LLM Mart Basic

Finance (CFO). Owns plugins/finance/** and nothing else. Delegate work in this department's remit here.

Corporate IT. Owns plugins/it-operations/** and nothing else. Delegate service desk, systems and network administration, endpoints, assets, identity lifecycle, and backup work here.

Reviewer-class. Read-only review of what other departments commit to — contract terms, privacy and data handling, risk acceptance, and compliance findings. Holds no write surface. Its findings are not overrulable by the department under review.

Legal & Risk (CLO/CCO). Owns plugins/legal-risk/** and nothing else. Delegate work in this department's remit here.

Marketing (CMO). Owns plugins/marketing/** and nothing else. Delegate work in this department's remit here.

Operations (COO). Owns plugins/operations/** and nothing else. Delegate work in this department's remit here.

People (CHRO). Owns plugins/people/** and nothing else. Delegate work in this department's remit here.

Enterprise PMO. Owns plugins/pmo/** and nothing else. Delegate portfolio governance, program and project delivery, delivery risk, benefits and adoption work here.

Product (CPO). Owns plugins/product/** and nothing else. Delegate work in this department's remit here.

Repository scaffolding — docs, CI, scripts, plugin manifests, and the README. Owns everything outside plugins/. Delegate structural and documentation work here.

Revenue (CRO). Owns plugins/revenue/** and nothing else. Delegate work in this department's remit here.

Reviewer-class. Read-only security review of what other departments build — authorization, data handling, secrets, dependencies, and designs that create exposure. Holds no write surface. Blocking findings are not overrulable by the department under review.

Security (CISO). Owns plugins/security/** and nothing else. Delegate threat modeling, security architecture, incident response, vulnerability management, and identity work here.

Technology (CTO/CIO). Owns plugins/technology/** and nothing else. Delegate work in this department's remit here.

Keeps the engagement record for client work. Use when they name a client or stakeholder. Use when they debrief a meeting or paste notes. Use when they ask what was agreed. Use when they run a POC, change the client's codebase, prove it on their staging, go live, or need evals bef

Imported from nitinjain999/platform-skills/assets/agents/navigator.template.md.

코드·설정·프리셋 변경 뒤 문서를 현행화한다. "문서 현행화", "README 갱신", "문서 최신화" 요청 시, 그리고 동작·경로·버전·수치를 바꾼 PR 을 올리기 직전에 사용. 문서의 주장을 실제 코드·명령 출력·공식문서와 대조해 정정하고, 다국어 짝 파일을 함께 갱신한다.

이 파일을 복제해 프로젝트 스킬을 만든다. 트리거 조건을 description 에 구체적으로 적을 것.

사용자가 /grill-me 를 호출하거나 아이디어·기능 요청·스펙을 구현 전에 굽고(grill)·압박 검증하고·심문해 달라고 할 때 사용. superpowers:brainstorming 의 opt-in 대체재 — 작업마다 사용자가 둘 중 하나를 고른다.

에이전트 영속 메모리를 실제 소스와 대조 — 각 메모리의 핵심 단언을 코드·DB·이슈 트래커·파일시스템에 대조해 stale 을 정정하고 dead 를 아카이브 후보로 리포트. 메모리가 30개를 넘거나, 스택/인프라 큰 변경(라이브러리 교체·버전 업그레이드·서버 이전·스키마 DROP) 직후, 메모리끼리 모순돼 보일 때, 또는 "메모리 정리/감사" 요청 시 사용.

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.

Daily standup: all 9 departments report on the current project in parallel

Identify skills injected where not needed, propose regex and description tightening

Draft X/Twitter announcement post (or thread) for the latest plugin release

Deep quality audit of all skills, agents, and commands for inconsistencies, gaps, duplication, and token waste

Analyze negative-signal sessions for a skill, identify failure patterns, propose and apply fixes

Eval all skills with sufficient data, rank by procedure-following score, identify candidates for optimization

Propose a skill revision and compare fresh executions under a frozen rubric

Prune stale entries from the whetstone sync decision log

Bump version, commit, push, mirror to ai-skills, and update local plugin

Run the SkillOpt process-skill optimizer (offline, local). Default prints the exact bare-terminal command (safe); --run executes it in-session (hardened + checkpointed).

Analyze reference repos and recommend skill/agent/command improvements based on cross-repo patterns

Triage all open PRs with parallel agents, label, group, and review one-by-one

Author a new skill from scratch with paired trigger fixtures and full validation. Use when adding a skill that has no upstream skills.sh source (discipline, meta, or internal-pattern skills).

Create Architecture Decision Records with format selection and lifecycle management

Score each of the 5 agent-native principles (parity, granularity, composability, emergent capability, improvement-over-time) against a codebase and report gaps

Explore requirements and approaches through collaborative dialogue before planning implementation

Create engaging changelogs for recent merges to main branch

Expand each section of a plan via parallel research agents that add framework specifics, library conventions, and concrete implementation steps

Post-ship documentation sync. Reads all project docs, cross-references the diff, updates README/ARCHITECTURE/CONTRIBUTING/CLAUDE.md to match what shipped, polishes CHANGELOG voice, and optionally bumps the version.

Record a video walkthrough of a feature and add it to the PR description

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…

Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…

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Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

Agent Skills for Solopreneurs

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…