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Found 13,632 Skills
Create code-based evaluators for LangSmith-traced agents with step-by-step collaborative guidance through inspection, evaluation logic, and testing.
Use when implementing RL algorithms, training agents with rewards, or aligning LLMs with human feedback - covers policy gradients, PPO, Q-learning, RLHF, and GRPOUse when ", " mentioned.
Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"
Execute deep web research on any topic using Yutori's research agents. Use for competitive analysis, market research, finding documentation, or answering complex questions that require synthesizing information from multiple sources.
Use when implementing agent memory, persisting state across sessions, building knowledge graphs, tracking entities, or asking about "agent memory", "knowledge graph", "entity memory", "vector stores", "temporal knowledge", "cross-session persistence"
Canonical Claude Code authoring kit covering Skills, sub-agents, plugins, slash commands, hooks, memory, settings, sandboxing, headless mode, and advanced agent patterns. Use when creating Claude Code extensions or configuring Claude Code features.
Claude Code config optimization skill. Use when: - Editing CLAUDE.md, rules/, skills/, agents/, commands/ - User asks about config best practices - Checking optimization status - User says "claude code changelog" or "claude code updates" - User asks about new features or breaking changes in Claude Code
Load PROACTIVELY when decomposing a user request into parallel agent work. Use when user says "build this", "implement this feature", or any request requiring multiple agents working concurrently. Guides task decomposition into parallelizable units, agent assignment with skill matching, dependency graph construction, WRFC loop coordination across up to 6 concurrent agent chains, and result aggregation.
Initialize a repository for ASDLC adoption with AGENTS.md and directory structure
Orchestrate multiple Antigravity skills through guided workflows for SaaS MVP delivery, security audits, AI agent builds, and browser QA.
Safe experimentation framework for AI agents. Creates isolated sandbox environments for trying new features, testing approaches, and exploring solutions without polluting the main codebase. USE WHEN: Agent needs to try something uncertain, explore multiple approaches, test a new library, prototype a feature, or run a technical spike before committing to implementation. PRIMARY TRIGGERS: "experiment with" = Setup sandbox + run experiment "try this approach" = Quick experiment in sandbox "spike" / "POC" / "prototype" = Time-boxed technical investigation "tinker" / "tinkering mode" = Enter experimentation workflow "explore options" = Multi-approach comparison in sandbox NOT FOR: Debugging (use debugger), testing (use test runner), or committed feature work (use git branches). DIFFERENTIATOR: Unlike git branches (for committed direction), tinkering is for "I don't know if this will work" exploration. Try 5 things in sandbox before committing to a branch. Faster feedback, zero codebase pollution.
Consolidate agent output results into structured final output, supporting multiple formats. Suitable for consolidating analysis reports and generating structured reports