Total 56,488 skills, AI & Machine Learning has 9401 skills
Showing 12 of 9401 skills
Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.
Runtime patch for Claude Code that unlocks hidden features, removes restrictions, and enables advanced capabilities like multi-agent swarms and computer use.
Cohere integration. Manage Documents, Models, Datasets, Jobs. Use when the user wants to interact with Cohere data.
Validate, audit, and fix agent skills for agentskills.io spec compliance. Use when creating a new skill structure, auditing an existing skill against the specification, fixing common spec deviations, or reviewing frontmatter, directory layout, progressive disclosure, or script interfaces. Triggers on "validate skill", "audit skill", "spec compliance", "fix skill structure", "skill frontmatter", "SKILL.md format", or "agent skills spec".
Diagnose ComfyUI errors, workflow failures, and quality issues. Suggests fixes based on error patterns, missing dependencies, and community-known workarounds. Use when ComfyUI workflows fail or produce unexpected results.
Progressive Domain Crystallization (PDC) — a skill for building and maintaining a living domain knowledge base for any custom business application. Use this skill whenever the user is developing a business application and wants the AI to accumulate understanding of internal terminology, entities, relationships, and business rules over time — especially when that knowledge is not fully defined upfront and grows across sessions. Trigger on any of: "remember how our system works", "learn our domain", "track business entities", "build domain knowledge", "understand our terminology", "grow AI context over time", "domain model", "business rules documentation", or whenever a user says the AI doesn't understand their business-specific language or data model. Also use at the start of any session where a DOMAIN.md file exists in the project — always read it before doing any work.
Reading coach: guides users through books systematically with knowledge compilation, mastery testing, spaced repetition, and knowledge querying. Use when user says 'read this book with me', 'book study', 'start studying X', 'reading plan', 'ingest this chapter', 'review what I read', 'quiz me on the book', 'what did the book say about X', or invokes /book-study. Supports sub-commands: ingest, query, review, compare, status. Triggers: book, study, read, chapter, ingest, review, quiz, reading plan, book notes.
Run structured multi-agent debates using argue CLI for cross-examined, high-confidence answers. Use when facing strategic decisions, ambiguous trade-offs, architecture debates, or questions where multiple perspectives improve the answer. Triggers on: argue, debate, cross-examine, second opinion, multi-agent, 'Should we X or Y?' with real stakes, consensus-building, risk analysis, or confirmation-bias mitigation.
Use this skill whenever calling agent-uml MCP tools (design_create, diagram_upsert, design_feedback, design_export) to render PlantUML diagrams on the collaborative canvas. Covers three tiers — rendering safety (syntax that prevents HTTP 400 blank canvas), conversation mechanics (when to push a version vs ask a question, what to write in the message parameter), and design effectiveness (decomposition thresholds, cross-diagram traceability, export readiness). Trigger even when the task seems simple — a missing `as alias` makes elements un-annotatable, and a skinparam mismatch makes diagrams unreadable on the warm
Manage Jetty workflows and assets. Use when the user wants to create, edit, run, deploy, debug, or monitor AI/ML workflows on Jetty. Also use when they mention collections, tasks, trajectories, datasets, models, labels, step templates, or workflow runs. Triggers include 'run workflow', 'create task', 'list collections', 'check trajectory', 'label trajectory', 'add label', 'deploy workflow', 'show results', 'download output', 'debug run', 'workflow failed', or any Jetty/mise/dock operations. Even if the user doesn't say 'Jetty' explicitly, use this skill whenever they're working with Jetty API endpoints, workflow JSON, or init_params.
This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of maximizing information density within token constraints.
Design hybrid recommendation systems combining multiple strategies for improved accuracy. Use this skill when the user needs to overcome single-method limitations, combine collaborative and content-based filtering, or build a production recommendation pipeline — even if they say 'combine recommendation approaches', 'best recommendation architecture', or 'cold start plus personalization'.