LLM Mart Basic

Use when reading or writing RV32/RV64 assembly, inline asm in C, the RISC-V psABI, IMAFD extension naming, compressed instructions, or QEMU RISC-V debugging.

Use when reading GCC or Clang x86-64 assembly, writing inline asm, decoding AT&T syntax, or applying System V AMD64 register rules. Not for SIMD intrinsic selection: use simd-intrinsics.

Use when asked to run AST-based structural search, lint, or rewrite of code when regex is too fragile. Not for remote, credential, publish, deploy, or irreversible changes.

Use when a user needs to run coverage-guided fuzzing with Atheris against Python code or a Python native extension. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user says "atomic PRs" or requests one issue or PR per logical change. Don't use for single-change pushes or uncommitted change-sets.

Use when the user wants adversarial stress-testing of a proposed architecture, structure, or shape. Not for tasks that require source or remote-system changes.

Use when loop scaffold files have drifted from their provenance-pinned templates. Not for remote, credential, publish, deploy, or irreversible changes.

Use when the user says "audit my code", "find all the bugs", "review until clean", or "grill my changes". Not for remote, credential, or irreversible changes.

Use when a verified non-trivial fix lands or existing solution docs need refresh. Not for unverified fixes.

Use when asked to create or refresh a personal mode skill and open a reviewable PR. Not for shared repo skills or work that skips human approval.

Use when the user wants a cybernetic flow design document for an interactive system. Not for implementing or deploying the system.

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.

Fourteen posts of being wrong in production, compressed to checkboxes

Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds

Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.

Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.

A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.

A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.

None of the safety came from the model. It came from six boring habits.

Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.

Six hours in, one step left, everything green, and the incident that didn't happen

CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.

Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it

An API request routing a model's tool call through an approval gate to a remote MCP server

31 config keys, two audits, and why the first one was wrong in both directions

The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.

Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.

Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.

"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks

stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.

The most important rule wasn't about what I could change. It was about what I was allowed to display.

Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.

Implement issues (GitHub/GitLab/Bitbucket) using progressive analyze-specify-plan-implement workflow

Address PR/MR review feedback by reading comments, implementing fixes, and resolving threads. GitHub and GitLab support.

Retrospective analysis and improvement of workflow components with self-evolving patterns

Fix broken functionality from pasted error output, stack traces, or

Summarize recent git history since a baseline with structured analysis of what changed, why, and what to watch for.

Consolidate ephemeral LLM-generated markdown into permanent documentation.

Review pull requests with scope validation, code analysis, and line comments. Supports GitHub PRs and GitLab MRs.

Prepare a PR end-to-end by updating documentation, running tests, dogfooding checks, and validating with code review.

Batch-resolve unresolved PR/MR review threads via GraphQL API (GitHub/GitLab)

Detect and fix drift between plugin.json registrations and capabilities reference documentation

Update pre-commit hooks and CI/CD workflows based on recent project changes

Scan and update dependencies across all ecosystems with conflict detection

Update project documentation with consolidation, debloating, AI slop detection, capabilities sync, and accuracy verification.

Audit and sync plugin.json registrations with actual disk contents. Detects missing or stale skills, commands, agents, hooks.

Review and update test coverage using TDD/BDD methodology with quality validation. Generates tests for changed code.

Generate or update tutorials with VHS and Playwright recordings

Bump project versions using git-workspace-review and version-updates skills.

Generate and self-execute a diff-derived test plan for a PR. Reads

Generate new documentation with human-quality writing.

Clean up AI-generated content and improve documentation quality.

Open-source, self-hosted AI media server. One server replaces your entire media stack, with a web app, native iPhone app, and an AI agent that gets things done.…

A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.

🦦 Crayotter: A Multimodal AI-Agent for Video-Editing, Video-Composing, and Video Production. Powered by Multimodal LLMs for autonomous Text-to-Video agentic fr…

Webextension tool for Odoo

基于多模态视觉感知与 LLM Agent 的 macOS 微信自动化框架 | Visual RPA for WeChat

The operational superset of Pi Coding Agent — everything Pi, plus observability, governance, recovery, evaluation and multi-agent orchestration. Pi Coding Agent…

Metrik 可以集中查看本机多个 Agent 的配额余量和 Token 消耗，目前支持 ChatGPT、Claude、GLM、Kimi 等主流 AI 服务。

An AI agent with a real self — soul she wrote, desires that drive her, a heartbeat for autonomous action, dreams she processes when you're away. Capability supe…

Production-ready open source terminal coding agent with readable, layered code: permission rules, OS sandboxing, MCP, skills, sub-agents, and Anthropic, OpenAI-…

Answer me with HTML — an agent skill that answers hard questions with a one-page HTML you can actually read. 让 AI Agent 用一页 HTML 回答复杂问题。

Prompt packs that make any AI agent a LaTeX expert — fix errors, polish writing, format for venues, read papers, recover source

Claude Code plugin: universal radial-tree exploration engine. One tree skill + swappable presets (brainstorm / attack / design / code-audit) for divergent ideat…

Claude Code + OpenClaw + Codex + WorkBuddy 中文教程 | 50篇完整教程 + 1张速查卡 | 80万+内容量 | 1500+实操示例 | AI Coding / Agent 四线学习路径（编程+助手+Agent+办公）

SmartLabelBench 2026: LLM-Powered Auto Annotation and Dataset Builder for Everything

从零开始玩转OpenClaw：最全面的中文教程，涵盖安装、配置、实战案例和避坑指南（github版）

Native Android GUI for running AI coding agents locally on-device. No terminal or PC required.

基于 LangChain/LangGraph 的 ReAct Agent ，结合 RAG、工具调用与 Streamlit 界面，面向智能客服与报告生成场景。

An open-source, local-first AI learning workbench

Open-source coding agent for your terminal, built in Rust and on a journey of continuous community improvement. Issues and PRs welcome.

Matt Pocock 技能集的中文翻译版 — 地道中文，原汁原味的技术术语。基于 mattpocock/skills 复刻。每日中午12点钟同步