ai-literacy-habitat

Run an AI literacy assessment — scan the repo for evidence, ask clarifying questions, produce a timestamped assessment document, apply immediate habitat fixes, recommend workflow changes, capture a reflection, and add a literacy level badge to the README

Run the carpaccio agent (cadence governor) on a task description — produces a slicing record at docs/superpowers/slices/<task-slug>.md; use at orchestrator step 0 or before spec-writer when running manually

Run the Choice Cartographer (decision-archaeology agent) on a spec — produces the choice-story record at docs/superpowers/stories/<slug>.md; use after spec-mode /diaboli dispositions are resolved, before plan approval

Sync HARNESS.md conventions to Cursor, Copilot, and Windsurf convention files

Capture AI tool cost data — guide through provider dashboards, record spend and token usage, compare to previous snapshot, update MODEL_ROUTING.md

Use after spec-writer completes (spec mode) or after the final code-reviewer PASS (code mode) — reads the spec or implementation and produces a structured objection record; read-only trust boundary enforces the human-cognition gate on dispositions at both gates

Use this agent to run an AI literacy assessment — scans the repository for observable evidence, asks clarifying questions, and produces a timestamped assessment document with a README badge. Examples: <example> Context: User wants to know their team's AI literacy level user: "Where are we on the AI literacy framework?" assistant: "I'll use the assessor agent to run a full assessment." <commentary> The assessor scans the repo, asks clarifying questions, and produces an evidence-based level assessment. </commentary> </example> <example> Context: User runs /assess command user: "/assess" assistant: "Starting the AI literacy assessment." <commentary> The /assess command dispatches the assessor agent. </commentary> </example>

Use when starting any new task via the orchestrator — reads the raw task description, slices it into end-to-end-complete pieces, and produces a structured slicing record; read-only trust boundary enforces the human-cognition gate on dispositions; runs at orchestrator step 0 before spec-writer

Use after spec-mode advocatus-diaboli dispositions are resolved and before plan approval — reads the spec, reconstructs the decisions it implies (including the silent ones), and produces a structured choice-story record; read-only trust boundary enforces the human-cognition gate on dispositions

Use after implementation is complete and tests are green — reviews code through the CUPID and literate programming lenses, returns PASS or a prioritised list of findings

Use when auditing a project for secrets committed to source control, setting up gitleaks, or hardening the "No secrets in source" harness constraint — covers scanning, baselining, configuration, and CI integration

Use when acting as the adversarial spec reviewer — raises steel-manned objections across six categories before plan approval, requires evidence per objection, and discloses what was not challenged

This skill should be used when the user asks to "assess AI literacy", "run an assessment", "check literacy level", "evaluate our AI collaboration", "where are we on the framework", or wants to determine their team's AI literacy level using the ALCI instrument.

Use when setting up automatic PR constraint enforcement via GitHub Actions — covers the advisory-vs-blocking split, workflow installation, configuration options, and reading the output

Use when acting as the cadence governor — slices a raw task description into thin, end-to-end-complete pieces before any spec is written; produces a structured slicing record for human disposition; runs at orchestrator step 0

15 hooks across 5 events