924건 · 관련도순
Autonomous tech-debt reduction workflow for the Humans repository. Use when the user wants Codex to improve high-value code quality issues in a dedicated git worktree branched from origin/main, commit each improvement separately, push progress, and continue until diminishing returns.
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Daily per-section review cycle driving a section toward the smallest, clearest form that still does everything it does today. Selects its section live each run (scripted), inventories every file, derives the target shape before any scan, works parallel reading threads into one ranked list and strikes it on a 2-3h budget. One PR per run; each run's report and Needs-Peter queue live in its own docs/health/runs/ file; 'resume' applies Peter's answers later. Use for the morning section-improvement run, 'doctor <section>', or 'run section doctor'.
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Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.
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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design AI feedback loops for continuous improvement. TRIGGERS - Use when user needs help with ai-feedback-loop related tasks.
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Design AI benchmarks for comparing models and tracking improvement. TRIGGERS - Use when user needs help with ai-benchmark-creator related tasks.
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Transform review analysis into constructive feedback with actionable improvements. Mentor/shepherd mode - strict but supportive. Use for "give constructive feedback", "shepherd this paper". 旧名 review-shepherd。
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Audit web pages for performance optimization following PageSpeed Insights guidelines. Use when analyzing page performance, optimizing web applications, reviewing performance metrics, implementing Core Web Vitals improvements, or when the user mentions page speed, performance optimization, Lighthouse scores, or Core Web Vitals.
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Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
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Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
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Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Improve code structure, readability, and maintainability without changing external behavior through systematic refactoring techniques like extracting functions, removing duplication, simplifying conditionals, and applying design patterns. Use when reducing technical debt, extracting functions or classes, removing code duplication, simplifying complex conditionals, renaming for clarity, applying design patterns, improving code organization, reducing coupling, increasing cohesion, or maintaining test coverage during structural improvements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a fast, read-only health check across all skills in the library and produce a structured quality report — without modifying anything. Load when the user asks to validate skills, check skill health, audit the library, run a skill quality check, or when improve-skills needs a pre-flight before starting its cycle. Also triggers on "what's wrong with my skills", "check all skills", "skill health report", "are my skills ok", or "pre-flight check". Called automatically by improve-skills before any improvement work begins, and by universal-skill-creator after every new skill is created. Never modifies any file — only reads and reports.
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Detect and resolve naming collisions, overlapping intent triggers, and insufficient intent diversity across the skill library. Load when a new skill is created (called by universal-skill-creator), during improvement passes (called by improve-skills), or when the user asks to deconflict skills, check for naming collisions, audit trigger overlap, review intent coverage, find duplicate triggers, check skill names, or says "are any skills too similar", "which skills overlap", "deconflict the library", "check for confusing skill names", "intent audit".
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Audit, improve, and compress every skill in the repo using live research. Load when the user asks to improve skills, audit the skill library, upgrade existing skills, refresh with new research, do a skill health check, or says "improve all skills", "update the skill library", "skill audit", or "run an improvement pass". Applies live domain research, fixes structural gaps, checks for skill linking opportunities, then rewrites and resizes each skill. Supports TARGETED mode (`TARGET=<skill> [SKIP_RESEARCH=true]`) for single-skill fixes, including learn-from-chat restructure escalations. All skills are in scope including meta skills.
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Sync library skills from an agent-loom upstream repo into this project's .agents/skills while preserving project-local and forked skills. Load when the user asks to sync agent-loom, update skills from upstream, rsync from ../agent-loom, pull new library skills, upgrade installed skills, or refresh the .agents folder without losing custom project skills. Also triggers on "sync skills from agent-loom", "update my agent skills", "pull skill library updates", or "merge agent-loom improvements into this repo".
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Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).
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Analyzes your recent Codex chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.