naimkatiman
UserAgents that gets sharper every session
Categories
Indexed Skills (51)
gateguard
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.
audit
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'.
goal-monitor
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.
recall
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.
reconcile
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, and verifies a push actually landed instead of assuming it did.
skill-distillation
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.
model-forward
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.
deploy-receipt
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.
ralph
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.
recovery-classification
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'.
safety-guard
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.
state-reconciliation
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.
strategic-compact
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.
wild-risa-balance
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.
worktree-safety
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.
continuous-improvement
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.
superpowers
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.
para-memory-files
Enforces Law 5 (Reflect After Every Session) and Law 7 (Learn From Every Session) of the 7 Laws of AI Agent Discipline by giving the agent a durable file-based memory it can read on resume and write at session end. File-based memory system using Tiago Forte's PARA method. Use this skill whenever you need to store, retrieve, update, or organize knowledge across sessions. Covers three memory layers: (1) Knowledge graph in PARA folders with atomic YAML facts, (2) Daily notes as raw timeline, (3) Tacit knowledge about user patterns. Also handles planning files, memory decay, weekly synthesis, and recall via qmd. Trigger on any memory operation: saving facts, writing daily notes, creating entities, running weekly synthesis, recalling past context, or managing plans.
proceed-with-the-recommendation
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.
tdd-workflow
Enforces Law 3 (One Thing at a Time) and Law 4 (Verify Before Reporting) of the 7 Laws of AI Agent Discipline. Use this skill when writing new features, fixing bugs, or refactoring code. Enforces test-driven development with 80%+ coverage including unit, integration, and E2E tests.
token-budget-advisor
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.
verification-loop
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.
workspace-surface-audit
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.
ai-slop-cleaner
Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode
autopilot
Full autonomous execution from idea to working code
autoresearch
Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior
cancel
Cancel any active OMC mode (autopilot, ralph, ultrawork, ultraqa, swarm, ultrapilot, pipeline, team)
handoff
Compact the current conversation into a handoff document for another agent to pick up.
grill-with-docs
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
api-and-interface-design
Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.
browser-testing-with-devtools
Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
code-review-and-quality
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
code-simplification
Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
debugging-and-error-recovery
Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.
deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing code.
documentation-and-adrs
Records decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
frontend-ui-engineering
Builds production-quality UIs. Use when building or modifying user-facing interfaces. Use when creating components, implementing layouts, managing state, or when the output needs to look and feel production-quality rather than AI-generated.
git-workflow-and-versioning
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, or when you need to organize work across multiple parallel streams.
idea-refine
Refines ideas iteratively. Refine ideas through structured divergent and convergent thinking. Use "idea-refine" or "ideate" to trigger.
incremental-implementation
Delivers changes incrementally. Use when implementing any feature or change that touches more than one file. Use when you're about to write a large amount of code at once, or when a task feels too big to land in one step.
planning-and-task-breakdown
Breaks work into ordered tasks. Use when you have a spec or clear requirements and need to break work into implementable tasks. Use when a task feels too large to start, when you need to estimate scope, or when parallel work is possible.
security-and-hardening
Hardens code against vulnerabilities. Use when handling user input, authentication, data storage, or external integrations. Use when building any feature that accepts untrusted data, manages user sessions, or interacts with third-party services.
shipping-and-launch
Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
source-driven-development
Grounds every implementation decision in official documentation. Use when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.
spec-driven-development
Creates specs before coding. Use when starting a new project, feature, or significant change and no specification exists yet. Use when requirements are unclear, ambiguous, or only exist as a vague idea.
test-driven-development
Drives development with tests. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality.
using-agent-skills
Discovers and invokes agent skills. Use when starting a session or when you need to discover which skill applies to the current task. This is the meta-skill that governs how all other skills are discovered and invoked.
ask
Process-first advisor routing for Claude, Codex, or Gemini via `omc ask`, with artifact capture and no raw CLI assembly
grill-me
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.