LLM Mart Basic

Use when the user is explicitly working with Quarto, .qmd files, _quarto.yml, Quarto projects, or Quarto features such as callouts, cross-references, citations, Mermaid diagrams, extensions, websites, books, presentations, and reports. Also use for explicit migration from or comp

Deploy or publish Python and R content to a Posit Connect server using rsconnect-python or the R rsconnect package. Handles interactive apps and dashboards, web APIs, rendered documents, and prepared bundles/manifests. Use whenever the user asks to deploy, publish, or redeploy co

Guide for drafting issue closure and decline responses as an open-source package maintainer. Use when helping compose a reply that says "no" to a feature request, closes an issue as won't-fix, redirects a user to a different package, explains why a design choice is intentional, o

Use when the user wants to find, evaluate, audit, or install an open-source AI agent skill or MCP server — e.g. "find an MCP server for Postgres", "is this skill safe to install", "what should I use to scrape a website". Searches a quality-scored, security-graded catalog of ~20K

Generator-evaluator separation and review methodology — loaded by review agents to enforce fresh-context review discipline and gate verdicts; findings from the code, security, and docs reviewers are formatted per the conventional-comments skill. Trigger on "review this diff", "re

Adversarially review a technical design document with fresh context. Dispatches the built-in read-only `Explore` subagent (clean context, no shared history with the design-author) against `docs/plans/<id>/design.md` and presents its verdict — APPROVE, REQUEST CHANGES, or COMMENT.

Tear down local and remote branch state after a pull request is finished, in one of two modes. Mode A (merged): verify the PR actually merged, remove the branch's worktree, resync the default branch, and delete the local branch. Mode B (closed / abandoned): close the PR(s), then

Bring a feature branch up to date with its base without changing what the branch does: capture a pre-rebase check baseline, fetch, rebase onto the latest base, resolve each conflict from both sides' intent with the rationale recorded to disk, re-run the same checks, and treat any

Watch your own pull request for review feedback: undraft it when the cue clearly says it is ready (an ambiguous cue watches the draft), take a baseline snapshot, then poll GitHub in ~31-minute cycles for up to 24 hours and triage new feedback as it arrives — inline review threads

Watch a pull request you are reviewing until your feedback is settled, re-review each settlement, then approve once: poll GitHub in ~31-minute cycles for up to 24 hours until every review thread you opened is resolved and every plain PR comment you posted has a later push behind

Land a reviewed pull request: discover the open PR for the current branch, push any unpushed commits, wait for CI to go green, then squash-merge it so the PR title (which may carry a version) lands as the commit subject. Handles a PR that has fallen behind its base (rebase + forc

Decide the approach before any code is written. The design-author drafts the ~200-line design document, resolving its own open questions autonomously as recorded assumptions, then an adversarial design review gates advancement. Trigger on "design this", "let's align on the approa

Compressed bug-fix pipeline — reproduce, write failing test, minimal fix, verify, and open a draft PR. Skips Question/Research/Design/Structure/Plan phases. Invoke ONLY on explicit pipeline intent — the user says "run the bug-fix pipeline", "team-fix this bug", or runs "/team-fix

Execute the implementation phase. Includes test-first sub-step (writing failing tests, mechanical confirmation gate) and adversarial verification (5 parallel reviewers with hard-gate retry loop). Trigger on "implement this", "execute the plan", or "/team-implement".

Produce the tactical implementation plan from the structure. The plan is an autonomous artifact for the implementer — no approval gate at this phase (the design was already gated by the adversarial design review). Trigger on "plan the implementation", "spell out the steps", or "/

Open the pull request after verification passes. Updates the changelog, optionally surfaces the tracking ticket, and closes out the topic. Trigger on "open the PR", "open a draft PR", or "/team-pr". To land/merge a reviewed PR (wait for CI, then squash-merge) use the separate /sh

Decompose a feature description, ticket, or issue link into the QRSPI Question artifacts (task.md, questions.md). Trigger on "shape this idea", "decompose this task", or "/team-question".

Research a codebase area before making changes. Dispatches parallel read-only agents (file-finder + researcher) that read questions.md only — never task.md. Trigger on "research this", "explore the codebase for", or "/team-research".

Break the reviewed design into vertical slices with verification checkpoints. Runs autonomously and advances to PLAN — no approval gate. Trigger on "slice this up", "break the design into steps", or "/team-structure".

Prepare one or more isolated git worktrees — one per repository the topic touches. Router action — no agent. Trigger on "set up the worktree", "isolate this work", or "/team-worktree".

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.

An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间，让人与智能体在此协作

Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.

The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.

Transform and optimize your markdown documentation for Large Language Models (LLMs) and RAG systems. Generate llms.txt automatically.

合乎周礼：DeepSeek-powered Zhouli-style Chinese translator, web app, and distributable Skill package.

This is a fork of the https://dockbox.dev project I made

🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly.

A Python framework for modular, self-contained skill management for machines.

ADHD — a skill for coding agents. Tree-of-thought with pruning, built on the Claude & Codex Agent SDK. Fans out parallel divergent thoughts under different cogn…

AI Agent 驱动的开源可自部署视频工作台：将小说与剧本转为角色、场景、道具资产、分镜、视频和剪映草稿，支持跨镜头一致性、多供应商与费用追踪 | Self-hosted AI video workspace for stories, storyboards and short-form video producti…

DeepSeek Harness Desktop App: a local AI desktop workspace for DSH Sessions, projects, files, web research, plugins, and Office artifacts.

Autonomous Offensive Security, Bug Bounty & Red Teaming Agent Framework powered by Hermes Agent, specialized reasoning skills, and multi-model LLM orchestration…

⌥ Coding agent with the IDE wired in

Supercharge AI Agents, Safely

X (Twitter) Scraper API and X API Alternative. You do not need an official X developer account. You do not need to connect or use an X account for supported scr…

My AI Stand. Realtime by day, rewriting itself by night. Summon my AI superpower.

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

观澜 / Guanlan：AI Agent 的中文互联网研究、阅读与信源路由工具。

Mac Agent for macOS 26: the agentic AI harness for your Mac Desktop. Computer use, automation, scripting, coding, and more. Powered by 18+ providers across loca…

Semantic version control => entity-level diffs, blame, and impact analysis on top of git. 28 languages via tree-sitter. Built for coding agents.