LLM Mart Basic

Use when a user wants a looser conceptual graph for exploratory work. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a human has an approved delivery plan and wants the full chain run under phase gates. Not for planning, open-ended debugging, or single-step execution: use work directly.

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 the user says release this, publish this package, or cut a release for a changesets-based npm package. Not for non-npm packages or releases without a changesets workflow.

Use when the user requests axiom, axiom-mode, axiom-compact, formal-logic, or compact form. Not for changing code or remote state.

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 writing reset-to-main startup code, vector tables, VTOR, .data/.bss init, stack setup, startup.s, or crt0 for Cortex-M/RISC-V. Not for the bootloader jump: use bootloaders-embedded.

Use when writing Bazel BUILD files with cc_library or cc_binary rules, Bzlmod dependencies, toolchain registration, remote execution, sandbox debugging, or bazel query and cquery graphs.

Use when asked to validate a web app, CLI, API, or generated artifact against a source-blind behavior contract. Not for source or remote-system changes.

Use when building static archives with ar, stripping or converting binaries, mapping crash addresses with addr2line, or demangling C++ symbols. Not for ELF analysis: use elf-inspection.

Use when asked to determine what a change could break before it ships. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user names one book, course, paper, or source document and asks to distill it into a reusable skill. Not for a folder of sources: use map-corpus.

Use when writing a custom bootloader, jumping to application code, relocating VTOR, or implementing DFU/USB firmware update on Cortex-M. Not for reset-to-main: use baremetal-startup.

Use when C or C++ code needs memory-safety or undefined-behavior guarantees proved with CBMC, or ACSL contracts checked with Frama-C Eva or WP. Not for choosing the proof policy: use proof-driven.

Use when explaining branch predictors, mispredict penalties, speculative execution, Spectre or Meltdown mitigations, or branchless code. Not for pipeline stage theory: use cpu-pipelines-and-hazards.

Use when the user asks for branded or style-governed output. Not for remote, credential, publish, deploy, or irreversible changes.

Use when bloated code needs clean re-derivation, or the user says "this module is bloated" or "break it and rebuild". Not for untracked data or changes without VCS rollback.

Use when the user runs /browser-cookie-store to populate the session cookie store from installed browsers. Not for remote, credential, publish, deploy, or irreversible changes.

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 building, debugging, or verifying browser-rendered code, or running browser tests for PR- or branch-affected pages. Not for source, remote-system, credential, publish, or deploy changes.

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

Analyze Python async code for correctness, patterns, and potential issues.

Profile Python code for performance bottlenecks using cProfile, memory_profiler, or py-spy.

Evaluate public API surfaces against guidelines and exemplars.

Principal-level architecture assessment against ADRs and design patterns.

Systematic bug detection with language-specific expertise.

Run a detailed review that picks its dimensions from what the codebase and diff contain.

Active security hardening of the existing codebase, with a report and concrete proposals to apply.

Audit Makefiles for best practices and portability.

Intensive mathematical analysis for numerical stability and correctness.

Static-analysis hot-spot review for time and space complexity.

Analyze code quality across 6 dimensions (duplication, algorithms, clean code, architecture, errors, style) and apply fixes.

Expert-level Rust audits for safety and correctness.

Audit shell scripts for correctness, safety, and portability.

View recent skill executions with full context and error details.

Analyze skill execution metrics and identify unstable or underperforming skills.

Evaluate and upgrade test suites with TDD/BDD rigor.

Run a computer use task on the desktop via Claude's vision and action API

Stage changes, generate conventional commit message, commit, and push to current branch. One-shot git add-commit-push.

Draft a Conventional Commit message for staged changes. Analyzes diffs, classifies change type, and formats scope/body.

Create git release tags from merged PRs or version args. Pushes a v-prefixed tag to trigger the release pipeline, then confirms the run started.

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…

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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.