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Found 1,672 Skills
The foundational context engineering skill — start here when exploring the discipline. This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Also activates when the user mentions "context engineering" or "context-engineering" for foundational understanding of AI agent context systems.
Creates new skills, either generic for the global catalog or specific to the current project. Trigger: /skill-create <name>, create skill, new skill, generate skill, add skill to project.
Decomposes a spec or architecture into buildable tasks with acceptance criteria, dependencies, and implementation order for AI agents or engineers. Produces `.agents/tasks.md`. Not for clarifying unclear requirements (use discover) or designing architecture (use system-architecture). For code quality checks after building, see review-chain. For packaging and PRs, see ship.
oh-my-agent project setup verification and configuration
Used when executing implementation plans with independent tasks in the current session
Use when dealing with 2 or more independent tasks that have no shared state or sequential dependencies
X (Twitter) data platform skill — tweet search, user lookup, follower extraction, engagement metrics, giveaway draws, monitoring, webhooks, 19 extraction tools, MCP server.
Maintain a reviewable LLM Wiki from immutable raw notes, including ingest planning, querying, linting, and guarded raw Graphify maps that help agents generate better wiki pages.
Start a Ralph Loop for iterative self-referential development. Use when the user asks to run a ralph loop, start an iterative loop, or wants repeated autonomous iteration on a task until completion.
Replace with a clear description of what this skill does and when Claude should use it. Be specific about trigger words and use cases.
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design production AI agents.
Discover and use shared team skills stored in PostHog. Use when the user asks to list, browse, load, or manage "shared skills", "team skills", or references the "skills store" / "skill store".