Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Author, edit, or iterate on prompts in the Phoenix prompt playground, including running experiments over a dataset. Load before any playground tool call, including single-shot prompt rewrites.
Spin up a single agent as an advisor — second opinion on the current task. Use when the user says "advisor", "second opinion", "what does X think", or wants an outside take without delegating the work itself.
First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.
Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo.
Use when an app developer wants to build an entity (a.k.a. an agent) for their Electric Agents app — designing a single entity type, picking a coordination pattern when needed (single-agent, manager-worker, pipeline, map-reduce, dispatcher, blackboard, reactive-observer), defining state, handler, schemas, and implementing it in one entity file. Applies to any use of `registry.define(...)` / `defineEntity(...)` in a `@electric-ax/agents-runtime` app.
Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.
QuantDinger repo workflow for coding agents: layered contracts, safety boundaries, and where backend, strategies, and Docker live. Use when editing Python API, strategies, deployment, or docs/agent.
Use this if the user wants to update the agent instructions for the Home Assistant website.
Run the PinchTab optimization loop (Docker, 3 blind subagents on the runner's HIGH model, 108 steps across 47 groups) against chrome, cloak, ghost-chrome, or all three providers. Pass `setup` (optionally followed by a provider or `all`) to run only the setup test (native binary, single subagent forced to the runner's LOW model) that validates the fresh-install OOTB flow per provider. Use when asked to 'run optimization', 'run the opt loop', 'benchmark the agent', '/pinchtab-opt', '/pinchtab-opt cloak', '/pinchtab-opt ghost-chrome', '/pinchtab-opt setup', '/pinchtab-opt setup all', or 'test pinchtab agent'.
Review recent conversations to find improvements for CLAUDE.md files.