By bjcoombs
Assess and improve your codebase's AI-readiness with layered scoring and complexity visualizations, run structured multi-perspective analyses, and automate GitHub workflows like PR fixing, issue triage, and bulk repo sync.
> Thin orchestrator. Triages open issues, then delegates execution of `agent-ready` issues
Enter autonomous PR review loop for the current branch's PR. Loops until the PR is merge-ready across ALL criteria, not just CI.
Six Thinking Hats Analysis
Slash commands shipped by the plugin. Portable framework commands work in any Claude Code session; the workflow commands embed one author's personal setup and are opt-in - see [Adapting for your workflow](../README.md#adapting-for-your-workflow) before relying on them. Back to the [Map of Content](../docs/index.md).
Assess failing CI on the repo's default branch (main / develop / etc., or nightly build), create a worktree with a fix, push a PR, and loop until CI passes and review comments are addressed.
Creates clear, actionable documentation from synthesized analysis. Invisible framework.
Objective fact-finder who finds code first, theories second. Verifies claims with evidence.
Identifies benefits, opportunities, and value across the full spectrum - from simple wins to bold strategic plays.
Subagent definitions invoked by the skills, or directly via `Task(subagent_type=...)`. Each file carries the frontmatter contract (`name`, `description`, `model`, `color`) documented in [`CLAUDE.md`](../CLAUDE.md). Back to the [Map of Content](../docs/index.md).
Scores a codebase against the /assess 0-8 layered contract model, reading the deterministic run-context.json and assigning Present/Partial/Missing per layer with evidence.
Compare two versions of an LLM-directed document - an original (teacher) and a candidate (student) - across a transfer set and return a per-case behavioural-equivalence verdict plus an efficiency signal. A transform-agnostic library capability other skills compose to gate a transform on behavioural sameness. TRIGGER when asked to A/B test two versions of a prompt / instruction / skill, to check whether a rewritten or compressed document still behaves the same as the original, to validate behavioural equivalence between two document versions, to gate a transform on no-regression, or when a skill needs the run-the-runner-on-both-versions-and-judge-equivalence capability.
Renders the /assess report from the deterministic run-context.json and the layer scorecard - the scorecard, the verbatim cross-layer findings, lying signals, and the mandatory Top 3 Actions. TRIGGER when the /assess orchestrator reaches the report-writing step; not a standalone user command.
The /assess end-of-run offers - open a PR with the report, track the Top 3 Actions in the user's issue tracker, freeze the assessment into a CI gate, and file tool feedback. TRIGGER when the /assess orchestrator reaches the end-of-run offers; not a standalone user command.
Assess a codebase's readiness for AI agent contributors using the layered contract model, and generate a complexity hotspot SVG treemap (size = LOC, hue = cyclomatic complexity, saturation = recent git churn). TRIGGER when the user types /assess, asks for an AI-readiness review, wants a complexity heatmap or hotspot map, asks 'how complex is this code?', wants migration risk triage, or asks for a codebase snapshot/report. Produces an MD report + SVG that can be opened as a PR in the target repo.
Detect and remove the telltale signs of AI-generated 'slop' from any written text - articles, reports, emails, essays, bios, marketing copy, documentation, encyclopedia entries, or anything meant to read as if a thoughtful human wrote it. Apply silently as a quality gate before finalizing substantial prose, and explicitly when asked to clean a draft. TRIGGER when the user says 'make this sound less like AI', 'remove the AI tells', 'de-slop this', 'check if this reads as AI-written', 'make it sound human', 'edit out the ChatGPT voice', or critiques a draft as generic, puffy, or robotic. Based on Wikipedia's 'Signs of AI writing' field guide.
Uses power tools
Uses Bash, Write, or Edit tools
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A Claude Code plugin - and a set of standalone skills for any AI assistant: skills, agents, and commands for AI-native development. In Claude Code it runs locally against your own codebase using whichever model you already pay for. Several of the skills also ship as standalone Agent Skills ZIPs you can upload to claude.ai, Claude Desktop, Cowork, or any assistant that supports the skills format - no Claude Code required.
Want the skills without Claude Code? Download the ZIPs from the latest release - the release notes link straight to that version's standalone skill bundle - and upload them in your assistant's Skills UI. Full walkthrough: Standalone skill ZIPs. Currently standalone:
/assess,/huddle,/deslop,/skill-forge,/semantic-compress.
New here? The Map of Content is the navigation index - one trail to every skill, command, agent, and design doc in this repo. The
CLAUDE.mdcontract holds the rules for editing it.
Here from the GitHub Marketplace? You found the AI-Readiness Assess Gate - the CI-gate half of this toolkit. It runs the same deterministic engine the
/assessskill uses (complexity treemap, promissory-marker scan, doc-graph signals - zero AI tokens) on every pull request and gates on what your.assess/config.tomlopts into. Jump straight to Use as a GitHub Action; the rest of this README covers the full plugin the action is carved from.
When you hand work to an AI, does it behave like a brand-new hire, or like an engineer who has been in the org eighteen months? The difference isn't capability - both can write correct code. It's externalized context: knowing where things live, which contracts are load-bearing, where the minefields are, and why the weird thing is weird. An AI contributor is structurally always the new hire - every session starts with an empty head, seeing the codebase through one narrow context window. So the whole question becomes: how much of the tenured engineer's implicit map has the codebase made explicit and navigable? The more it has, the more a fresh agent behaves like it has been here eighteen months.
The aim is not an AI that comprehends complexity humans no longer can, trusted blindly - an agent fluent about code nobody can verify is the dangerous case, not the goal. The aim is a codebase legible enough that the relevant slice fits one context window, where the agent's answers stay anchored to code you can still check in ten seconds. Legibility you can trust, not omniscience you can't.
There's a second half, and it's the same ethic pointed the other way. When a contributor makes a mistake, the question is never "who do we blame" but "what made that mistake possible, and what would make it impossible next time?" A bank doesn't give a new engineer production access and hope - it builds role-based access, staged environments, and CI that catches the error before it ships. Those guardrails aren't distrust; they're how you protect people from costly mistakes by design, and an AI contributor needs the same protection a human does. /assess scores exactly these: linters, architecture tests, CI gates, coverage, review automation. Framed positively: have we set the codebase up so that doing the wrong thing is hard, and the right thing is the path of least resistance? Give the contributor the map and the guardrails - that is what /assess measures the distance to.
The headline pieces are five skills:
npx claudepluginhub bjcoombs/ai-native-toolkit --plugin ai-native-toolkitPersonal Claude Code + Codex dev stack: security hooks, AI-first code conventions, /security-review, /repo-map, /stack-check, portable statusline. Designed to complement other skills-based plugins, not replace them.
Check how well your repo supports AI coding agents.
Agent-Ready Codebase Assessment — scores your codebase across 8 dimensions and generates an actionable improvement roadmap framed around the Stripe AI benchmark
AI-SDLC governance framework for Claude Code — action enforcement, telemetry, quality gates, and review agents
Adversarial multi-agent pipeline for Claude Code. GAN-style loops where generators produce artifacts, discriminators validate them, and feedback drives convergence.
AI-powered development workflow automation - Phase-based planning, implementation orchestration, preflight code quality checks with security scanning, ship-it workflow, and development principles generator for CLAUDE.md