LLM Mart Basic

Use when asked to prune a design or codebase until only primitives remain, producing a first-principles map. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user says "loop me" or asks to design a recurring workflow. Don't use for remote, credential, publish, deploy, or irreversible changes.

Use when the user needs a compact read-only current-work view from Git state, recorded test evidence, and optional graph.yaml. Not for tasks that need source or remote-system changes.

Use when a project is between phases, the author asks what to do next, too many threads are open, or work needs re-entry. Not for gating whether one named task may proceed.

Use when a user commits to a direction and asks to plan, brief, or research it; modes score, breakdown, shape, visual. Not for codebase audit: use plan-review. Not for four-phase review: use autoplan.

Use when a design dispute has at least two live interpretations and the caller wants worlds made explicit or a plain-language recommendation. Not for selecting a design or source/remote changes.

Use when asked to judge whether to adopt, switch, reject, or revisit technology, library, pattern, or architecture, or a second opinion. Not for scoping: use brainstorm. Not for forks: use decide.

Use when a user asks for a gut-check on a decision or action, or asks whether enough is known to proceed. Not for numeric confidence scoring.

Use when the user asks for a flattened view of roadmaps and next actions. Not for multi-session route planning: use wayfinder.

Use when the user wants to harden a chosen but tentative artifact into one durable result. Not for remote, credential, publish, deploy, or irreversible changes.

Use when starting a project or feature, requirements are unclear, or a change crosses modules. Not for implementing from an existing spec: use spec-driven-implementation.

Use when work has distinct modes and the user wants states, events, guards, outcomes, illegal transitions, not a prose todo list. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a user wants to design an abstraction boundary that collapses a complex implementation into a simpler interface without leaking internal state. Not for implementation.

Use when user wants an async questionnaire, a discovery questionnaire, or a knowledge gap needs answers outside the repo. Not for direct conversation: use askme. Not for agent research: use research.

Use when settled conversation decisions need synthesis into an agent-ready implementation spec, stopping before publication. Not for turning plans into tickets: use to-tickets.

Use when a settled plan needs implementation tickets published as blocker-linked slices, tracer bullets, or expand-contract sequencing. Not for implementation: use work.

Use when a message contains `TODO ADD: <requirement>`. Not for deepening coarse lists: use todos-enhance. Not for resyncing lists: use todos-update. Not for retitling/reordering/completing todos.

Use when tasks are too vague, read as headings, or the user asks to sophisticate the todos. Not for stale reconciliation: use todos-update. Not for adding requirements: use todo-add.

Use when user asks to update todos, resync the task list, say what to do next, or plan and tree have drifted apart. Not for coarse lists: use todos-enhance. Not for adding requirements: use todo-add.

Use when a greenfield project or large feature build will not fit in a single agent session. Don't use for implementation, remote credential changes, or work that fits in one session.

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

Convert a Claude Code or Codex session JSONL file into an animated replay of the conversation.

Convert the current session's work into a shareable blog post, case study, or social thread.

Extract writing style from exemplar text to create a reusable style profile.

Extract your writing voice from samples into a reusable profile

Generate text in your extracted writing voice

Run learning pass on manually edited text to improve voice profile

Review existing text against a voice profile

Record browser sessions using Playwright

Create terminal recordings with VHS tape scripts

Cross-artifact consistency analysis across spec.md, plan.md, and tasks.md after task generation

Generate a custom checklist for the current feature based on user requirements.

Ask targeted questions to resolve spec ambiguities

Create/update project constitution from principle inputs, syncing dependent templates

Assess the codebase against spec, plan, and tasks, then append unbuilt work as new convergence tasks

Execute the implementation plan by processing all tasks from tasks.md

Execute implementation planning from spec to generate design artifacts.

Create or update the feature specification from a natural language feature description.

Bootstrap spec-driven development workflow at the start of a session

Generate dependency-ordered tasks.md from design artifacts

Convert tasks.md entries into GitHub Issues

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.