LLM Mart Basic

Use when recent resolved feedback may reveal a broader recurring defect pattern across the project surface. Not for source-level feedback collection: use feedback-sweep.

Use when one named review viewpoint must run fix cycles until a fresh reviewer finds nothing. Not for multi-viewpoint review or remote, credential, publish, deploy, or irreversible changes.

Use when the user invokes this skill to generate targeted questions proving the author understands the change's codebase effect. Not for reviewing the change: use review.

Use when asked to review a pull request, examine code changes, find bugs, or audit a branch, in standard or depth mode. Not for an iterative review-and-fix loop: use audit-project.

Use when the user wants a per-finding visual walk through a diff or PR. Not for written review reports: use review. Not for codebase tours: use show-me.

Use when implementation must be checked against an authoritative specification, or during PR review for spec drift against checked-in specs. Not for spec updates: use spec-driven-implementation.

Use when the user runs /browser-qa for report-only QA results without entering a fix loop. Not for remote, credential, publish, deploy, or irreversible changes.

Use when asked to reproduce, profile, or verify CLI/TUI behavior. Produces a deterministic transcript or profile proof with session cleanup. Not for CLI design advice, use cli-for-agents.

Use when asked to verify or reproduce browser or Electron UI behavior with before-and-after evidence and no leftover processes. Not for remote, credential, publish, deploy, or irreversible changes.

Use when asked to prove coverage, find missing cases, or enumerate state, decision, requirement, or behavior space. Not for round-based or single-property tests: use askme, property-test-authoring.

Use when a complete product needs production-like acceptance evidence against documented acceptance criteria. Not for single-component evaluation or evaluation without documented criteria.

Use when verification is looping, would re-run untouched code, or duplicates an established proof. Not for tasks that require source or remote-system changes.

Use when a test surface needs behavior-guarding coverage raised to a configured target with mutation kill evidence. Not for line-coverage inflation without mutation proof.

Use when asked to initialize, scope, estimate, configure, validate, or optimize a mewt, muton, or mutation testing campaign before execution. Writes the TOML config. Not for running it: use the mewt CLI.

Use when a mutation campaign leaves surviving mutants needing triage. Classifies each as false-positive, missing-test, genotoxic, or removable. Not for setup: use mutation-campaign-configuration.

Use when a product surface must be tested against extreme or hostile worlds. Not for design disputes: use possible-worlds. Not for remote, credential, publish, deploy, or irreversible changes.

Use when property-based testing, theorem proving, or formal proof tactics require zero unproven properties. Not for remote, credential, publish, deploy, or irreversible changes.

Operate explicit orchestrator, implementer, validator, and scribe roles through a caller-selected agent runtime. Triggers: "agent-native factory", "role-shaped agent panes", "persistent workers".

Use an explicitly selected AGY runtime for one provided packet or fresh validator context. Triggers: "agy", "antigravity", "AGY evidence".

Run one caller-supplied Codex command non-interactively and capture evidence. Triggers: "run Codex headless", "capture Codex evidence".

A green PR, a controller reporting success, and not one line of the new code running

Capture and document session learnings

Setup automated release workflows

Configure CDN for optimal delivery

Setup complete testing infrastructure

Setup complete development environment

Configure code formatting tools

Configure Kubernetes deployment manifests

Setup code linting and quality tools

Configure load and performance testing

Setup monitoring and observability tools

Configure monorepo project structure

Implement API rate limiting

Setup visual regression testing

Open the Build with Claude contribution guide for writing a community story

Test and refine simulation accuracy with validation loops, bias detection, and continuous improvement frameworks.

Plan and organize sprint workflows

Generate daily standup reports

Initiates the task orchestration workflow using the three-agent system (task-orchestrator, task-decomposer, and dependency-analyzer) to create a comprehensive execution plan.

Check the current status of tasks in the orchestration system with various filtering and reporting options.

Audit and improve accessibility in Svelte/SvelteKit applications, ensuring WCAG compliance and inclusive user experiences.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.