Wp Performance Review
elvismdev/claude-wordpress-skills
WordPress performance code review and optimization analysis.
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance.
$ npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zhnnky329/MathModeling-skills paper-polisher --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/paper-polisher .claude/skills/paper-polisher && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisher into .claude/skills/paper-polisher/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paper-polisher", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisherType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zhnnky329/MathModeling-skills paper-polisher --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.codex/skills/paper-polisher .agents/skills/paper-polisher && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisher into .agents/skills/paper-polisher/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paper-polisher", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zhnnky329/MathModeling-skills paper-polisher --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.codex/skills/paper-polisher .cursor/skills/paper-polisher && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisher into .cursor/skills/paper-polisher/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paper-polisher", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zhnnky329/MathModeling-skills.git --path .codex/skills/paper-polisher--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zhnnky329/MathModeling-skills paper-polisher --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.codex/skills/paper-polisher .gemini/skills/paper-polisher && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisher into .gemini/skills/paper-polisher/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paper-polisher", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zhnnky329/MathModeling-skills paper-polisherInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/.codex/skills/paper-polisher .github/skills/paper-polisher && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisher into .github/skills/paper-polisher/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paper-polisher", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zhnnky329/MathModeling-skills paper-polisher --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.codex/skills/paper-polisher .opencode/skills/paper-polisher && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "paper-polisher" agent skill from https://github.com/zhnnky329/MathModeling-skills/tree/main/.codex/skills/paper-polisher into .opencode/skills/paper-polisher/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paper-polisher", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
paper-polisherPolish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance.
Paper Polisher is an agent skill from zhnnky329/MathModeling-skills. 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.
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Business, Finance & HR, covering Performance reviews. The repository describes itself as: 面向数学建模竞赛的 Claude Code / Codex Skills ,支持分阶段建模流程与 Python、MATLAB/北太天元代码分支。 The licence is MIT.
12 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0b46e9c. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Paper Polisher loads about 3.4k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 1,734 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from zhnnky329/MathModeling-skills at commit 0b46e9c, republished under its MIT licence (© zhnnky329). 1,734 words, ~3,444 tokens.
.claude/skills/paper-polisher/SKILL.md (or your agent's skills folder).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 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.
Use this skill:
paper-section-writer has drafted one or more paper sections.quality-assurance-auditor.The following should already exist or be provided:
paper/sections/.planning/symbol_table.md (if available).If paper sections do not exist, hand back to paper-section-writer.
Use or request:
paper/sections/*.md or paper/sections/*.tex — the drafted sections.methods/Qx/qx_final_method_explanation.md — for formula and notation verification.results/Qx/reports/qx_final_result_analysis.md — for claim verification.planning/symbol_table.md — for notation consistency.Identify the paper type and section.
Run the 12-point polish checklist (see below).
Calibrate claims against evidence.
Check formula and notation consistency.
Check terminology consistency.
Produce polished sections.
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 → strongestindicates / suggests / supports → moderatemay indicate / appears to / is consistent with → weakcould potentially / might possibly → weakest (use sparingly)Flag and downgrade or remove:
$$...$$ or \begin{equation}...\end{equation}) for important equations.$...$) for variable references and short expressions.planning/symbol_table.md.\ref{fig:...} or "Figure X" must correspond to an actual figure file.| 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 |
When the source text is in Chinese and needs translation to English:
Before handing off, verify:
Stop and report a blocker if:
This skill must stop instead of guessing when:
When stopping, output:
After polishing:
→ quality-assurance-auditor
With:
Original: "Our entropy-TOPSIS model demonstrates significantly better performance than all existing evaluation methods, achieving optimal ranking accuracy."
Polish: "The entropy-TOPSIS model improves score differentiation compared to the equal-weight baseline (standard deviation: 0.15 vs 0.08). Among the three candidate methods tested (equal-weight, entropy-TOPSIS, AHP-TOPSIS), entropy-TOPSIS was selected for its objective weight derivation and ranking stability under ±10% perturbation."
Changes:
Original: "Let x_i be the score of city i. The weight w_j is computed by..."
Fixed: "Let $S_i$ be the composite score of city $i$. The indicator weight $\omega_j$ is computed by entropy method..."
Issue: x_i was used for scores in Q1 but for raw indicator values in Q2. Changed to S_i to match the global symbol table, and distinguished \omega_j (weight) from w_j (used elsewhere for a different variable).
Original (38 words): "After applying the entropy method to compute objective indicator weights and then using the TOPSIS method to compute the relative closeness of each city to the ideal solution, we obtained the final ranking as shown in Table 1."
Polished (two sentences, 15 + 14 words): "Entropy weighting produced objective indicator weights from data dispersion. TOPSIS then computed each city's relative closeness to the ideal solution, yielding the final ranking (Table 1)."
© zhnnky329, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .codex/skills/paper-polisher of zhnnky329/MathModeling-skills.
Open the folder on GitHubat commit 0b46e9c
Paper Polisher next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Paper Polisher this skillzhnnky329/MathModeling-skills | 1.1k | — | ~3.4k | Automated safety check: Pass | MIT | |
| Wp Performance Reviewelvismdev/claude-wordpress-skills | 235 | 1 repos | ~4.5k | Automated safety check: Pass | MIT | |
| Align Humanagentscope-ai/OpenJudge | 871 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Run Mv Hoi Reconstructionnvidia-isaac/video_to_data | 861 | — | ~1.5k | Automated safety check: Pass | Custom licence | |
| Company Analysiszhu1090093659/dsh-trading | 238 | — | ~4.2k | Automated safety check: Pass | Custom licence | |
| Windbg Diagnostic Methodmicrosoft/win-dev-skills | 466 | — | ~1.9k | Automated safety check: Pass | MIT |
elvismdev/claude-wordpress-skills
WordPress performance code review and optimization analysis.
agentscope-ai/OpenJudge
A skill your agent uses when the user has a judge/grader and human-labeled data, and wants to measure how well the judge agrees with humans, detect systematic biases, determine whether automatic…
nvidia-isaac/video_to_data
Run and validate the repository-local multi-view camera calibration and human-object reconstruction pipelines.
zhu1090093659/dsh-trading
A skill your agent uses when the user wants to analyze a listed company, stock, business, or investment target; challenge or revise an existing company report; compare A/H or primary-listing/ADR…
microsoft/win-dev-skills
Use with every WinDbg plugin investigation to apply evidence-first reasoning, confidence calibration, contrarian review, structured reporting, and deterministic validation.
mizchi/skills
Method and tooling for measuring how AI-generated a piece of prose reads, in Japanese or English.
zhnnky329/MathModeling-skills
Build and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion.
zhnnky329/MathModeling-skills
Classify each parsed mathematical-modeling subquestion by required output and structure, surface ambiguous framing trade-offs for human choice, and record primary/secondary task types without…
zhnnky329/MathModeling-skills
Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream…
zhnnky329/MathModeling-skills
Build one compact choice card at a genuine mathematical-modeling judgment point.
zhnnky329/MathModeling-skills
Generate and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.
zhnnky329/MathModeling-skills
Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract.
Categories
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Paper Polisher is an agent skill from zhnnky329/MathModeling-skills. Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance.
Paper Polisher fits situations like: tasks that involve Performance reviews.
Run `npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a claude-code`. Or copy the skill folder (.codex/skills/paper-polisher in zhnnky329/MathModeling-skills) into .claude/skills/paper-polisher in your project. Claude Code loads it when a task matches its description.
Run `npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a codex`. Or copy the skill folder (.codex/skills/paper-polisher in zhnnky329/MathModeling-skills) into .agents/skills/paper-polisher in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zhnnky329/MathModeling-skills --skill paper-polisher -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/paper-polisher, .gemini/skills/paper-polisher, .github/skills/paper-polisher and .opencode/skills/paper-polisher in your project.
SKILL.md names no scripts, command-line tools or credentials: Paper Polisher is instructions for the agent only.
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Paper Polisher is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Paper Polisher: Wp Performance Review (elvismdev/claude-wordpress-skills, 235 stars), Align Human (agentscope-ai/OpenJudge, 871 stars), Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 861 stars) and Company Analysis (zhu1090093659/dsh-trading, 238 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
zhnnky329 (a GitHub user) maintains it in zhnnky329/MathModeling-skills, which has 1,060 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 24, 2026.
Source: zhnnky329/MathModeling-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.