table.md` โ for notation consistency. - Contest formatting requirements.
# Workflow
1. Identify the paper type and section. - Mathematical modeling contest papers follow a standard structure: Abstract โ Problem Restatement โ Problem Analysis โ Assumptions โ Symbols โ Model Construction (per Q) โ Model Solution โ Results Analysis โ Robustness โ Strengths & Limitations โ Conclusion. - Each section has different polishing priorities (see section-specific rules below).
2. Run the 12-point polish checklist (see below).
3. Calibrate claims against evidence. - For each numerical or comparative claim, verify it is supported by the final result analysis or robustness report. - If a claim overstates the evidence, downgrade the language. - If a claim is unsupported, flag it as a blocker (do not silently remove โ the writer needs to decide).
4. Check formula and notation consistency. - Every symbol must appear in the global symbol table or be defined locally. - Same concept must use the same symbol across all sections. - Subscripts, superscripts, and indices must be consistent. - Formula numbering must be sequential and match references in text.
5. Check terminology consistency. - Same concept must use the same term throughout. - Method names must match the final method explanation. - "Baseline", "main model", "improved model" must be used consistently.
6. Produce polished sections. - Show a diff or change summary. - Mark any claims that were downgraded and why. - Flag any remaining issues that need author attention.
# 12-Point Polish Checklist
## 1. Sentence Length - Split sentences longer than 30 words. - Vary sentence length: mix short (8-15 words) and medium (15-25 words). - The first and last sentences of each paragraph should be the clearest.
## 2. Paragraph Structure - Each paragraph should have one main point. - Topic sentence first, support following, transition at end (or beginning of next). - Paragraphs longer than 5-6 sentences should be split or tightened.
## 3. Tense Consistency - **Problem restatement / Assumptions / Symbols**: Present tense. - **Model construction**: Present tense for model description. - **Model solution / Results analysis**: Past tense for what was done and found. - **Conclusion**: Present tense for final findings, past tense for what was done. - Do not mix tenses within a single paragraph without reason.
## 4. Hedging Calibration
Match claim strength to evidence:
| Evidence Level | Appropriate Hedging | Example | |---------------|-------------------|---------| | Robust, multiple checks | Strong claim, no hedge | "The entropy-TOPSIS method produces stable rankings." | | Single check, moderate perturbation | Moderate hedge | "The rankings appear stable under moderate weight changes." | | Limited check, narrow range | Weak hedge | "The results suggest that rankings may be stable within the tested range." | | No check, extrapolation | No claim allowed | Flag as unsupported. Do not write. |
Hedging phrases (strongest to weakest): - `demonstrates` / `shows` / `establishes` โ strongest - `indicates` / `suggests` / `supports` โ moderate - `may indicate` / `appears to` / `is consistent with` โ weak - `could potentially` / `might possibly` โ weakest (use sparingly)
## 5. Overclaim Detection
Flag and downgrade or remove: - Absolute claims: "always", "never", "proves", "guarantees", "optimal" (unless proven). - Unwarranted causation: "A causes B" when only correlation is shown. - Scope expansion: "All models benefit from..." when only one model was tested. - Unverified "first" or "novel" claims. - "Significantly" without statistical test or defined threshold. - "Our model outperforms all existing methods" when only 1-2 baselines were compared. - Numerical precision beyond data support: "The score is 0.883214" โ "The score is approximately 0.88".
## 6. Formula Formatting - Formulas in display math mode (`$$...$$` or `\begin{equation}...\end{equation}`) for important equations. - Inline math (`$...$`) for variable references and short expressions. - Consistent subscript/superscript style. - Units after numerical values. - Variable definitions immediately after first use in a formula.
## 7. Notation Consistency - Cross-check every symbol against `planning/symbol_table.md`. - Decision variables vs state variables vs parameters must be distinguished. - Vector/matrix notation must be consistent (bold, arrow, or neither โ pick one).
## 8. Figure and Table References - Every `\ref{fig:...}` or "Figure X" must correspond to an actual figure file. - Figure references must be in order (Fig.1 before Fig.2 in text). - Every table reference must correspond to an actual table. - Captions must include the main takeaway, not just a description.
## 9. Transition and Flow - Between sections: one bridging sentence connecting to the next section. - Between paragraphs: logical flow (therefore, however, in contrast, furthermore, specifically). - Avoid "As mentioned above" / "As discussed previously" โ restate briefly instead. - Avoid "It is worth noting that..." / "It should be mentioned that..." โ just state it.
## 10. Word Choice - Prefer specific over vague: "RMSE improved by 35%" not "the error got better". - Prefer simple over ornate: "use" not "utilize", "show" not "elucidate", "about" not "approximately" (unless precision matters). - Remove filler: "It is important to note that", "Interestingly", "Remarkably". - Remove redundant pairs: "various different", "basic fundamentals", "advance planning".
## 11. Voice - Prefer active voice for clarity: "We applied TOPSIS to the indicator matrix" not "TOPSIS was applied to the indicator matrix". - Use passive voice sparingly, mainly in Methods/Model Solution: "The weights were computed using the entropy method". - Use "we" consistently (not "the authors", "this paper", "the research team"). - In ChineseโEnglish translation: avoid literal translation of Chinese academic conventions.
## 12. Formatting Compliance - Check contest-specific formatting: word count, page limit, font size, margin requirements. - Section numbering is consistent. - Reference format is consistent. - Appendix materials are properly labeled.
# Section-Specific Polish Priorities
| Section | Top Priority | |---------|-------------| | Abstract | Claim calibration, numerical precision, word count | | Problem Restatement | Clarity, no added requirements | | Assumptions | Necessity check, impact statements | | Symbols | Completeness, consistency, distinction of variable types | | Model Construction | Formula correctness, notation consistency, assumption traceability | | Model Solution | Procedural clarity, reproducibility | | Results Analysis | Claim-evidence alignment, figure/table references | | Robustness | Stable vs fragile separation, boundary conditions | | Strengths & Limitations | Specificity, honesty | | Conclusion | Subquestion coverage, claim calibration |
# Chinese-to-English Translation Notes
When the source text is in Chinese and needs translation to English: - Do not translate literally. Translate the MEANING. - Chinese academic writing often uses more hedging; keep only what the evidence supports. - Chinese sentences tend to be longer; split into shorter English sentences. - "ๆฌๆ" โ "This paper" or "We" depending on context. - "ๆพ็ถ" / "ๆพ่ๆ่ง" โ avoid "obviously" unless truly obvious; use "clearly" only with strong justification. - "ไธๅฎ็" โ drop or replace with specific quantifier. - "่พๅฅฝ็ๆๆ" โ must be quantified: "improved RMSE by X%" not "good results".
# Rules
- Polish language and structure; do not invent new content. - Downgrade overclaims; do not upgrade weak claims to sound stronger. - Flag unsupported claims as issues; do not silently remove or modify them. - Do not change formulas without checking against the final method explanation. - Do not add new references, experiments, figures, or numerical values. - Do not remove limitations or uncertainty statements. - Keep changes traceable โ show what was changed and why. - If the underlying argument is broken, flag it rather than polishing over it.
# Verification
Before handing off, verify:
- Every modified sentence is grammatically correct. - Every formula cross-checked against the final method explanation. - Every claim calibrated to match available evidence. - Overclaims are flagged or downgraded. - Notation is consistent across all sections. - Figure/table references are in order and correspond to existing files. - Contest formatting requirements are met. - A change summary is produced.
# Failure modes
Stop and report a blocker if:
- A claim in the paper has no supporting evidence at all (not just weak evidence โ NO evidence). - A formula in the paper contradicts the final method explanation. - A referenced figure or table does not exist. - A numerical value in the paper cannot be found in any result file. - The paper claims a result for a subquestion that has no final result analysis.
# Stop conditions
This skill must stop instead of guessing when:
- Fixing language would require changing the scientific meaning. - The evidence for a claim is entirely absent. - Multiple contradictory claims exist in the same section. - A referenced artifact cannot be found. - Continuing would hide a fundamental logical flaw under polished prose.
When stopping, output: - the blocker - the affected sentence or paragraph - the missing or contradictory evidence - recommended action
# Handoff
After polishing: โ `quality-assurance-auditor`
With: - polished section paths - change summary (what was modified and why) - flagged overclaims (downgraded or awaiting author decision) - remaining issues needing author attention
# Examples
## Example 1:
Source provenance
Decision snapshot
695 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 paper-polisher, ready for a manual X post.
paper-polisher: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging... 695 stars https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x
Listing + install path for paper-polisher: https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x Install: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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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 "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/paper-polisher. 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: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. 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":"zhnnky329-paper-polisher","task":"Install paper-polisher","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 zhnnky329/MathModeling-skills --skill paper-polisher
Maintenance
fresh
12d since push
Risk
Safe to try
Quality score needs review
GitHub quality
695
75/100 Quality ยท 83/100 Trust
Coverage tags
Review notes
Quality score needs review
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
695 GitHub stars
Repo activity
695 stars, 31 forks
Maintenance
12d since push
License
MIT
Install
npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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 zhnnky329/MathModeling-skills --skill paper-polisherDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/zhnnky329-paper-polisher/install
Agent should check
Copy prompt
Task: Use paper-polisher in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install
Install command: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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/zhnnky329-paper-polisher/install
LLM text format
/api/skills/zhnnky329-paper-polisher/install?format=text
Find alternatives
/api/skills/search?q=paper-polisher&limit=3
Agent prompt
Use paper-polisher for this task. Review https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install, then install with: npx skills add zhnnky329/MathModeling-skills --skill paper-polisherRegistry 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/zhnnky329-paper-polisher
LLM text
/api/registry/manifest/zhnnky329-paper-polisher?format=text
Install alias
/api/registry/install/zhnnky329-paper-polisher
Recommend
/api/registry/recommend?task=Use%20paper-polisher%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
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO695 GitHub stars
Stars/forks activity
INFO695 stars, 31 forks; issue activity unavailable in current metadata
Recent maintenance
PASS12d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
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Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
Infisical is the open-source platform for secrets, certificates, and privileged access management.
--- name: paper-polisher description: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. license: MIT ---
# Purpose
Polish mathematical modeling contest paper sections for language quality, logical clarity, formula consistency, and claim calibration.
This skill operates on already-drafted paper sections. It improves wording, fixes grammar, checks formulas, calibrates hedging to match evidence strength, detects overclaims, and ensures formatting compliance. It does not invent new content, add unsupported claims, or rewrite the paper's scientific argument.
Adapted from [nature-polishing](https://github.com/Yuan1z0825/nature-skills) design principles: language serves the argument, polish should not hide weak reasoning, and claims must be proportional to evidence.
This skill does not write new paper sections, run experiments, generate figures, or perform final QA.
# When to use
Use this skill:
- After `paper-section-writer` has drafted one or more paper sections. - Before `quality-assurance-auditor`. - When the user says: "polish the paper", "check the English", "fix the grammar", "improve the writing", "calibrate the claims", "check for overclaims", "proofread Q1 section". - When Chinese-to-English translation has produced rough drafts that need smoothing. - When formulas, notation, or terminology are inconsistent across sections.
# Preconditions
The following should already exist or be provided:
- Paper section drafts under `paper/sections/`. - Final method explanations (for formula and notation verification). - Final result analyses (for claim verification). - The global symbol table at `planning/symbol_table.md` (if available). - Contest formatting requirements (if available).
If paper sections do not exist, hand back to `paper-section-writer`.
# Inputs
Use or request:
- `paper/sections/*.md` or `paper/sections/*.tex` โ the drafted sections. - `methods/Qx/qx_final_method_explanation.md` โ for formula and notation verification. - `results/Qx/reports/qx_final_result_analysis.md` โ for claim verification. - `planning/symbol_table.md` โ for notation consistency. - Contest formatting requirements.
# Workflow
1. Identify the paper type and section. - Mathematical modeling contest papers follow a standard structure: Abstract โ Problem Restatement โ Problem Analysis โ Assumptions โ Symbols โ Model Construction (per Q) โ Model Solution โ Results Analysis โ Robustness โ Strengths & Limitations โ Conclusion. - Each section has different polishing priorities (see section-specific rules below).
2. Run the 12-point polish checklist (see below).
3. Calibrate claims against evidence. - For each numerical or comparative claim, verify it is supported by the final result analysis or robustness report. - If a claim overstates the evidence, downgrade the language. - If a claim is unsupported, flag it as a blocker (do not silently remove โ the writer needs to decide).
4. Check formula and notation consistency. - Every symbol must appear in the global symbol table or be defined locally. - Same concept must use the same symbol across all sections. - Subscripts, superscripts, and indices must be consistent. - Formula numbering must be sequential and match references in text.
5. Check terminology consistency. - Same concept must use the same term throughout. - Method names must match the final method explanation. - "Baseline", "main model", "improved model" must be used consistently.
6. Produce polished sections. - Show a diff or change summary. - Mark any claims that were downgraded and why. - Flag any remaining issues that need author attention.
# 12-Point Polish Checklist
## 1. Sentence Length - Split sentences longer than 30 words. - Vary sentence length: mix short (8-15 words) and medium (15-25 words). - The first and last sentences of each paragraph should be the clearest.
## 2. Paragraph Structure - Each paragraph should have one main point. - Topic sentence first, support following, transition at end (or beginning of next). - Paragraphs longer than 5-6 sentences should be split or tightened.
## 3. Tense Consistency - **Problem restatement / Assumptions / Symbols**: Present tense. - **Model construction**: Present tense for model description. - **Model solution / Results analysis**: Past tense for what was done and found. - **Conclusion**: Present tense for final findings, past tense for what was done. - Do not mix tenses within a single paragraph without reason.
## 4. Hedging Calibration
Match claim strength to evidence:
| Evidence Level | Appropriate Hedging | Example | |---------------|-------------------|---------| | Robust, multiple checks | Strong claim, no hedge | "The entropy-TOPSIS method produces stable rankings." | | Single check, moderate perturbation | Moderate hedge | "The rankings appear stable under moderate weight changes." | | Limited check, narrow range | Weak hedge | "The results suggest that rankings may be stable within the tested range." | | No check, extrapolation | No claim allowed | Flag as unsupported. Do not write. |
Hedging phrases (strongest to weakest): - `demonstrates` / `shows` / `establishes` โ strongest - `indicates` / `suggests` / `supports` โ moderate - `may indicate` / `appears to` / `is consistent with` โ weak - `could potentially` / `might possibly` โ weakest (use sparingly)
## 5. Overclaim Detection
Flag and downgrade or remove: - Absolute claims: "always", "never", "proves", "guarantees", "optimal" (unless proven). - Unwarranted causation: "A causes B" when only correlation is shown. - Scope expansion: "All models benefit from..." when only one model was tested. - Unverified "first" or "novel" claims. - "Significantly" without statistical test or defined threshold. - "Our model outperforms all existing methods" when only 1-2 baselines were compared. - Numerical precision beyond data support: "The score is 0.883214" โ "The score is approximately 0.88".
## 6. Formula Formatting - Formulas in display math mode (`$$...$$` or `\begin{equation}...\end{equation}`) for important equations. - Inline math (`$...$`) for variable references and short expressions. - Consistent subscript/superscript style. - Units after numerical values. - Variable definitions immediately after first use in a formula.
## 7. Notation Consistency - Cross-check every symbol against `planning/symbol_table.md`. - Decision variables vs state variables vs parameters must be distinguished. - Vector/matrix notation must be consistent (bold, arrow, or neither โ pick one).
## 8. Figure and Table References - Every `\ref{fig:...}` or "Figure X" must correspond to an actual figure file. - Figure references must be in order (Fig.1 before Fig.2 in text). - Every table reference must correspond to an actual table. - Captions must include the main takeaway, not just a description.
## 9. Transition and Flow - Between sections: one bridging sentence connecting to the next section. - Between paragraphs: logical flow (therefore, however, in contrast, furthermore, specifically). - Avoid "As mentioned above" / "As discussed previously" โ restate briefly instead. - Avoid "It is worth noting that..." / "It should be mentioned that..." โ just state it.
## 10. Word Choice - Prefer specific over vague: "RMSE improved by 35%" not "the error got better". - Prefer simple over ornate: "use" not "utilize", "show" not "elucidate", "about" not "approximately" (unless precision matters). - Remove filler: "It is important to note that", "Interestingly", "Remarkably". - Remove redundant pairs: "various different", "basic fundamentals", "advance planning".
## 11. Voice - Prefer active voice for clarity: "We applied TOPSIS to the indicator matrix" not "TOPSIS was applied to the indicator matrix". - Use passive voice sparingly, mainly in Methods/Model Solution: "The weights were computed using the entropy method". - Use "we" consistently (not "the authors", "this paper", "the research team"). - In ChineseโEnglish translation: avoid literal translation of Chinese academic conventions.
## 12. Formatting Compliance - Check contest-specific formatting: word count, page limit, font size, margin requirements. - Section numbering is consistent. - Reference format is consistent. - Appendix materials are properly labeled.
# Section-Specific Polish Priorities
| Section | Top Priority | |---------|-------------| | Abstract | Claim calibration, numerical precision, word count | | Problem Restatement | Clarity, no added requirements | | Assumptions | Necessity check, impact statements | | Symbols | Completeness, consistency, distinction of variable types | | Model Construction | Formula correctness, notation consistency, assumption traceability | | Model Solution | Procedural clarity, reproducibility | | Results Analysis | Claim-evidence alignment, figure/table references | | Robustness | Stable vs fragile separation, boundary conditions | | Strengths & Limitations | Specificity, honesty | | Conclusion | Subquestion coverage, claim calibration |
# Chinese-to-English Translation Notes
When the source text is in Chinese and needs translation to English: - Do not translate literally. Translate the MEANING. - Chinese academic writing often uses more hedging; keep only what the evidence supports. - Chinese sentences tend to be longer; split into shorter English sentences. - "ๆฌๆ" โ "This paper" or "We" depending on context. - "ๆพ็ถ" / "ๆพ่ๆ่ง" โ avoid "obviously" unless truly obvious; use "clearly" only with strong justification. - "ไธๅฎ็" โ drop or replace with specific quantifier. - "่พๅฅฝ็ๆๆ" โ must be quantified: "improved RMSE by X%" not "good results".
# Rules
- Polish language and structure; do not invent new content. - Downgrade overclaims; do not upgrade weak claims to sound stronger. - Flag unsupported claims as issues; do not silently remove or modify them. - Do not change formulas without checking against the final method explanation. - Do not add new references, experiments, figures, or numerical values. - Do not remove limitations or uncertainty statements. - Keep changes traceable โ show what was changed and why. - If the underlying argument is broken, flag it rather than polishing over it.
# Verification
Before handing off, verify:
- Every modified sentence is grammatically correct. - Every formula cross-checked against the final method explanation. - Every claim calibrated to match available evidence. - Overclaims are flagged or downgraded. - Notation is consistent across all sections. - Figure/table references are in order and correspond to existing files. - Contest formatting requirements are met. - A change summary is produced.
# Failure modes
Stop and report a blocker if:
- A claim in the paper has no supporting evidence at all (not just weak evidence โ NO evidence). - A formula in the paper contradicts the final method explanation. - A referenced figure or table does not exist. - A numerical value in the paper cannot be found in any result file. - The paper claims a result for a subquestion that has no final result analysis.
# Stop conditions
This skill must stop instead of guessing when:
- Fixing language would require changing the scientific meaning. - The evidence for a claim is entirely absent. - Multiple contradictory claims exist in the same section. - A referenced artifact cannot be found. - Continuing would hide a fundamental logical flaw under polished prose.
When stopping, output: - the blocker - the affected sentence or paragraph - the missing or contradictory evidence - recommended action
# Handoff
After polishing: โ `quality-assurance-auditor`
With: - polished section paths - change summary (what was modified and why) - flagged overclaims (downgraded or awaiting author decision) - remaining issues needing author attention
# Examples
## Example 1:
Source provenance
Decision snapshot
695 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 paper-polisher, ready for a manual X post.
paper-polisher: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging... 695 stars https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x
Listing + install path for paper-polisher: https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x Install: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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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 "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/paper-polisher. 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: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. 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":"zhnnky329-paper-polisher","task":"Install paper-polisher","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 zhnnky329/MathModeling-skills --skill paper-polisher
Maintenance
fresh
12d since push
Risk
Safe to try
Quality score needs review
GitHub quality
695
75/100 Quality ยท 83/100 Trust
Coverage tags
Review notes
Quality score needs review
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
695 GitHub stars
Repo activity
695 stars, 31 forks
Maintenance
12d since push
License
MIT
Install
npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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 zhnnky329/MathModeling-skills --skill paper-polisherDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
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-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/zhnnky329-paper-polisher/install
Agent should check
Copy prompt
Task: Use paper-polisher in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20paper-polisher%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install
Install command: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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/zhnnky329-paper-polisher/install
LLM text format
/api/skills/zhnnky329-paper-polisher/install?format=text
Find alternatives
/api/skills/search?q=paper-polisher&limit=3
Agent prompt
Use paper-polisher for this task. Review https://www.openagentskill.com/api/skills/zhnnky329-paper-polisher/install, then install with: npx skills add zhnnky329/MathModeling-skills --skill paper-polisherRegistry 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/zhnnky329-paper-polisher
LLM text
/api/registry/manifest/zhnnky329-paper-polisher?format=text
Install alias
/api/registry/install/zhnnky329-paper-polisher
Recommend
/api/registry/recommend?task=Use%20paper-polisher%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
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
INFO695 GitHub stars
Stars/forks activity
INFO695 stars, 31 forks; issue activity unavailable in current metadata
Recent maintenance
PASS12d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
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Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
Infisical is the open-source platform for secrets, certificates, and privileged access management.
--- name: paper-polisher description: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections. license: MIT ---
# Purpose
Polish mathematical modeling contest paper sections for language quality, logical clarity, formula consistency, and claim calibration.
This skill operates on already-drafted paper sections. It improves wording, fixes grammar, checks formulas, calibrates hedging to match evidence strength, detects overclaims, and ensures formatting compliance. It does not invent new content, add unsupported claims, or rewrite the paper's scientific argument.
Adapted from [nature-polishing](https://github.com/Yuan1z0825/nature-skills) design principles: language serves the argument, polish should not hide weak reasoning, and claims must be proportional to evidence.
This skill does not write new paper sections, run experiments, generate figures, or perform final QA.
# When to use
Use this skill:
- After `paper-section-writer` has drafted one or more paper sections. - Before `quality-assurance-auditor`. - When the user says: "polish the paper", "check the English", "fix the grammar", "improve the writing", "calibrate the claims", "check for overclaims", "proofread Q1 section". - When Chinese-to-English translation has produced rough drafts that need smoothing. - When formulas, notation, or terminology are inconsistent across sections.
# Preconditions
The following should already exist or be provided:
- Paper section drafts under `paper/sections/`. - Final method explanations (for formula and notation verification). - Final result analyses (for claim verification). - The global symbol table at `planning/symbol_table.md` (if available). - Contest formatting requirements (if available).
If paper sections do not exist, hand back to `paper-section-writer`.
# Inputs
Use or request:
- `paper/sections/*.md` or `paper/sections/*.tex` โ the drafted sections. - `methods/Qx/qx_final_method_explanation.md` โ for formula and notation verification. - `results/Qx/reports/qx_final_result_analysis.md` โ for claim verification. - `planning/symbol_table.md` โ for notation consistency. - Contest formatting requirements.
# Workflow
1. Identify the paper type and section. - Mathematical modeling contest papers follow a standard structure: Abstract โ Problem Restatement โ Problem Analysis โ Assumptions โ Symbols โ Model Construction (per Q) โ Model Solution โ Results Analysis โ Robustness โ Strengths & Limitations โ Conclusion. - Each section has different polishing priorities (see section-specific rules below).
2. Run the 12-point polish checklist (see below).
3. Calibrate claims against evidence. - For each numerical or comparative claim, verify it is supported by the final result analysis or robustness report. - If a claim overstates the evidence, downgrade the language. - If a claim is unsupported, flag it as a blocker (do not silently remove โ the writer needs to decide).
4. Check formula and notation consistency. - Every symbol must appear in the global symbol table or be defined locally. - Same concept must use the same symbol across all sections. - Subscripts, superscripts, and indices must be consistent. - Formula numbering must be sequential and match references in text.
5. Check terminology consistency. - Same concept must use the same term throughout. - Method names must match the final method explanation. - "Baseline", "main model", "improved model" must be used consistently.
6. Produce polished sections. - Show a diff or change summary. - Mark any claims that were downgraded and why. - Flag any remaining issues that need author attention.
# 12-Point Polish Checklist
## 1. Sentence Length - Split sentences longer than 30 words. - Vary sentence length: mix short (8-15 words) and medium (15-25 words). - The first and last sentences of each paragraph should be the clearest.
## 2. Paragraph Structure - Each paragraph should have one main point. - Topic sentence first, support following, transition at end (or beginning of next). - Paragraphs longer than 5-6 sentences should be split or tightened.
## 3. Tense Consistency - **Problem restatement / Assumptions / Symbols**: Present tense. - **Model construction**: Present tense for model description. - **Model solution / Results analysis**: Past tense for what was done and found. - **Conclusion**: Present tense for final findings, past tense for what was done. - Do not mix tenses within a single paragraph without reason.
## 4. Hedging Calibration
Match claim strength to evidence:
| Evidence Level | Appropriate Hedging | Example | |---------------|-------------------|---------| | Robust, multiple checks | Strong claim, no hedge | "The entropy-TOPSIS method produces stable rankings." | | Single check, moderate perturbation | Moderate hedge | "The rankings appear stable under moderate weight changes." | | Limited check, narrow range | Weak hedge | "The results suggest that rankings may be stable within the tested range." | | No check, extrapolation | No claim allowed | Flag as unsupported. Do not write. |
Hedging phrases (strongest to weakest): - `demonstrates` / `shows` / `establishes` โ strongest - `indicates` / `suggests` / `supports` โ moderate - `may indicate` / `appears to` / `is consistent with` โ weak - `could potentially` / `might possibly` โ weakest (use sparingly)
## 5. Overclaim Detection
Flag and downgrade or remove: - Absolute claims: "always", "never", "proves", "guarantees", "optimal" (unless proven). - Unwarranted causation: "A causes B" when only correlation is shown. - Scope expansion: "All models benefit from..." when only one model was tested. - Unverified "first" or "novel" claims. - "Significantly" without statistical test or defined threshold. - "Our model outperforms all existing methods" when only 1-2 baselines were compared. - Numerical precision beyond data support: "The score is 0.883214" โ "The score is approximately 0.88".
## 6. Formula Formatting - Formulas in display math mode (`$$...$$` or `\begin{equation}...\end{equation}`) for important equations. - Inline math (`$...$`) for variable references and short expressions. - Consistent subscript/superscript style. - Units after numerical values. - Variable definitions immediately after first use in a formula.
## 7. Notation Consistency - Cross-check every symbol against `planning/symbol_table.md`. - Decision variables vs state variables vs parameters must be distinguished. - Vector/matrix notation must be consistent (bold, arrow, or neither โ pick one).
## 8. Figure and Table References - Every `\ref{fig:...}` or "Figure X" must correspond to an actual figure file. - Figure references must be in order (Fig.1 before Fig.2 in text). - Every table reference must correspond to an actual table. - Captions must include the main takeaway, not just a description.
## 9. Transition and Flow - Between sections: one bridging sentence connecting to the next section. - Between paragraphs: logical flow (therefore, however, in contrast, furthermore, specifically). - Avoid "As mentioned above" / "As discussed previously" โ restate briefly instead. - Avoid "It is worth noting that..." / "It should be mentioned that..." โ just state it.
## 10. Word Choice - Prefer specific over vague: "RMSE improved by 35%" not "the error got better". - Prefer simple over ornate: "use" not "utilize", "show" not "elucidate", "about" not "approximately" (unless precision matters). - Remove filler: "It is important to note that", "Interestingly", "Remarkably". - Remove redundant pairs: "various different", "basic fundamentals", "advance planning".
## 11. Voice - Prefer active voice for clarity: "We applied TOPSIS to the indicator matrix" not "TOPSIS was applied to the indicator matrix". - Use passive voice sparingly, mainly in Methods/Model Solution: "The weights were computed using the entropy method". - Use "we" consistently (not "the authors", "this paper", "the research team"). - In ChineseโEnglish translation: avoid literal translation of Chinese academic conventions.
## 12. Formatting Compliance - Check contest-specific formatting: word count, page limit, font size, margin requirements. - Section numbering is consistent. - Reference format is consistent. - Appendix materials are properly labeled.
# Section-Specific Polish Priorities
| Section | Top Priority | |---------|-------------| | Abstract | Claim calibration, numerical precision, word count | | Problem Restatement | Clarity, no added requirements | | Assumptions | Necessity check, impact statements | | Symbols | Completeness, consistency, distinction of variable types | | Model Construction | Formula correctness, notation consistency, assumption traceability | | Model Solution | Procedural clarity, reproducibility | | Results Analysis | Claim-evidence alignment, figure/table references | | Robustness | Stable vs fragile separation, boundary conditions | | Strengths & Limitations | Specificity, honesty | | Conclusion | Subquestion coverage, claim calibration |
# Chinese-to-English Translation Notes
When the source text is in Chinese and needs translation to English: - Do not translate literally. Translate the MEANING. - Chinese academic writing often uses more hedging; keep only what the evidence supports. - Chinese sentences tend to be longer; split into shorter English sentences. - "ๆฌๆ" โ "This paper" or "We" depending on context. - "ๆพ็ถ" / "ๆพ่ๆ่ง" โ avoid "obviously" unless truly obvious; use "clearly" only with strong justification. - "ไธๅฎ็" โ drop or replace with specific quantifier. - "่พๅฅฝ็ๆๆ" โ must be quantified: "improved RMSE by X%" not "good results".
# Rules
- Polish language and structure; do not invent new content. - Downgrade overclaims; do not upgrade weak claims to sound stronger. - Flag unsupported claims as issues; do not silently remove or modify them. - Do not change formulas without checking against the final method explanation. - Do not add new references, experiments, figures, or numerical values. - Do not remove limitations or uncertainty statements. - Keep changes traceable โ show what was changed and why. - If the underlying argument is broken, flag it rather than polishing over it.
# Verification
Before handing off, verify:
- Every modified sentence is grammatically correct. - Every formula cross-checked against the final method explanation. - Every claim calibrated to match available evidence. - Overclaims are flagged or downgraded. - Notation is consistent across all sections. - Figure/table references are in order and correspond to existing files. - Contest formatting requirements are met. - A change summary is produced.
# Failure modes
Stop and report a blocker if:
- A claim in the paper has no supporting evidence at all (not just weak evidence โ NO evidence). - A formula in the paper contradicts the final method explanation. - A referenced figure or table does not exist. - A numerical value in the paper cannot be found in any result file. - The paper claims a result for a subquestion that has no final result analysis.
# Stop conditions
This skill must stop instead of guessing when:
- Fixing language would require changing the scientific meaning. - The evidence for a claim is entirely absent. - Multiple contradictory claims exist in the same section. - A referenced artifact cannot be found. - Continuing would hide a fundamental logical flaw under polished prose.
When stopping, output: - the blocker - the affected sentence or paragraph - the missing or contradictory evidence - recommended action
# Handoff
After polishing: โ `quality-assurance-auditor`
With: - polished section paths - change summary (what was modified and why) - flagged overclaims (downgraded or awaiting author decision) - remaining issues needing author attention
# Examples
## Example 1:
Source provenance
Decision snapshot
695 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 paper-polisher, ready for a manual X post.
paper-polisher: Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging... 695 stars https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x
Listing + install path for paper-polisher: https://www.openagentskill.com/skills/zhnnky329-paper-polisher?ref=x Install: npx skills add zhnnky329/MathModeling-skills --skill paper-polisher
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 zhnnky329 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.
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Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
Wazuh
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
16.3K StarsMaigret
๐ต๏ธโโ๏ธ Collect a dossier on a person by username from 3000+ sites
32.9K StarsNuclei
Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
29.2K StarsInfisical
Infisical is the open-source platform for secrets, certificates, and privileged access management.
27.4K 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