LLM Mart Basic

Measure whether a test suite is any good, not only that it passes: branch coverage, mutation score, complexity-times-coverage risk, duplication. Use when adding or reviewing tests on a change that matters, a suite passes but a bug still shipped, coverage is high and confidence is

Decide whether to spawn a subagent, and on which model tier and reasoning effort. Use when planning a fan-out, choosing a subagent model, writing a workflow script's opts.model, authoring an agent definition, setting a repo's cost posture, or when a delegation decision is non-obv

Raise the visual quality of something that already renders: build, screenshot, independent scored critique, fix, against rubrics with hard accessibility, design-token, runtime and asset-licensing gates. Use when asked to make a UI, page, HTML doc, dashboard, game scene or 3D asse

Decide where an instruction belongs and write it there, then sync and lint. Use when asked to add, change or remove a rule, skill, instruction, hook, setting or CLAUDE.md line, to "remember" something that should persist beyond this session, or when a correction should apply to f

Verify a third-party library, asset, model, font, dataset or copied snippet is safe to ship under the project's licensing stance, and record it. Use before adding or upgrading any dependency, before downloading any asset, and before a release.

Write, review or apply a database schema migration without destroying data. Use for any task that creates or modifies a migration file (Alembic, Prisma, Django, Rails, Flyway, raw SQL), and before applying one to a shared environment.

Fence an autonomous or long-running agent loop: the built-in sandbox with network off, or a container with the worktree mounted. Use before any unattended loop, before `execute` autonomy on an unfamiliar repo, and whenever a task pulls untrusted input.

Frame a spike so its result is a decision: the question, the cheapest experiment, a numeric exit criterion, the measured result, the machine it ran on. Use when asked to "spike", "prototype to find out", "de-risk", or "check whether X is feasible", and when writing the spikes sec

Bound what subagents return and what tool output enters the transcript; load when briefing a subagent or reading large output.

Contribute from a fork to a repository you do not own without burning maintainer trust. Use when the working repo has an `upstream` remote, when the user says "open a PR against <someone else's repo>", or before the first commit in any repo the user is a guest in.

Show progress of background Workflow runs: who has returned, who is still working, how much output. Use when the user asks about workflow progress, says "/workflows doesn't work", asks "is the workflow done", "how's the workflow going", "check the workflow", or wants to inspect a

Isolate an agent's work in its own git worktree branched off the default branch, so two agents never land conflicting changes on the shared checkout. Use at the start of any implementation task in a repo where others may also be working, and whenever a repo's instructions say "wo

Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.m

Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corro

Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citat

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Qu

Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGG

Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGG

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel wi

Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated f

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.

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Quick health check

Find pages that swallowed the graph

Rebuild the index

Import an exported chat history

Ingest book or article highlights

Ingest your own finished work

Ingest newsletters without duplicating

Ingest an academic paper

Ingest a PDF

Clip and ingest a web page

Ingest a voice note

Ingest a video or podcast transcript

Ingest new material from raw/ into the wiki

Scaffold a new vault

Install the skills, commands and agents

What do I know about a topic

Find and add missing connections

Audit structure and repair what is mechanical

Merge two pages

Record a dated metrics snapshot

Claude Code skills for Chinese-narration recaps from supported video files, with optional editable JianYing/CapCut draft export. | 用 Claude Code skills 为支持的视频文件…

Drive the Unity Editor from an AI agent or the terminal. The Editor serves MCP itself over HTTP, so there is no second process to run, and the isuzu-unity-cli c…

面向企业的数字员工构建与运行平台：把专业员工的经验、流程与判断标准，固化为可随时上岗、可配置、可审批、可观测的 AI 数字员工。

A local-first, Pi-powered AI agent workspace for Desktop, WebUI, and CLI

660+ muapi-hosted generative-media models plus community-submitted third-party API tools (SEO, enrichment, social, scraping) — one YAML file per entry, browsabl…

A compounding agent OS for recursive agents. Also an open source alternative to Grok Bot and Meta's Muse.

Independent desktop client for OpenCode 2. Manage projects, sessions, parallel agents, requests, and changes on Linux and macOS.

Production agent skills for Claude Code, Cursor, and any SKILL.md harness — Codex fleets, video pipeline, monorepo review bundles, multi-chain explorer.

See and manage what your coding assistants load — skills, commands, subagents, plugins and MCP servers, with real usage.

An Enterprise-Grade Full-Stack RBAC Permission Management System Built with Go + React

Create AI Agents in a No-Code Visual Builder or TypeScript SDK with full 2-way sync. For shipping AI assistants and multi-agent AI workflows.

基于 DeepSeek Harness（DSH）的稳定桌面端，集成Git、内置浏览器与记忆功能 | DeepSeek Harness desktop GUI with local workspaces, Git, browser and memory.

⚡ Control Apache Airflow with natural language via MCP. Chat with your workflows using Claude, GPT, or any LLM — no REST API calls needed. Supports Airflow 2.x…

Open-source CLI, schemas, resolver, and DSH agent tools for DSH Plugin Hub

Pi Coding Agent 中文学习蓝皮书：从安装与第一个可验收任务开始，逐步掌握 Session、Context、Skill、Extension、Subagent 与长期 Agent 工作流。

Self-hosted framework for orchestrating fleets of specialist AI agents — ensemble reasoning and a full agentic coding pipeline, model-agnostic and local-friendl…

Self-hosted, vendor-neutral control plane for your local coding agents (Claude Code & Codex). Run your agents, on any plane.

A practical AI agents handbook covering agent systems, agentic workflows, LangGraph, MCP/A2A, context engineering, agent memory, evaluation, observability, and…

Vibe Coding 从入门到精通教程｜AI 结对编程工作流｜Prompt、Skill、Workflow、上下文管理、codex实战指南

Static security scanner for LLM agents — prompt injection, MCP config auditing, taint analysis. 51 rules mapped to OWASP Agentic Top 10 (2026). Works with LangC…