measuring-ai-proficiency

How to drive the 14-workflow agent factory in this repo from a Claude session. Covers: when to use the factory vs. direct edits, how to start the chain, where the human gates are, how to pick an implementer, how to recover from stuck PRs, and all the failure modes learned to date. Use this skill when the user asks you to ship a feature, fix, or refactor through the factory; when they reference an existing issue or PR in the factory chain; when a workflow is stuck or misbehaving; or when you need to file issues or plan files that the factory will pick up. Do NOT use this skill for: single-file scratch edits on an untracked branch, research questions, one-shot script runs, or any work that does not produce a PR to main.

[Beta] Cross-session analysis of accumulated .learnings/ files. Reads all entries, groups by pattern_key, computes recurrence across sessions, and outputs ranked promotion candidates. This is the outer loop's inspect step — it turns raw learning data into actionable gap reports. Use on a regular cadence (weekly, before major tasks, or at session start for critical projects). Can be invoked manually or scheduled.

Ensures alignment between user and Claude during feature/spec planning through a structured interview process. Use this skill when the user invokes /plan-interview before implementing a new feature, refactoring, or any non-trivial implementation task. The skill runs an upfront interview to gather requirements across technical constraints, scope boundaries, risk tolerance, and success criteria before any codebase exploration. Do NOT use this skill for: pure research/exploration tasks, simple bug fixes, or when the user just wants standard planning without the interview process.

Ensures alignment between user and Claude during feature/spec planning through a structured interview process. Use this skill when the user invokes /plan-interview before implementing a new feature, refactoring, or any non-trivial implementation task. The skill runs an upfront interview to gather requirements across technical constraints, scope boundaries, risk tolerance, and success criteria before any codebase exploration. Do NOT use this skill for: pure research/exploration tasks, simple bug fixes, or when the user just wants standard planning without the interview process.

[Beta] Creates permanent eval cases from promoted learnings and runs regression checks against them. Turns failures into test cases that prevent silent regression. This is the outer loop's regress-test step. Use when a learning is promoted and has a clear pass/fail condition, or on cadence to verify promoted rules still hold.

[Beta] Session-start scan that surfaces relevant learnings, recent errors, and eval status before work begins. Bridges the outer loop back into the inner loop by making accumulated knowledge visible at task start. Activated via SessionStart hook or manually before major tasks.

Post-completion self-review for coding agents that runs simplify, harden, and micro-documentation passes on non-trivial code changes. Use when: a coding task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.

Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.

Monitors context window health throughout a session and rides peak context quality for maximum output fidelity. Activates automatically after plan-interview and intent-framed-agent. Stays active through execution and hands off cleanly to simplify-and-harden and self-improvement when the wave completes naturally or exits via handoff. Use this skill whenever a multi-step agent task is underway and session continuity or context drift is a concern. Especially important for long-running tasks, complex refactors, or any work where degraded context would silently corrupt the output. Trigger even if the user doesn't say "context surfing" — if an agent task is running across multiple steps with intent and a plan already established, this skill is live.

Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.

Assess and improve repository AI coding proficiency and context engineering maturity. Use when users ask about: (1) AI readiness or AI maturity assessment, (2) context engineering quality or improvement, (3) CLAUDE.md, .cursorrules, or copilot-instructions files, (4) measuring how well a repo is prepared for AI coding assistants, (5) recommendations for improving AI collaboration, (6) what context files to add, or (7) comparing their repo to AI proficiency best practices.

Customize AI proficiency measurement for your specific repository through a guided interview. Use when: setting up measure-ai-proficiency for a new repo, adjusting thresholds for your team's size, hiding irrelevant recommendations, or mapping custom file names to standard patterns.

Assess and improve repository AI coding proficiency and context engineering maturity. Use when users ask about: (1) AI readiness or AI maturity assessment, (2) context engineering quality or improvement, (3) CLAUDE.md, .cursorrules, or copilot-instructions files, (4) measuring how well a repo is prepared for AI coding assistants, (5) recommendations for improving AI collaboration, (6) what context files to add, or (7) comparing their repo to AI proficiency best practices.