Install structured self-improvement loops with instinct-based learning into Claude Code — research, plan, execute, verify, reflect, learn, iterate. On-demand or weekly analysis to save tokens. Supports multi-agent parallel analysis.
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Audits a window of recent commits for real defects, confirms each finding before touching code so false positives die first, and checks every surface a change touches — so 'looks done' is never mistaken for 'is correct'.
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline at the deploy seam. A merge into a branch that auto-deploys is not "done" until the deploy provider reports the merged commit SHA running and a healthcheck endpoint returns 200. Companion to the vendored finishing-a-development-branch skill…
Enforces Law 1 (Research Before Executing) of the 7 Laws of AI Agent Discipline. Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated…
Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Detects when a session has drifted away from its stated goal by scoring recent tool activity against the '## Goal' section of taskplan.md, so drift is caught mid-session instead of at end-of-session reflection.
Enforces Law 1 (Research Before Executing) of the 7 Laws of AI Agent Discipline. Interview the user relentlessly about a plan or design until shared understanding is reached, resolving every branch of the decision tree before any code is written. Ported from mattpocock/skills under MIT.
Enforces Law 1 (Research Before Executing) and Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline. Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates CONTEXT.md + ADRs inline as decisions crystallise. Ported from mattpocock/skills under…
Enforces Law 5 (Reflect After Every Session) of the 7 Laws of AI Agent Discipline. Compact the current conversation into a handoff document for another agent to pick up. Ported from mattpocock/skills under MIT.
Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Turn an ambiguous or high-impact change into scoped, verifiable acceptance criteria (observable AC-NNN, explicit in/out scope, named verification methods, and a [revised] protocol that forbids silently dropping a criterion) before or alongside…
Enforces all 7 Laws as a standing stance — go with Claude Code and the model, not against it. Skills are scaffolding that merges into the model over time; the durable core is goal-driven execution (the higher the stated goal, the better) plus self-discipline guardrails.
Orchestrator for all 7 Laws of AI Agent Discipline. Walks an agent-emitted recommendation list top-to-bottom under the 7 Laws — restate, route per item, verify before advancing, reflect at the end, close with the mandatory three-section block. Standalone with inline fallbacks; trigger phrases are matched by the…
Enforces Law 6 (Iterate Means One Thing) of the 7 Laws of AI Agent Discipline at PRD scale. Ralph is an autonomous AI agent loop that runs repeatedly until all PRD items are complete. Converts PRDs to executable JSON, implements stories iteratively with quality checks, and tracks progress.
Enforces Law 1 (Research Before Executing) of the 7 Laws of AI Agent Discipline. Makes past sessions first-class research material by searching the observation log with BM25 ranking, so 'have I hit this before?' is answerable before re-deriving a fix or repeating a mistake.
Enforces Law 1 (Research Before Executing) of the 7 Laws of AI Agent Discipline. Establishes git ground truth — branch, status, stashes, worktrees, ahead/behind — before any mutation, halts on protected or destructive operations, then carries the known-good state through to a landed PR: stage by filename, commit one…
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. After any failure in the verification ladder or auto-loop, classify the failure class before retrying — provider, tool-schema, deterministic-policy, git, worktree, runtime — so retry-vs-pause-vs-self-heal-vs-stop is an intentional decision…
Enforces Law 1 (Research Before Executing) of the 7 Laws of AI Agent Discipline. Convene a 5-persona adversarial council (Contrarian, Expansionist, Logician, Researcher, Buyer) that attacks an idea from every angle, then a Judge returns one GO / RESHAPE / KILL verdict plus the cheapest 48-hour test to de-risk it — so…
Enforces Law 3 (One Thing at a Time) of the 7 Laws of AI Agent Discipline by scoping edits to a directory and blocking destructive shell commands. Use this skill to prevent destructive operations when working on production systems or running agents autonomously.
Enforces Law 1 (Research Before Executing), Law 3 (One Thing at a Time), and Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Fix one defect through TDD and one PR, isolate unrelated dirty checkouts in an owner-locked clean worktree, and return only eligible clean checkouts to the detected default…
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Reviews the current diff for over-engineering (code that could reuse an existing file, a stdlib or native feature, or fewer lines) and reports trim findings without touching code, so 'it works' is never mistaken for 'it is the minimum that…
Enforces Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline. Distills repeated successful tool sequences into reusable draft instincts, so a pattern that worked three times becomes a captured recipe instead of being re-derived from scratch every session.
Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: reconcile DB-vs-disk-vs-memory state before any unit runs, so a stale flag, missing artifact, or out-of-sync row never re-dispatches a unit that already completed or never started.
Enforces Law 5 (Reflect After Every Session) of the 7 Laws of AI Agent Discipline at phase boundaries. Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.