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Found 711 Skills
This skill should be used when the user asks to "create a skill", "build a skill", "write a skill", "improve skill structure", "understand skill creation", or mentions SKILL.md files, skill development, progressive disclosure, XML structure, or bundled resources (scripts, references, assets). Comprehensive guide for creating effective Claude Code skills.
Build new AI method from scratch using the MTHDS standard (.mthds bundle files). Use when user says "create a pipeline", "build a workflow", "new .mthds file", "make a method", "design a pipe", or wants to create any new method from scratch. Guides the user through a 10-phase construction process.
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.
Edit existing MTHDS bundles (.mthds files). Use when user says "change this pipe", "update the prompt", "rename this concept", "add a step", "remove this pipe", "modify the workflow", "modify the method", "refactor this pipeline", or wants any modification to an existing .mthds file. Supports automatic mode for clear changes and interactive mode for complex modifications.
Manage regulatory requirements, number bundles, supporting documents, and verified numbers for compliance. This skill provides Java SDK examples.
Canonical ticket lifecycle engine for multi-agent orchestration. Two backends: (1) filesystem YAML bundles for project-level work management (roadmap → bundle → tickets → review), (2) DB-backed durable tickets for session-level claim/block/close lifecycle. This skill is the single source of truth for all ticket operations.
Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets. Use when you have a structured input bundle and want ranked hypothesis cards with experiment designs, kill criteria, and optional strategy.yaml export compatible with edge-finder-candidate/v1.
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.
Use when user asks about 1 Staat, State - Constitutional Law, Bundesverfassung, BV, constitutional, Bürgerrecht, citizenship, Staatsrecht, Behörden, authorities, Parlament, Bundesrat, Bundesgericht, SR 1xx. Covers SR category 1 of the Systematische Rechtssammlung.
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.
macOS screen capture, window recording, GIF conversion, and agent evidence bundles from the terminal. Built on ScreenCaptureKit for window-level targeting ffmpeg cannot do. Use when the user wants a screenshot of a specific window or app, a screen recording, a GIF conversion, a before/after diff, an evidence bundle for a PR, OCR text from a window, a terminal VHS recording, a Remotion render, or wants to watch a UI for changes. Requires macOS Screen Recording permission on first run.