skillsmith

Senior product designer that audits UI screens and PRDs for usability flaws, missing error states, edge-case gaps, and accessibility issues. Use when the user says "review this design", "audit this UI", "critique this flow", or invokes /design-review with a screenshot and a PRD.

Fifth subagent in the agent-audit pipeline. Reads evals-[n].json and grading.json from the run dir, aggregates timing across all evals, computes pass rate and token stats, and writes timing.json and benchmark.json. Handles missing or partial data gracefully — writes what it can and flags gaps. Use when agent-audit hands off "compute the benchmark".

Second subagent in the agent-audit pipeline. Grades every assertion in evals-[n].json against actual_output using LLM judgment for semantic checks and tool calls for mechanical checks. Writes grading.json with per-assertion pass/fail and evidence, and updates evals-[n].json with a verdict per case. Use when agent-audit hands off "grade the evals".

Third subagent in the agent-audit pipeline (comprehensive mode only). Checks the environment for agentlinter and agnix, installs missing tools via npm if possible, runs each linter independently, then runs an LLM safety check against the audit registry. Each check is independent — a missing or failing tool is flagged and skipped, never blocks the others. Writes audit-[n].json to the run dir. Use when agent-audit hands off "run lint and safety check".

Fourth subagent in the agent-audit pipeline (comprehensive mode only). Measures the target skill's description trigger rate, iterates to improve it, and writes the winning description back to SKILL.md. Optimised for token efficiency: train iterations use 1 run per query; validation and baseline use 3. Stops early at 90% train pass rate. Appends optimiser results to audit-[n].json. Use when agent-audit hands off "run the description optimiser".

Orchestrator for the agent-audit pipeline. Prepares the run directory, asks the user which checks to run via a labelled checklist, dispatches specialist subagents in the correct dependency order, and produces a final report. Use when the user says "audit this skill", "run agent-audit", "check this agent", or agent-evaluate hands off "run audit".

First subagent in the agent-audit pipeline. Generates 3-5 test cases from the target skill's SKILL.md, runs them in parallel eval subagents, and writes 3-5 verifiable assertions per case from the actual output. Writes evals-[n].json to the run dir and updates the target skill's schemas.json with assertions. Use when agent-audit hands off "generate and run tests".

Builds a runnable skill from a plan produced by agent-plan. Writes SKILL.md and all named ref files to .claude/skills/<name>/. Use when handed a plan path, or when the user says "build this", "generate the skill", or "create the agent".

Orchestrator that evaluates a built skill by running agent-audit, agent-quality, or both. Asks the user which agents to run and which mode (flash or comprehensive). Use when the user says "evaluate this agent", "run evaluation on", or invokes /agent-evaluate after agent-build completes.

Reads audit artifacts from a completed agent-audit run (grading.json, audit-[n].json, evals-[n].json, feedback.json, quality-[n].json), classifies every finding by source and severity, generates a prioritised fix plan, presents it for user approval, then applies the approved fixes directly to the target skill's SKILL.md and ref files. Use when the user says "fix this skill", "apply fixes from the audit", "run agent-fix", or agent-evaluate hands off "apply fixes".

Plan a specialist AI agent before building. Outputs a plan for agent-build to consume. Use when the user wants to design or plan an agent, even if they don't explicitly say "skill" or "prompt". Invoked via "/agent-plan" or naturally via phrases like "plan an agent", "design a custom Claude", "think through a specialist prompt", "start a new agent".

Compares output quality between running the target skill (following its full SKILL.md protocol) and a vanilla claude-opus-4-7 run using a plain prompt derived from the skill description. Runs both as parallel subagents, shows side-by-side output, scores three dimensions, and emits a graded recommendation: 🟢 skill better, 🟡 marginal, 🔴 Opus alone better. Writes quality-[n].json to the skill's run dir. Use when the user says "run quality check", "compare skill vs Opus", "is this skill worth it", or agent-evaluate hands off "run quality".

Plan, build, audit, and fix a new agent in one automated run — no approval gates after the initial ask. Use when the user wants to create an agent end-to-end in one step, or says "ship an agent", "build this end-to-end", "one-shot build", "create and deploy an agent", "build an agent start to finish".