LLM Mart Basic

Live debugging workflow - reproduce first, DebugMCP breakpoints, conversational state questions, isolated repair branch, before/after tests, repair report. Use for /debug and hard production bugs.

Multi-source web research with comparison, source quality checks, and a cited report. Use for market scans, tech decisions, due diligence, or “what do sources say about X”.

Audit the software supply chain - vulnerable and outdated dependencies, CVEs, lockfile integrity, license risks, typosquatting, and SBOM generation. Use for /lineage, dependency reviews, or "are our packages safe?" questions.

Full-funnel marketing orchestrator - strategy, content, SEO, social, email, paid, and measurement. Use as the entry point for any broad marketing request, then delegate to specialist skills.

Prepare discovery call questions, live-call structure, and post-call summaries that feed qualification and proposals. Use before and after prospect meetings.

Income / dividend tilt for the Trading Agent OS. Favor durable cash return over narrative growth.

Build, run, inspect, and diagnose Docker containers and Compose stacks safely. Use when debugging images, ports, volumes, or container logs.

Visual theme catalog and template handling for generated documents (PPTX, DOCX, PDF, XLSX). Use whenever a document request names a theme, or provides a template file to adapt.

Create, read, and edit Word (.docx) files - styles, tables, images, headers, and templates - using python-docx. Use whenever a deliverable must be a Word document.

Earnings-event skill for the Trading Agent OS. Fade or follow only with a thesis and a hard stop.

Design newsletters, nurture sequences, and lifecycle emails - structure, cadence, deliverability, and measurement. Use for any recurring or automated email program.

Write professional emails - commercial, administrative, follow-ups, difficult messages - with the right tone in FR/EN/AR. Use for any one-to-one email that matters.

Research companies and people - leadership, revenue signals, tech stack, contacts, and sources. Use for sales intel, partner diligence, or competitive briefs.

Verify claims, statistics, quotes, and citations before publication - with sources and confidence levels. Use before publishing anything containing factual assertions.

Write J+3 and J+7 recruiter follow-ups from the Career inbox and pipeline. Use when an application is silent or the user asks for a relance.

Build or defend a freelance daily rate (TJM) from the Career profile, market, and mission scope. Use for freelance pricing, rate cards, and counter-offers.

Build, run, and debug frontend, backend, and database projects end to end in the workspace (plan, investigate, code, execute, verify). Use for Dev mode sessions, scaffolding apps, fixing bugs, and running dev servers.

Optimize content to be cited by ChatGPT, Gemini, Perplexity, and AI Overviews (GEO). Use when the user wants visibility in generative answers, not just blue links.

Full git workflow for developers: status, staging, commits, branches, push/pull, merge/rebase, conflict resolution, stash, history (log/blame/bisect), remotes, tags, worktrees, cherry-pick, and recovery with reflog. The everyday operations live in the git tool; this skill covers

Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.

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.