continuous-improvement

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 companion hook, not enumerated here.

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 works'.

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 — does not replace it, runs after it for projects on Railway, Cloudflare Workers, Vercel, Netlify, Fly.io, or any other auto-deploy target.

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 agents.

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 concern, push the feature branch, verify the push landed, open the PR, and after the PR merges fast-forward the default branch and check it out.

Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. A comprehensive verification system for agent coding sessions covering build, types, lint, tests, security, and diff with a PASS/FAIL report.

Enforces Law 1 (Research Before Executing) of the 7 Laws of AI Agent Discipline. Audits the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommends the highest-value continuous-improvement-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Claude Code or understanding what capabilities are actually available in their environment.

Enforces Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Pre-dispatch invariant: validate worktree root before any source-writing tool call. Catches missing .git, fallback path-only creation, stale leases, foreign-session ownership, and non-worktree git operations before they corrupt history.

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 implementation, so the plan that gets built is the plan that was agreed, not an invented default. Use when clarifying a feature, defining acceptance criteria, de-risking a security/data/migration/integration change, or preparing implementation requirements for another agent. Do not trigger for trivial edits, straightforward fixes, active debugging, or code review.

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 you pressure-test an idea before sinking time into building the wrong thing.

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.

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 task_plan.md, so drift is caught mid-session instead of at end-of-session reflection.

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 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.

Law activator for the 7 Laws of AI Agent Discipline. Unified four-source dispatcher — routes tasks to the correct Law-aligned specialist across the CI plugin (tdd-workflow, verification-loop, gateguard, ralph, deploy-receipt) and four registered upstream companions (Obra superpowers, addy agent-skills, ruflo-swarm, oh-my-claudecode) so the right discipline fires automatically instead of the agent skipping a step. Product-management coverage comes from phuryn/pm-skills via an out-of-band marketplace install (see docs/THIRD_PARTY.md). Not a peer skill — a dispatcher for the others.

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 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 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 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 MIT.

Enforces Law 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline by making token-budget tradeoffs explicit before the response is composed. Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.

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 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, not a generic 'try again'.

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 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 2 (Plan Is Sacred) of the 7 Laws of AI Agent Discipline. Decision-framing lens that pairs WILD generation with RISA execution when emitting recommendation lists. Not a runtime hook.

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.

Stream live swarm events using the Monitor tool for real-time observability

Initialize a multi-agent swarm with anti-drift configuration

2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection

Socratic deep interview with mathematical ambiguity gating before autonomous execution

Diagnose and fix oh-my-claudecode installation issues

Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode

Full autonomous execution from idea to working code

Cancel any active OMC mode (autopilot, ralph, ultrawork, ultraqa, swarm, ultrapilot, pipeline, team)

Claude-Codex-Gemini tri-model orchestration via /ask codex + /ask gemini, then Claude synthesizes results

Configure notification integrations (Telegram, Discord, Slack) via natural language

Configure HUD display options (layout, presets, display elements)

Install or refresh oh-my-claudecode for plugin, npm, and local-dev setups from the canonical setup flow

CLI-team runtime for claude, codex, or gemini workers in tmux panes when you need process-based parallel execution