LLM Mart Basic

Use when the user explicitly asks to save, curate, or consolidate what was learned, or closes a meaningful knowledge-work session. Not for remote, publish, deploy, or irreversible changes.

Use when adding C/C++ dependencies with Conan or vcpkg, managing binary compatibility, integrating with CMake via conanfile.txt or vcpkg.json, or choosing between Conan and vcpkg.

Use when reading TSan race reports, debugging deadlocks with GDB thread inspection, analyzing Helgrind lock-order violations, or reviewing std::atomic and happens-before usage.

Use when reviewing cryptographic code for timing side-channels, statically in compiled output or at runtime with statistical timing tests. Not for known-answer vectors: use wycheproof.

Use when asked to implement under non-negotiable constraints: performance budgets, platform limits, legal or API rules. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user asks to rank deployment platforms and stacks against their product with quantitative trade-offs. Not for source or remote mutation.

Use when explaining or building on Linux container primitives: namespaces, cgroups v2, overlayfs, runc and the OCI spec, seccomp-bpf, capabilities, or escapes. Not for VM isolation: use qemu-kvm.

Use when a long session has accumulated stale or conflicting context or the user asks to refresh it. Not for remote, credential, publish, deploy, or irreversible changes.

Use when asked to mine prior chats on a scheduled or watcher tick and maintain project memory. Not for remote, credential, publish, deploy, or irreversible mutation.

Use when crossing a public API boundary, guarding complex invariants, or hardening untrusted input or integration seams. Not for remote 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 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 asked to write or fix product/marketing copy, landing pages, UI strings, brand voice, AI tells, an end-of-article CTA, or a hook/lede. Not for whole articles: use technical-article-writer.

Use when asked to create, refresh, or port a brand tone of voice guide (TONE.md) or measurable prose style rules (PROSE.md). Not for general copywriting: use copywriting.

Use when loading core files in GDB or LLDB, enabling core dump generation via ulimit or coredumpctl, mapping symbols with debuginfod, or extracting backtraces from production segfaults.

Use when a candidate patch or answer needs independent corroboration before it is trusted. Not for multi-stance investigation: use council.

Use when a measured cost surface needs one-change-at-a-time reduction under frozen guardrails. Not for speed-only optimization: use optimize.

Use when the user asks for a council, second opinions, or parallel investigation. Not for cross-examination of existing proposals: use cross-critique.

Use when working with C++20 co_await, co_yield, and co_return, implementing promise_type, sizing coroutine frames, or debugging suspended coroutines in GDB. Not for Rust async: use idiomatic-rust.

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

Create and manage database migrations

Analyze GitHub issue and create technical specification with implementation plan

Scaffold new feature with boilerplate code

Create a Jobs to be Done (JTBD) document for a product feature focusing on user needs

Create developer onboarding guide

Create a new branch, commit changes, and submit a pull request with automatic commit splitting

Create a Product Requirements Document (PRD) for a product feature

Create a comprehensive Product Requirement Prompt (PRP) with research and context gathering

Guide for creating pull requests using GitHub CLI with proper templates and conventions

Manage git worktrees for open PRs and create new branch worktrees

Manage cross-platform reference links

Systematically debug and fix errors

Analyze decision quality with scenario testing, bias detection, and team decision-making process optimization.

Explore decision branches with probability weighting, expected value analysis, and scenario-based optimization.

Audit dependencies for security vulnerabilities

Map and analyze project dependencies

Design optimized database schemas

Design RESTful API architecture

Create systematic digital twins with data quality validation and real-world calibration loops.

Analyze directory structure and purpose

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.