ai-literacy-superpowers

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 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

Run the adversarial reviewer on a spec or implementation — produces the objection record at docs/superpowers/objections/<slug>.md (spec mode) or <slug>-code.md (code mode); use after spec-writer completes or after the final code-reviewer PASS

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 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 this agent when conducting a deep governance investigation — semantic drift analysis, governance debt inventory, constraint falsifiability scoring, three-frame alignment checks, or governance health reporting. Examples: <example> Context: User runs /governance-audit user: "/governance-audit" assistant: "I'll use the governance-auditor to conduct a deep governance investigation." <commentary> The governance-auditor owns the full audit methodology — drift detection, debt inventory, frame alignment. </commentary> </example> <example> Context: User suspects governance constraints have drifted user: "Our governance constraints feel out of date — the team works differently now" assistant: "I'll use the governance-auditor to check for semantic drift and governance debt." <commentary> Semantic drift is the governance-auditor's primary detection target. </commentary> </example> <example> Context: Quarterly governance review user: "Time for the quarterly governance audit" assistant: "I'll dispatch the governance-auditor for a full governance deep-dive." <commentary> Quarterly audit is the governance-auditor's primary scheduled cadence. </commentary> </example>

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 decision-archaeology agent — surfaces decisions a spec has made (including the silent ones), emits each material choice as a Henney-style pattern story for human disposition, and pays down intent debt before plan approval

This skill should be used when the user asks to "add a constraint", "design a constraint", "write a harness rule", "choose enforcement type", "promote a constraint", "configure a verification slot", or needs guidance on the Constraints section of HARNESS.md.

11 hooks across 3 events