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Found 3,053 Skills
Read a ***plain project — its `.plain` files, `test_scripts/`, `config.yaml`(s), and `resources/` — and determine every command-line tool, runtime, package manager, and external service the project needs on the host machine. Probe the host for each one, then emit a `PASS` / `FAIL` report listing what's installed (with versions), what's missing, and concrete OS-specific install commands for the gaps. Run this any time someone is about to render, test, or onboard onto a ***plain project for the first time.
Add a concept to the ***definitions*** section of a ***plain spec file. Use when the user wants to define a new concept, entity, or domain term in a .plain file.
L2 AI-driven web UI testing for a React/Vite dashboard app. Originally authored against the Onsager Dashboard (the body's route table + file paths are Onsager-shaped); other React dashboards fork the procedure and substitute their own routes / test paths. Use when testing UI on PRs, triaging L1 test failures, or verifying UI behavior at desktop + mobile viewports. Triggers include "test the UI", "check the dashboard", "triage L1 failure", "run L2 tests", "validate this PR", "exploratory test the web app".
Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file. Use this skill to extract slice, thread, or memory data from Android Perfetto traces using trace_processor.
Multi-model deep review of the Ralph bd graph and plan via three parallel opencode processes (claude opus, gemini, gpt). Use for high-stakes runs where cross-model consensus reduces single-model bias.
Use when writing or formatting Jira descriptions, comments, or any text destined for Jira. Converts Markdown to Jira wiki markup, provides templates (bug reports, feature requests), and validates syntax before submission. Trigger on any Jira content authoring task.
Converts any Claude Code skills repository into an official plugin marketplace. Analyzes existing skills, generates .claude-plugin/marketplace.json conforming to the Anthropic spec, validates with `claude plugin validate`, tests real installation, and creates a PR to the upstream repo. Encodes hard-won anti-patterns from real marketplace development (schema traps, version semantics, description pitfalls). Use when the user mentions: marketplace, plugin support, one-click install, marketplace.json, plugin distribution, auto-update, or wants a skills repo installable via `claude plugin install`. Also trigger when the user has a skills repo and asks about packaging, distribution, or making it installable.
Use when the user asks to "improve my agent", "self-improving agent", "auto-tune my agent", "iterate on my agent prompt", "fix my agent based on test results", "close the loop on agent quality", "auto-improve agent prompt", "use eval results to improve agent", "optimize my prompt based on failures", "rewrite my prompt", or describes agent self-improvement, prompt iteration from run results, or automated agent quality loops. Covers the full diagnose → propose → apply → re-validate loop for VAPI agents (squads + tool definitions) and for self-hosted agents (custom websocket servers, including the offline / pasted-prompt degenerate variant).
Verify a WordPress plugin's Abilities API registrations: enumerate abilities, check that callback behavior matches each annotation's claim (the adversarial readonly-but-writes detection), validate permissions and schemas, and validate audit documents produced by wp-abilities-audit.
Optional AI SDLC research workflow. Use when an AI assistant needs to investigate a customer, market, domain, technology, regulation, competitor, operational question, or implementation uncertainty and produce a routed source inventory plus synthesized findings with confidence, limitations, open questions, and delivery trace targets. Supports `--quick-flow` for focused evidence and `--full-flow` for multi-source and source-diversity gates.
Turns ideas into working prototypes and MVPs with the smallest useful scope. Use when validating a core hypothesis, shipping a demo-ready flow, or choosing a practical stack for fast learning.
Post-implementation plan verification. Cross-references plans against actual changes for completeness and accuracy.