ai-native-toolkit

**$ARGUMENTS**

Deep Understanding Mode

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.

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.

Critical analysis through mechanistic causal reasoning and proportionality testing. Challenges both over-complex and over-simple solutions.

Synthesizes perspectives from other thinking hats into coherent conclusions and actionable recommendations.

Creative problem-solving across the full complexity spectrum. Finds solutions nobody proposed.

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.