LLM Mart Basic

Use this skill when reviewing AI-generated unit, functional, API, or end-to-end tests for false confidence, weak assertions, missing risks, or unsafe test behavior; triggers include AI-generated test review and functional test review.

Use this skill when you need evidence-bounded safety policy, abuse categories, refusal/redirect behavior, privacy, escalation, and Human risk decisions; triggers include AI 安全 and AI safety.

Use this skill when you need to verify API contract compatibility, consumer expectations, and schema change risk; triggers include API contract testing.

Use this skill when an API, OpenAPI, or consumer contract needs a quality review before implementation or versioning; triggers include API design review, contract readiness review, and consumer compatibility audit.

Use this skill when you need to review API error shape, status, code, and disclosure behavior against sourced contracts; triggers include API 错误契约测试 and API error contract testing.

Use this skill when you need to assess API retry and duplicate-request behavior against sourced side-effect evidence; triggers include API 幂等性测试 and API idempotency testing.

Use this skill when you need to design evidence-bounded API failure and rejection scenarios; triggers include API 负向测试 and API negative testing.

Use this skill when you need to design API pagination scenarios from ordered data and cursor or offset evidence; triggers include API 分页测试 and API pagination testing.

Use this skill when you need to design evidence-bounded API quota, burst, and recovery scenarios; triggers include API 限流测试 and API rate limit testing.

Use this skill when you need to compare API schemas with sourced request and response evidence; triggers include API Schema 校验 and API schema validation.

Use this skill when you need evidence-bounded api-security-testing analysis and validation preparation; triggers include API 安全测试 and api-security-testing.

Use this skill when you need to parse multi-format API definitions and generate Bruno collections for executable regression; triggers include Bruno collections and Bruno API testing.

Use this skill when you need to design Postman collections, environments, scripts, and Newman-ready API regression plans; triggers include Postman API testing, API testing, and api-test-postman.

Use this skill when you need to parse multi-format API definitions and generate Pytest API automation; triggers include Pytest API tests and API automation with Pytest.

Use this skill when you need to parse multi-format API definitions and generate Rest Assured Java test classes; triggers include Rest Assured, RestAssured, and Java API automation.

Use this skill when you need to parse multi-format API definitions and generate executable Supertest scripts; triggers include Supertest, Node.js API testing, and Supertest automation.

Use this skill when you need to assess API version compatibility from old-client, new-server, and deprecation evidence; triggers include API 版本兼容性测试 and API version compatibility testing.

Use this skill when you need evidence-bounded authentication-testing analysis and validation preparation; triggers include 身份认证测试 and authentication-testing.

Use this skill when you need evidence-bounded authorization-testing analysis and validation preparation; triggers include 授权测试 and authorization-testing.

Use this skill when you need evidence-bounded automation candidates, build/run/maintenance costs, benefit assumptions, time horizon, and sensitivity; triggers include 自动化投资回报 and automation ROI.

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.

Scan for codebase bloat using 3-tier progressive analysis: dead code, duplication, God classes, and documentation waste.

Review the current working diff against the elegant-code decision ladder and propose deletions, honoring the negligence floor.

Suggest tier-1 filter commands for a log file before any compression or paste. Anchors on the log-debugging-hygiene module.

Analyze and optimize context window usage using MECW principles

Remove dead code, duplicate files, and unused dependencies with user approval at each step. Backs up before deleting.

The ONLY way to stop the egregore. Human-initiated graceful shutdown that saves all state.

Install the egregore watchdog daemon for automatic session relaunching

Show current egregore state and progress

Summon the egregore to autonomously process work items through the full development lifecycle. Runs indefinitely by default until dismissed.

Remove the egregore watchdog daemon and clean up files

Add or edit a knowledge annotation

Rebuild the knowledge base from the current codebase

Build, search, and query the code knowledge graph

Start or resume a guided onboarding path

Show challenge accuracy stats, weak areas, and streak

Run an ad-hoc gauntlet challenge session (5 questions, random scope)

Interactive interface to enable/disable rules

Convert Python SDK hooks to declarative rules

Display help and documentation

Create behavioral rules to prevent unwanted actions

PiG (Pi in Go) is a faithful Go port of upstream Pi, the TypeScript codebase behind the Pi coding agent. It is a parity-bound translation, not a rewrite: upstre…

An AI Agent that lives in your pocket. Local-first and privacy focused.

Unofficial skill that teaches coding agents to build with TypeSafe AI's Jev: typed decisions, calibrated confidence, and prior art from 150+ community projects.

Adaptive Test-time Learning and Autonomous Specialization

Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests

Knowledge Management for Humans and Agents

Open-source Claude Cowork / Codex / WorkBuddy alternative — a local-first AI office agent that turns one request into real PPTX, DOCX, XLSX and HTML files. Runs…

DeepAgent Code: AI coding agent with persistent memory and control plane

Awesome Jev — evidence-graded index of TypeSafe System One: SDKs, MCP tools, agents, apps and open models. 20 languages, rebuilt every 2 hours.

CLI for Telegram — agent-friendly, daemon-based, with webhook event push.

AI deep-research agent that turns any question into a cited report: plans searches, reads real sources, verifies evidence. Self-hosted, multi-provider, Docker-r…

Event-stream AI Agent framework for building your persona bot 🍊

Give the agent a machine. Just not yours. Each AI coding agent gets its own isolated machine with root, Docker, and systemd - active defense detects and stops t…

Local Emperor-style AI agent with Vue WebUI, multi-provider LLMs, streaming chat, tools, skills, memory, and token telemetry.

Open-source AI reverse-engineering agent platform and MCP server for Ghidra, Frida, x64dbg and Rizin — automated PE/APK/binary analysis, CTF and malware researc…

"Never send a human to do a machine's job" - Open Source AI hacking agent

Prismer Cloud

My Personal Blog (Robotics)

Tau Coding Agent - like Pi, but twice as much

Open-source alternative to OpenAI Dots: self-hosted AI chat, tools, approvals, connectors, and computer tasks.