ai-literacy-superpowers

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.

This skill should be used when an agent is deciding whether to author a dynamic workflow — a self-authored, ephemeral multi-agent harness — for a task. Use when the user asks about "dynamic workflows", "multi-agent harness", "fan-out", "subagents in parallel", "adversarial verification", "the ultracode trigger", "should I use a workflow", or when a task looks long-running, massively parallel, highly structured, or adversarial. Names the six composable patterns, the election rubric for when NOT to spend the extra compute, and the two governance invariants (INV-1 ephemeral-proposes, INV-2 quarantine).

This skill should be used when the user asks about "verification slots", "integrating a linter", "adding a deterministic tool", "harness-enforcer", "constraint enforcement interface", "wrapping a tool", or needs the technical reference for how deterministic and agent-based checks work in the harness framework.

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 the user wants to estimate or predict the cost, token usage, or time of a task BEFORE it runs — "how much will this feature cost to build", "estimate the tokens for this spec", "what will this slice cost", "predict the agent-compute time before I commit" — produces a token + time estimate as a range with disclosed confidence, adding a dollar figure only when an observability/costs snapshot grounds it. The prospective sibling of cost-tracking, which records actual spend after the fact.

Use when the user wants to capture AI tool costs, review spending trends, set cost budgets, or integrate cost data into health snapshots — guides quarterly cost capture, records data in a structured format, and updates MODEL_ROUTING.md with observed cost patterns

Use when watching the human verifier rather than the output — defines the observable proxies, the observed/inferred/asked confidence discipline, the default thresholds, the one firm principle, six-level scaling, and the honesty rule that the reservoir-warden agent and the reservoir-check Stop hook both inherit

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

Use when checking harness health, setting up observability cadences, understanding snapshot formats, configuring telemetry export, or verifying that the harness's own observability is working — covers all four layers of harness observability

Use when designing a new plugin component (skill, agent, command, or backing script) for the ai-literacy-superpowers plugin or a sister plugin in this marketplace. Surfaces the design questions that the four-layer TDAD architecture implies — component type, tier targeting, scenario shape, FINDING-vs-scenario judgement, modification-vs-refactor heuristic. Loadable by spec-writer, tdd-agent, or directly during human brainstorming. Not a gate; a methodology guide that names the questions to ask before authoring.

Use when running the shared drift-detection logic that backs /harness-audit and /harness-sync — produces a structured drift report covering convention files, ONBOARDING.md, snapshot staleness, template drift, constraint regressions, recurring reflection patterns, and HARNESS.md Status section accuracy.

This skill should be used when the user asks about "garbage collection rules", "entropy fighting", "documentation staleness", "dead code detection", "convention drift", "periodic checks", "auto-fix rules", or needs guidance on the Garbage Collection section of HARNESS.md.

Use when authoring or interpreting Mitchell-extended model cards in this plugin. Covers when each of the 10 sections applies, the citation discipline, the honesty rules (claim-level and card-level), and the tiered source strategy. Reference for the model-card-researcher agent and the /model-card command.

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

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

Use when generating a human-readable onboarding document from HARNESS.md, AGENTS.md, and REFLECTION_LOG.md — produces a friendly guide for new team members joining a harnessed project

Use when setting up a new project's conventions, onboarding AI to an existing codebase, after team composition changes, or when AI output quality varies depending on who prompts — guides structured discovery of tacit team knowledge into explicit, enforceable artefacts

Use when coordinating changes across multiple repositories — syncing skills, templates, agents, or harness policies between upstream and downstream repos, or designing portfolio-level agent orchestration

Use when reviewing or refactoring code and wanting a structured lens beyond SOLID — applies Daniel Terhorst-North's CUPID properties to surface improvement opportunities in any codebase or language.

Use when auditing Docker images in this project for CVEs, base image staleness, or remediation recommendations — covers all four TUI images (Go, Python, Kotlin, C#)

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 defining governance metrics, reading governance health snapshots, generating the governance dashboard, or understanding the governance data model. Referenced by the governance-auditor agent and the /governance-health command.

Use when assessing AI literacy across multiple repositories — aggregates individual assessments into a portfolio view with level distribution, shared gaps, outliers, and a prioritised improvement plan grouped by organisational impact. Discovers repos from local paths, GitHub orgs, or topic tags.

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.

This skill should be used when the user asks about "harness engineering", "what is a harness", "harness framework", "AI code quality", "context engineering", "architectural constraints", "garbage collection for code", or wants to understand the conceptual foundation behind the harness-engineering plugin.

Use when conducting a governance audit — detecting semantic drift in governance constraints, inventorying governance debt, checking three-frame alignment, or when the governance-auditor agent needs methodology for deep investigation.

Use when writing governance constraints for HARNESS.md, translating governance language into operational meaning, reviewing existing governance constraints for falsifiability, or when "/governance-constrain" needs guidance on the authoring workflow.

Use when the user wants to create or update a Team Topologies Team API document with AI literacy portfolio assessment data — generates a template Team API with literacy levels, discipline scores, shared gaps, and improvement plans, or updates an existing Team API with the latest assessment data

Use when the user wants to generate, update, or customise an HTML dashboard from portfolio assessment data — produces a self-contained HTML file with level distribution, repo table, shared gaps, improvement plan, and trend visualisation from multiple quarterly assessments

Use when generating a prioritised improvement plan after an AI literacy assessment, or when a user knows their current level and wants to know what to do next — maps gaps to specific plugin commands and skills, grouped by target level, with accept/skip/defer for each item

This skill should be used when the user asks about "local models", "custom models", "fine-tuning", "self-hosting models", "model selection", "which model should I use", "data privacy and models", "LoRA", "RAG vs fine-tuning", "Ollama", "vLLM", or wants guidance on whether to build, host, or customise their own AI models.

Use when auditing project dependencies for known vulnerabilities, supply chain risk, or provenance issues — covers Go modules, Maven/JVM, and CI integration for automated scanning

Use when designing architectural fitness functions as GC rules — periodic checks that verify system-wide properties like layer boundaries, coupling trends, and complexity hotspots, complementing per-change constraints with weekly architectural health monitoring

Use when syncing HARNESS.md conventions to Cursor, Copilot, and Windsurf convention files — reads Context and Constraints sections and generates tool-specific output so all AI coding tools share the same project rules

This skill should be used when the user asks about "writing conventions", "codebase context", "HARNESS.md context section", "convention documentation", "how to write enforceable rules", or needs guidance on the Context section of HARNESS.md.

Use when reviewing GitHub Actions workflow files for security issues, hardening CI pipelines, or assessing supply chain risk in a repository that uses GitHub Actions

Use when creating new source files, writing new functions or types, or significantly rewriting existing code — ensures code is structured for humans to read first, with narrative preambles, reasoning-based documentation, and presentation ordered by understanding rather than compiler convention