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Found 2,174 Skills
Use whenever the user mentions LLM prompt/prefix cache misses, cached_tokens=0, cache_read_input_tokens/cache_creation_input_tokens, prompt_cache_key, cache_control/cachePoint placement, stable prefixes, tool/schema stability, TTFT/prefill latency, OpenAI/Claude/Bedrock/OpenRouter routing, vLLM/SGLang KV reuse, or LLM cost/speed regressions on repeated long prompts. Use when reviewing LLM request shape changes: prompt text, message order, request builders, tools, schemas, response_format, provider API surface, model/router settings, agent loop structure, context compaction, or inference deployment. Use for speeding up agents only when prompt-cache stability, TTFT, or cache cost is central. Do not use for generic prompt writing, generic RAG design, token counting, or non-LLM performance.
This skill should be used when the user asks to "fix my skill" or "audit this skill". Make sure to use this skill whenever the user mentions skill quality, structural issues, broken skills, or skill diagnostics — even if they don't explicitly say "repair-skill". Not for adding features or improving effectiveness — use improve-skill. Not for agents — use repair-agent.
Audit a WordPress plugin's REST surface and produce a standardized audit document proposing Abilities API registrations. Produces a markdown doc with a YAML schema and prose sections that humans and agents can both consume when planning a registration rollout. Works on any WP plugin.
Configure and use ktx to build an executable context layer for AI agents querying data warehouses with semantic layers, wiki knowledge, and approved metrics
Context layer for AI data agents - query warehouses accurately with semantic layers, metrics, and wiki knowledge through MCP
Context layer for data agents - builds semantic layer, wiki, and warehouse metadata to enable accurate AI-powered analytics queries
Build hierarchical memory systems for AI agents using GAM (General Agentic Memory) with text, video, and long-horizon trajectory support
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.
A shared, file-based town square where multiple coding agents talk, coordinate, and debate — no server required. Use whenever more than one agent works the same repo (parallel Claude Code or Codex sessions, separate git worktrees, a fleet splitting a task) and they must stay out of each other's way or think together. TRIGGER on phrasings like "coordinate with the other agent/session", "post to / check the agora", "ask the other agents", "leave a message for whoever's working on X", "announce what files you're touching", "is anyone else editing this?", or any time you're about to edit shared code while other agents are live. Also trigger when an agent is stuck and wants a peer's second opinion, or when several agents each drafted a design (an API, a schema, an architecture) and the group needs to compare the proposals and converge on the best one. Works for any agent that can run a Python script, not just Claude Code.
Use when the user asks to research a topic in depth, map a competitive/market landscape, run a multi-source investigation, or "fan out" parallel research agents — anything where many findings must be gathered and then NOT lost. Enforces durable, detail-preserving research (write full findings to disk; keep a full appendix beside the synthesis).
Run /sync as the last step after a change is complete, around merge, to keep durable knowledge current. Updates root and nested AGENTS.md, reconciles the scope from repo evidence, and flags specs the change made stale. Surgical edits only: it adds lines, and rewrites single lines it owns. Never a whole section, never curated prose.
Develop a Base44 app remotely inside Base44's cloud sandbox using your own agent — no local checkout and no deploy/push commands. The implementation is remote: writing a resource file into the sandbox is what ships it (backend functions, entities, and agents all auto-sync from the file you write), and OAuth connectors are set up against the remote app via MCP tools or the projectless `base44 connectors` CLI. This skill is the place for learning what you can author in the sandbox, how backend functions, entities, and agents are structured, and how to connect a connector without a local filesystem. Triggers on 'develop my Base44 app remotely', 'no local files', 'cloud sandbox', 'create an entity/agent remotely', 'connect a connector remotely', 'bring my own agent', or any work editing a Base44 app inside a sandbox.