Loading...
Loading...
Found 15 Skills
Use when assessing whether a repo is ready for autonomous AI agents (pylot workers) — produces a scored, actionable Agent Readiness Report as a GitHub issue in the assessed repo.
Audit and build the infrastructure a repo needs so agents can work autonomously — boot scripts, smoke tests, CI/CD gates, dev environment setup, observability, and isolation. Use when a repo can't boot, tests are broken or missing, there's no dev environment, agents can't verify their work, or agents need human help to get anything done. Do not use for reviewing an existing diff or for documentation-only cleanup.
Assesses and improves a git repository's readiness for AI coding agents with the agentready tool, and reports the score, the certification level, the failing findings, and where the report was written. Covers one repository or every RHDH repository under a directory, and applies the fixes the report supports. Use for "assess agent readiness", "run agentready", "improve our agent readiness score", "prepare this repository for coding agents", or "assess all the RHDH repositories".
Assess how ready a codebase is for autonomous AI coding agents — by delegating a deep investigation of each of Factory.ai's 9 readiness pillars (~80 criteria) to a dedicated subagent, then cross-referencing their reports into a pass-rate + maturity level with a prioritized, concrete fix list. Use when asked to "check agent readiness", "is this repo agent-ready", "readiness report/score", "/readiness", "how well does this repo support AI agents", or to audit a repo's dev environment for agent autonomy.
Assess a codebase's readiness for autonomous agent development and provide tailored recommendations. Use when asked to evaluate how well a project supports unattended agent execution, assess development practices for agent autonomy, audit infrastructure for agent reliability, or improve a codebase for autonomous agent workflows. Triggers on requests like "assess this project for agent readiness", "how autonomous-ready is this codebase", "evaluate agent infrastructure", or "improve development practices for agents".
Update repo documentation and agent-facing guidance such as AGENTS.md, README.md, docs/, specs, plans, and runbooks. Use when code, skill, or infrastructure changes risk doc drift or when documentation needs cleanup or restructuring. Do not use for code review, runtime verification, or `agent-readiness` setup.
Turn a completed experiment iteration into an honest, evidence-backed analysis — a markdown report and a portable data dump. Pulls run data via the tpc CLI, scores each task, clusters friction by root cause (with a transcript example per claim), compares arms, and closes on agent-readiness gaps. The natural companion to setup-experiment: setup → run → analyze. Trigger when users say: "analyze my experiment", "write the report", "experiment report", "analyze the results", "summarize the runs", "what happened in this iteration", "friction report", or "report gen".
Verify your own completed code changes using the repo's existing infrastructure and an independent evaluator context. Use after implementing a change when you need to run unit or integration tests, check build or lint gates, prove the real surface works with evidence, and challenge the changed code for clarity, deduplication, and maintainability. If the repo is not verifiable yet, hand off to `agent-readiness`; if you are reviewing someone else's code, use `review`.
Create and maintain Architecture Decision Records (ADRs) optimized for agentic coding workflows. Use when you need to propose, write, update, accept/reject, deprecate, or supersede an ADR; bootstrap an adr folder and index; consult existing ADRs before implementing changes; or enforce ADR conventions. This skill uses Socratic questioning to capture intent before drafting, and validates output against an agent-readiness checklist.
Build and use the verification infrastructure coding agents need to prove their work. Use when: a repo has no bootable dev environment, no real-surface tests, or no interaction layer an agent can use; auditing or grading a repo's agent-readiness; verifying changes work end to end on real surfaces; or when harness gaps block reliable agent output.
Evaluate how well a codebase supports autonomous AI development. Analyzes repositories across eight technical pillars (Style & Validation, Build System, Testing, Documentation, Dev Environment, Debugging & Observability, Security, Task Discovery) and five maturity levels. Use when users request `/readiness-report` or want to assess agent readiness, codebase maturity, or identify gaps preventing effective AI-assisted development.
Use this skill to design an OpenAPI spec from scratch, assess an existing spec for AI agent readiness, security, or design quality, or fix issues found in a spec. Trigger when the user describes an API they want to build, asks to "design", "create", "draft", or "scaffold" an OpenAPI spec, or mentions building a REST API for a service or domain. Trigger when the user says things like "I want to expose endpoints for X", "help me design an API for Y", or "I need an OpenAPI spec for Z" — even without saying "OpenAPI" explicitly. Trigger when the user asks to evaluate, review, check, or assess an OpenAPI spec for agent compatibility, API quality, security, OWASP compliance, WSO2 guidelines, or REST best practices — or when they share a .yaml/.json OpenAPI file and ask how good it is. Trigger when the user asks to fix, correct, remediate, or apply fixes to issues in an OpenAPI spec — including "fix issue spec-001", "fix all HIGH severity issues", "apply autoFixable fixes", or "fix the spec issues from this report".