polish-claw-project
À propos
Cette compétence propose un processus structuré en 9 étapes permettant aux développeurs de contribuer à l'amélioration de la sécurité et du code des projets de l'écosystème OpenClaw. Elle met l'accent sur l'audit parallèle, la prévention des faux positifs et la vérification croisée des découvertes avec les problèmes existants, afin de garantir des demandes de fusion à fort impact. Utilisez-la pour des contributions systématiques et respectueuses des conventions aux dépôts OpenClaw, NemoClaw ou NanoClaw.
Installation rapide
Claude Code
Recommandénpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/polish-claw-projectCopiez et collez cette commande dans Claude Code pour installer cette compétence
Documentation
Polish Claw Project
Structured workflow for contributing to OpenClaw ecosystem projects. Novel value in Steps 5-7: parallel audit, false positive prevention, cross-referencing findings against open issues to select high-impact contributions. Mechanical steps (fork, PR creation) delegate to existing skills.
When Use
- Contributing to NVIDIA/OpenClaw, NVIDIA/NemoClaw, NVIDIA/NanoClaw, or similar Claw ecosystem repos
- First-time contributions to unfamiliar open-source project with security-sensitive architecture
- Want repeatable, auditable contribution workflow rather than ad-hoc fixes
- After identifying Claw project that accepts external contributions (check CONTRIBUTING.md)
Inputs
- Required:
repo_url— GitHub URL of target Claw project (e.g.https://github.com/NVIDIA/NemoClaw) - Optional:
contribution_count— Number of contributions to aim for (default: 1-3)focus— Preferred contribution type:security,tests,docs,bugs,any(default:any)fork_org— GitHub org/user to fork into (default: authenticated user)
Steps
Step 1: Identify and Verify Target
Confirm project accepts external contributions and is actively maintained.
- Open repository URL. Read
CONTRIBUTING.md,CODE_OF_CONDUCT.md,LICENSE - Check recent commit activity (last 30 days) and open PR merge rate
- Verify project uses permissive or contribution-friendly license
- Read
SECURITY.mdor security policy if present — note responsible disclosure rules - Identify primary language, test framework, CI system
Got: CONTRIBUTING.md exists. Commits within last 30 days. Clear contribution guidelines.
If fail: No CONTRIBUTING.md or no recent activity? Document why and stop. Stale projects rarely merge external PRs.
Step 2: Fork and Clone
Build working copy of repository.
- Fork:
gh repo fork <repo_url> --clone - Set upstream remote:
git remote add upstream <repo_url> - Verify:
git remote -vshows bothorigin(fork) andupstream - Sync:
git fetch upstream && git checkout main && git merge upstream/main
Got: Local clone with both remotes configured and up to date.
If fail: Fork fails? Check GitHub authentication (gh auth status). Clone slow? Try --depth=1 for initial exploration.
Step 3: Explore Codebase
Build mental model of project architecture.
- Read
README.mdfor architecture overview and project goals - Identify entry points, core modules, public API surface
- Map test structure: where tests live, what framework, coverage level
- Note code style conventions: linter config, naming patterns, import style
- Check for Docker/container setup, CI configuration, deployment patterns
Got: Clear understanding of project structure, conventions, where contributions would fit.
If fail: Architecture unclear? Focus on specific subsystem rather than whole project.
Step 4: Read Open Issues
Survey existing issues to understand project needs and avoid duplicate work.
- List open issues:
gh issue list --state open --limit 50 - Categorize by type: bugs, features, docs, security, good-first-issue
- Note issues labeled
help wanted,good first issue,hacktoberfest - Check for stale issues (>90 days open, no recent comments) — may be abandoned
- Read any linked PRs to understand attempted solutions
Got: Categorized list of unclaimed issues with type labels.
If fail: No open issues exist? Proceed to Step 5 — audit may uncover unlisted improvements.
Step 5: Parallel Audit
Run security and code quality audits in parallel. This is where novel findings emerge.
- Run
security-audit-codebaseskill against project root - Simultaneously run
review-codebaseskill with scopequality - Critical: verify each finding against project's threat model and architecture
- "Hardcoded secret" in sandbox bootstrap script is not a vulnerability
- Missing input validation on internal-only function is low severity
- Dependency flagged as vulnerable may already be mitigated by project's architecture
- Rate verified findings: CRITICAL, HIGH, MEDIUM, LOW
- Document false positives with reasoning — informs Pitfalls for future runs
Got: List of verified findings with severity ratings and false positive annotations.
If fail: No findings emerge? Shift focus to test coverage gaps, documentation improvements, developer experience enhancements.
Step 6: Cross-Reference Findings
Map verified audit findings to open issues — core judgment step.
- For each verified finding, search open issues for related discussions
- Categorize each finding as:
- Matches open issue — link finding to issue
- New finding — no existing issue covers this
- Already fixed in PR — check open PRs for in-progress fixes
- Prioritize findings matching existing issues (highest merge probability)
- For new findings, assess if maintainers would welcome fix based on project priorities
Got: Prioritized list with finding-to-issue mapping and merge probability assessment.
If fail: All findings already addressed? Return to Step 4. Look for documentation, test, or developer experience contributions.
Step 7: Select Contributions
Pick 1-3 contributions based on impact, effort, expertise.
- Score each candidate on:
- Impact: How much does this improve project? (security > bugs > tests > docs)
- Effort: Can this be done well in focused session? (prefer small, complete PRs)
- Expertise: Does contributor have domain knowledge for this fix?
- Merge probability: Does this match stated project priorities?
- Select top candidates (default: 1-3)
- For each, define: branch name, scope boundary, acceptance criteria, test plan
Got: 1-3 selected contributions with clear scope and acceptance criteria.
If fail: No contributions score well? Consider filing well-written issues instead of PRs.
Step 8: Implement
Create branch per contribution. Implement fix.
- For each contribution:
git checkout -b fix/<description> - Follow project conventions exactly (linter, naming, import style)
- Add or update tests covering change
- Run project's test suite. Verify all tests pass
- Run project's linter. Verify no new warnings
- Keep each PR focused — one logical change per branch
Got: Clean implementation with passing tests and no linter warnings.
If fail: Tests fail on pre-existing issues? Document them. Ensure PR doesn't introduce new failures.
Step 9: Create Pull Requests
Submit contributions following project's CONTRIBUTING.md.
- Push branch:
git push origin fix/<description> - Create PR using
create-pull-requestskill - Reference related issue in PR body (e.g. "Fixes #123")
- Follow project's PR template if one exists
- Be responsive to reviewer feedback — iterate quickly
Got: PRs created, linked to issues, following project conventions.
If fail: PR creation fails? Check branch protection rules and contributor license agreements.
Checks
- All selected contributions implemented and submitted as PRs
- Each PR references related issue (if one exists)
- All project tests pass on each PR branch
- No false positive findings submitted as real issues
- PR descriptions follow project's CONTRIBUTING.md template
Pitfalls
- False positive overclaim: Claw projects use sandbox architectures — "vulnerability" inside sandboxed environment may be by design. Always verify against project's threat model before reporting.
- Digest/signature chain disruption: Claw projects often use verification chains for model integrity. Changes must preserve these chains or PR will be rejected.
- Convention mismatch: Claw projects enforce strict style. Run project's own linter, not generic one. Match import ordering, docstring format, test patterns exactly.
- Scope creep: 3 focused PRs merge faster than 1 sprawling PR. Keep each contribution atomic.
- Stale fork: Always sync with upstream before starting work (
git fetch upstream && git merge upstream/main).
See Also
- security-audit-codebase — used in Step 5 for security findings
- review-codebase — used in Step 5 for code quality review
- create-pull-request — used in Step 9 for PR creation
- create-github-issues — for filing issues from findings not addressed as PRs
- manage-git-branches — branch management during implementation
- commit-changes — commit workflow
Dépôt GitHub
Compétences associées
content-collections
MétaCette compétence propose une configuration éprouvée en production pour Content Collections, un outil axé sur TypeScript qui transforme des fichiers Markdown/MDX en collections de données typées de manière sûre avec une validation Zod. Utilisez-la lors de la création de blogs, de sites de documentation ou d'applications Vite + React riches en contenu pour garantir la sécurité de typage et la validation automatique du contenu. Elle couvre tout, de la configuration du plugin Vite et de la compilation MDX à l'optimisation des déploiements et la validation des schémas.
polymarket
MétaCette compétence permet aux développeurs de créer des applications avec la plateforme de marchés prédictifs Polymarket, incluant l'intégration d'API pour le trading et les données de marché. Elle fournit également une diffusion de données en temps réel via WebSocket pour surveiller les transactions en direct et l'activité du marché. Utilisez-la pour mettre en œuvre des stratégies de trading ou pour créer des outils traitant les mises à jour de marché en direct.
creating-opencode-plugins
MétaCette compétence aide les développeurs à créer des plugins OpenCode qui s'interconnectent avec plus de 25 types d'événements tels que les commandes, les fichiers et les opérations LSP. Elle fournit la structure du plugin, les spécifications de l'API événementielle et les modèles d'implémentation pour les modules JavaScript/TypeScript. Utilisez-la lorsque vous avez besoin d'intercepter, de surveiller ou d'étendre le cycle de vie de l'assistant IA OpenCode avec une logique personnalisée pilotée par les événements.
sglang
MétaSGLang est un framework de service LLM haute performance spécialisé dans la génération rapide et structurée pour les workflows JSON, regex et agentiques grâce à son cache de préfixe RadixAttention. Il offre une inférence nettement plus rapide, particulièrement pour les tâches avec des préfixes répétés, ce qui le rend idéal pour les sorties complexes et structurées ainsi que les conversations multi-tours. Choisissez SGLang plutôt que des alternatives comme vLLM lorsque vous avez besoin d'un décodage contraint ou que vous construisez des applications avec un partage étendu de préfixes.
