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Found 565 Skills
Fix issues in MTHDS bundles. Use when user says "fix this workflow", "fix this method", "repair validation errors", "the pipeline is broken", "fix the .mthds file", after /check found issues, or when validation reports errors. Automatically applies fixes and re-validates in a loop.
Run MTHDS methods and interpret results. Use when user says "run this pipeline", "execute the workflow", "execute the method", "test this .mthds file", "try it out", "see the output", "dry run", or wants to execute any MTHDS method bundle and see its output.
Explain and document MTHDS bundles. Use when user says "what does this pipeline do?", "explain this workflow", "explain this method", "walk me through this .mthds file", "describe the flow", "document this pipeline", "how does this work?", or wants to understand an existing MTHDS method bundle.
TanStack Router bundler plugin for route generation and automatic code splitting. Supports Vite, Webpack, Rspack, and esbuild. Configures autoCodeSplitting, routesDirectory, target framework, and code split groupings.
Dynamically load components based on the current route to reduce initial bundle size.
Use when the user wants to initialize, switch, inspect, optimize, export, or diagnose a role bundle with /roleMe.
Autonomous experiment loop for optimization research. Use when the user wants to: - Optimize a metric through systematic experimentation (ML training loss, test speed, bundle size, build time, etc.) - Run an automated research loop: try an idea, measure it, keep improvements, revert regressions, repeat - Set up autoresearch for any codebase with a measurable optimization target Implements the autoresearch pattern with MAD-based confidence scoring, git branch isolation, and structured experiment logging.
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or audit such a base, or initialize a new one. Trigger on phrases like "add this to my wiki", "ingest this paper", "compile this into the knowledge base", "what does my wiki say about X", "lint the wiki", "build a knowledge base from these documents", "research notes", "second brain", "personal knowledge base", or any reference to LLM Wiki / OmegaWiki. Trigger even when the user does not say "wiki" — if they are accumulating sources over time and want them organized, this applies. The skill scales — sharded indexes, atomic pages, YAML frontmatter, and a bundled search script keep the wiki from becoming a context bottleneck at hundreds or thousands of pages.
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.
Scaffold or audit the memex (vault + AGENTS.md + spec templates + bundled skills) in any repo — an externalized, navigable project memory for agents (Claude Code, Codex, Cursor, OpenCode, etc.). Agent-agnostic. Idempotent — safe to run repeatedly. Use when the user wants to set up, verify, or fix the memex in a project.
Verify a released archon binary works end-to-end via a specific install path. Use when: cutting a new release, reproducing a user bug report on the released version, or validating that a hotfix binary actually works after a re-tag. Triggers: "test the release", "test 0.3.1 via brew", "verify the curl install", "smoke test the binary", "did the release binary work", "run /test-release", "verify the release". NOT for: testing dev work (use bun link directly), testing unreleased changes (build locally via scripts/build-binaries.sh first), or running the full validate suite (bun run validate is separate).
Specification and SDK for creating Agent Skills - a standard format for giving AI agents new capabilities through discoverable instruction bundles