LLM Mart Basic

Use when any bounded workflow starts or reaches an action, path, proposal, or merge boundary. Refuses rather than default-allow on an unreadable constraint set.

Use when work is about to grow past the ask, the task may already be done, or the user requests only the minimum. Not for executing the work: use tdd to build or strike-the-root to fix.

Use when an artifact or skill has just changed and is about to be called done, committed, or handed off. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user suspects no installed skill covers a task and wants proof. Names the owning skill or writes a missing-skill brief. Never routes or invokes the matched skill.

Use when a task, feature, or fix is called done, complete, finished, or fixed, or before a commit, PR, or next task. Not for fact-checking: use verify-both-ways. Not for measuring: use verify-this.

Use when the user wants to classify abstractions as useful, bad, or busy and keep one shallow level. Not for tasks requiring source or remote-system changes.

Use when a task is ambiguous or intent needs eliciting: exhaustive/collaborative/adversarial askme, batch questions, interview, ambiguity scan, or intent proposal. Not for one fork: use decide.

Use when the user runs /autoplan on a plan or idea. Reviews, amends, and derives task IDs with a final human approval gate. Not for remote, credential, publish, deploy, or irreversible changes.

Use when asked to park an undecided idea without representing it as decided or active work. Not for decided or active work: use the project task system.

Use when the user wants to collapse an open decision field to one decision and record its rationale locally. Not for multi-lens pressure testing. No remote or irreversible changes.

Use when the user has a fork and wants it resolved and applied, not explored: "decide this", "choose the path", or "decide and fix it".

Use when the user wants the finished-system contract for a piece of work: behavior, protocols, allowed, forbidden, and impossible states with a state-space proof. Not for runtime verification.

Use when the user wants to expand a decision field with additional options and dimensions. Not for selecting or applying an option: use decide. No source or remote-system changes.

Use when the user explicitly requests a Tarot draw or casually delegates an ambiguous choice among multiple valid approaches.

Use when a user wants to define failure states, recovery actions, bypasses, and degraded modes for a component during design. Not for runtime recovery.

Use when a user wants to rebuild a design, organization, or API from primitives. Not for a perspective take: use from-*-perspective seats.

Use when asked to derive the general rule a request carries as examples instead of a stated rule, then bound it. Not for ambiguity in a stated request: use askme. Read-only.

Use when a durable effort needs an approved, checkable success predicate before work starts. Not for requirement-to-evidence ledgers. Never remote, credential, publish, deploy, or irreversible.

Use when defining, revising, or gate-replanning the project structural backbone in project-root graph.yaml. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user asks to park ideas or inspiration for later. Not for code, backlog, or divergence-class cards, or remote, credential, publish, deploy, or irreversible changes.

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

"Tiered plugin quality review: branch (quick gates),

Check GitHub Discussions for highly voted learnings and promote them to Issues.

Evaluate Claude Code rules in .claude/rules/ directories for quality

Audit skill quality, frontmatter compliance, token efficiency, and activation reliability. Recommends improvements.

Test Claude Code skills using RED/GREEN/REFACTOR TDD phases in fresh subagents to prevent priming bias.

Validate hooks for security, performance, and SDK compliance

Validate plugin structure, schema, and naming. Use for plugin creation, debugging, or verification.

Initialize projects with architecture-aware templates using paradigm research and selection guidance for the target domain.

Generate an implementation plan with system architecture design and dependency-ordered task breakdown from a specification.

Guide project ideation through Socratic questioning to generate briefs with validated approaches and decision rationale.

Execute implementation plan systematically with progress tracking and checkpoint validation

Run full attune lifecycle as a mission with state detection and phase routing

Initialize a new project with git setup, CI/CD workflows, pre-commit hooks, Makefiles, and language-specific tooling.

Build a project skill library under .claude/skills/ as a resumable mission: discover, author in parallel, review adversarially.

Create specs from project briefs with acceptance criteria and testable requirements

Update existing project configurations to current best practices with selective component upgrades

Validate project structure and configurations against best practices with detailed issue reporting

Convene a multi-LLM expert panel to pressure-test strategic decisions with adversarial review and reversibility assessment.

Generate visual diagrams of codebase structure using Mermaid Chart MCP rendering.

Audit codebase for AI-generated code quality issues (vibe coding, Tab bloat, slop)

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.