Loading...
Loading...
Found 11,972 Skills
Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.
Inspect Claude Code session logs, tool calls, token usage, subagents, and context window using claude-devtools visual UI
Connect Jira, Confluence, and Compass to AI agents and IDEs using Atlassian's remote MCP server with OAuth 2.1 or API token authentication.
Team composition for writing workflows: which agents to spawn, how many, what focus areas to assign, and how to scale effort. Use when composing critic panels, dispatching researchers, staffing draft/revise loops, or setting up brainstorm fan-outs.
Discover feature areas in the current repository that are not yet documented under the agent docs `features/` tree (scaffolded by `setup-agentic-repository` — `agents-docs/features/` by default, or wherever `--docs-dir` put it), then create populated feature docs from the canonical template. Use whenever the user wants to find undocumented features, fill out `features/`, catch up on missing feature documentation, document feature X/Y/Z, or mentions "find features". This is the natural follow-up to `setup-agentic-repository`, which scaffolds the empty `features/` tree this skill populates.
Populate `<docs-dir>/features/<slug>.md` for one, several, or every undocumented feature area by dispatching up to 10 parallel subagents — one per feature. The agent docs directory is discovered from `AGENTS.md` — typically `agents-docs/` (the `setup-agentic-repository` default) but may be elsewhere if `--docs-dir` was used. Use whenever the user wants to document features, fill out feature docs, write up specific features (e.g. "document auth and billing"), document all undocumented features, or follow up on `find-features` discovery. This is the natural sequel to `find-features` — that skill identifies what is missing, this skill writes the docs in parallel.
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
Using the Pi terminal agent — workspace setup, sessions, /commands, compaction, settings.json/AGENTS.md, skill discovery, providers/models, plus theme/keybinding/prompt customization (SYSTEM.md, APPEND_SYSTEM.md, settings.json, keybindings.json). Use for any "how do I configure/run Pi" question.
Use this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured outputs with Pydantic models, wiring up tools/function calling, configuring model providers (OpenAI, Anthropic, Gemini, etc.), managing dependencies via agent context, handling streaming responses, or debugging agent runs. Trigger this skill even for adjacent tasks like "how do I make my agent return JSON", "set up a multi-step agent", "add a tool to my agent", or "validate LLM output with Pydantic" — any time Pydantic AI is mentioned or implied as the target framework.
Use when the user says "get started with Cekura", "set up Cekura", "onboard to Cekura", "I'm new to Cekura", "help me set up my agent", "how do I use Cekura", "walk me through Cekura", "configure my project", "first time using Cekura", or needs guidance on initial platform setup. Covers two onboarding paths: **testing** (default — build evaluators and run simulated calls) and **observability** (ingest production call logs and evaluate them).
Use when the user asks to "create a metric", "write a metric", "design a metric", "build a metric for", "evaluate agent performance", "measure call quality", "track a KPI", "add a workflow metric", "improve my metric", "fix a metric", "debug metric results", "set up quality scoring", or "what metrics do I need". Also relevant when discussing LLM judge prompts, custom code metrics, evaluation triggers, VALID_SKIP patterns, section extraction, or metric best practices for Cekura voice AI agents. Covers both creating new metrics and reviewing, iterating on, or troubleshooting existing ones.
This skill should be used to summarize coaching or therapy session transcripts after a Fathom/Granola sync. The agent analyzes the transcript itself (no API key, runs on the subscription) and appends key insights, decisions, action items, and trail connections. Supports quick extraction or deep analysis with cross-session pattern detection.