Claude Skills by yeaight7

Use when you have a spec or requirements for a multi-step task, before touching code.

Use when writing technical documentation that needs to be readable by both humans and AI models, converting existing docs to HADS format, validating a HADS document, or optimizing documentation for token-efficient AI consumption.

Verify that API endpoints match their OpenAPI/Swagger specifications.

Record why an architectural choice was made to prevent agents or humans from unintentionally reverting it.

Maintain short, focused Markdown files per subsystem to provide agents with isolated context.

Audit documentation for broken file paths, outdated commands, and renamed variables.

Ensure the project README provides immediate, exact commands for setup, testing, and deployment to help agents and humans bootstrap quickly.

Use when mining PR review comments and text diffs for reusable writing, documentation, tone, and editorial improvement patterns.

Address actionable GitHub pull request review feedback. Use when the user wants to inspect unresolved review threads, requested changes, or inline review comments on a PR, then implement selected fixes.

Inspect GitHub PR checks, fetch actionable failure logs, summarize the breakage, and propose a local fix plan before changing code.

Run a review and CI loop around a pull request with explicit approval gates for code changes, remote writes, and follow-up actions.

Ensure that new complex models actually outperform simple, naive baselines.

Audit the methodology used to split data into train, validation, and test sets.

Verify that all hyperparameters, metrics, and data references are properly logged.

Identify and prevent target leakage in ML preprocessing pipelines.

Standardize the reporting of model metrics to ensure statistical rigor and business relevance.

Analyzes ML training scripts to enforce seed setting, deterministic operations, and environment tracking for exact reproducibility.

Diagnose NaN losses, out-of-memory errors, and shape mismatches in deep learning or ML pipelines.

Design high-quality MCP servers around workflows, narrow schemas, context-aware outputs, and actionable errors. Use when building or reviewing MCP tools for real agent tasks.

Use when the user provides a URL to a standard web page and clean Markdown extraction with Defuddle would reduce clutter and token cost.

any input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report. Use when the user wants to build, inspect, or query a cross-file knowledge graph from code, docs, papers, images, or videos with upstream graphify.

Use when the user provides a MarkItDown-supported file or URL and converting it to Markdown first will make inspection easier, cheaper, or more reliable.

Use when a user needs to build or refresh persistent graph memory from a mixed corpus and the right path may include graphify, incremental update, or helper conversion before ingestion.

Use when deciding the lowest-cost context path for a mixed corpus, especially when choosing among direct reading, helper conversion, graph build, graph update, or graph query.

Use when a graph already exists and the user needs retrieval, tracing, explanation, or gap detection from graph memory before reopening the full corpus.

Deterministic checks first, agent review second, regression test for every real bug fixed or document why not. Targets the blind spot where an agent writes and reviews its own code.

Use before submitting a PR or considering a task done to evaluate the 'blast radius' of your changes.

Use when a CI pipeline fails to extract the actual error from thousands of lines of logs.

Use before tagging a release or deploying to production to ensure all quality gates have passed.

Use when receiving code review feedback, before implementing suggestions — requires technical verification and reasoned pushback, not performative agreement or blind implementation.

Use when completing tasks, implementing major features, or before merging to verify work meets requirements.

Use when reviewing code (or your own plan) to allocate attention based on the danger of the change.

Use when implementing any feature or bugfix, before writing implementation code

Audit agent configuration files for security vulnerabilities and misconfigurations. Covers settings.json, .mcp.json, .codex/config.toml, AGENTS.md, hooks, plugin manifests, and relay config. Classify findings as P0 or P1.

Use when designing or reviewing filesystem MCP access, path boundaries, allowed roots, method allowlists, and safe local file operations.

Extract a learned skill from the current conversation manually

Create or refactor high-quality skills with lean frontmatter, progressive disclosure, and optional bundled helpers. Use when authoring reusable agent workflows.

Full autonomous execution from idea to working code

Run large codebase migrations in reviewable local batches with codemods, checkpoints, and verification. Use when a wide refactor is too risky to ship as one monolithic change.

Run independent work in parallel with explicit wave boundaries, lightweight coordination, and bounded verification. Use when latency matters and the work does not need a persistent completion loop.

Self-referential loop until task completion with configurable verification reviewer

Coordinate multiple subagents through dependency-aware waves, stage handoffs, monitoring, and verification loops. Use when the work is too large or too mixed for a single agent.

Turn vague implementation requests into testable requirements before coding.

Use when validating rendered web pages, local dev servers, browser automation, screenshots, forms, auth sessions, or UI evidence with strict browser safety boundaries.

Use when a Codex or generic local agent needs a focused second opinion from Claude CLI without MCP routing.

Use when a Claude Code agent needs to delegate a question, subtask, or review to a local Codex instance.

Use when a Codex or generic local agent needs a focused second opinion from Gemini CLI without MCP routing.

Fix broken builds, type errors, or test failures with the smallest safe diff — no opportunistic refactors, no cleanup, no unrelated changes.

Turn commits, PRs, or diffs into user-facing release notes or an internal changelog, with modes for breaking-change scanning.

Diagnose local agent and project setup problems — missing commands, broken MCP configs, stale generated assets, bad paths, invalid catalog entries, and missing env vars.