LLM Mart Basic

Use when the user wants lint, format, and type errors fixed in the current changes. Reads CLAUDE.md for the repo's lint/format/type-check commands, runs each, and fixes only style/format/type issues — no behavior changes. Also known as `klaussy-fix`.

Use when the user is tired of approving the same routine dev work ("stop asking me yes", "allow the normal dev tools", "grant permissions"). Detects the repo's stack and writes a curated allow-list into the agent's own local permission file so the basics stop prompting — reading,

Use whenever prose, a comment, a doc, a PR or commit body, or a file's text should read like a human engineer wrote it instead of an AI — "humanize this", "make it sound less like a bot", "does this read AI-written?", or before shipping prose a human will read. Rewrites in four p

Use when the user pastes a ticket, design doc, or task description and wants it implemented. Multi-phase flow — understand, investigate (in plan mode), plan, implement, verify. Enforces strict scope rules and writes failing tests first for bug fixes. Also known as `klaussy-implem

Use when the user wants a new git worktree created for a task. Picks a kebab-case branch name with a fix/feat/chore/docs/refactor prefix, runs `git worktree add` from the configured base branch, and reports the new path. Also known as `klaussy-new-worktree`.

Use when the user wants a plan for a non-trivial task in this repo. Runs discovery, parallel exploration of the codebase, clarifying questions and parallel architectures, then checks the plan against the requirements and against the repo itself (an adversarial sub-agent) before w

Use when reviewing a staged diff or an about-to-commit/push change for last-mile issues — silent failures, leaked secrets, debug leftovers, blatant correctness landmines, and excessive/narrating comments. Reports findings on the changed lines only; it does not refactor or rewrite

Use when the user wants the current change QA'd and PR-ready evidence captured. Classifies the diff and runs the verification that actually fits it — screen recordings and screenshots for UI/frontend changes, endpoint or e2e runs for backend, command output for a CLI, tests for a

Use when the user wants to restructure code while preserving behavior exactly. Establishes a passing test baseline first, then makes incremental moves that each leave the suite green. Refuses to change behavior and structure in the same step. Also known as `klaussy-refactor`.

Use when the user wants to cut a release — bump the version, update the changelog from conventional commits, and tag. Detects where the version lives, derives the next version from the commits since the last tag, and stages the release locally; it does not push or publish unless

Use when the user hands you a task definition and wants the ENTIRE development loop run end-to-end — plan, implement, review and fix, QA the change with evidence appropriate to it, open a humanized PR, then poll CI and code review, fixing and resolving until the PR is green and c

Use when the user has a stack of dependent branches or PRs that needs rebasing — the base branch moved, the bottom branch merged, or a mid-stack branch was amended. Derives the parent/child chain from git ancestry, rebases each branch onto its new parent, and force-pushes with a

Use when the user wants a thorough PR or branch review. Triages by diff size — small PRs get a single-pass review, large PRs fan out to parallel sub-agents (correctness, architecture, security, scope, and an Agentic & Evals lens that activates on AI/agent code) with a validation

Use when the user wants to run, start, or launch this project's app — to watch a change work end-to-end, reproduce behavior in the real app, or smoke-test locally. Finds the run command from CLAUDE.md and drives the app; it does not write features or fix bugs. Also known as `klau

Use when the user wants a focused security pass over the current change — scanning the branch diff for leaked secrets, injection and SSRF, broken access control, unsafe deserialization, and newly added or vulnerable dependencies. Narrower and deeper than the general review skill:

Use right before declaring an implementation done — a last-pass review of your OWN uncommitted change against a fixed checklist (reuse, stdlib, comments, dead code, tests, scope). Catches the things that make a diff read as AI-written before a human ever sees it. Reviews the curr

Use when the user explicitly wants to turn clean, human prose INTO maximal AI slop — as a joke, a demo of what AI tells look like, or to stress-test the humanize skill by feeding it the worst input imaginable. The evil twin of humanize — it adds every tell humanize strips. For la

Use when a change is too large to review in one pass and should ship as a stack of dependent PRs instead. Strips comment bloat so the size is honest, proposes layers from the import graph, then builds the branch chain, opens one request per layer targeting the layer below, and re

Use when the user wants tests written for current changes (uncommitted diff or recent feature). Matches the repo's existing test framework, fixtures, and assertion style. Covers happy path, edge cases, and error paths without over-mocking. Also known as `klaussy-test`.

Use when the user wants stale or unused git worktrees cleaned up. Classifies every worktree as merged, abandoned, dirty, or in use, proposes which to remove, and on confirmation removes them with `git worktree remove` and prunes the leftovers. Never forces a removal and never tou

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.

Options Foundations — options and derivatives analysis

Technical Execution — options and derivatives analysis

Volatility Trading — options and derivatives analysis

Long Short Equity — portfolio strategy analysis

Portfolio Hedging — portfolio strategy analysis

Position Sizing — portfolio strategy analysis

Dividend Discount Model — multi-stage DDM for mature dividend payers and financials

PEG valuation — P/E ÷ Growth Rate with Peter Lynch thresholds and sector comparison

Financial ratio analysis — 24 ratios across 6 categories with cross-company comparison

Residual Income / EVA — Book Value + PV of future economic profit for banks and insurers

Reverse DCF — solve for implied growth rate/margins from current price

agentii.challenge — the spec 046 kit command. Use the Skill tool to run agentii:challenge on this workspace.

agentii.clarify — the spec 046 kit command. Use the Skill tool to run agentii:clarify on this workspace.

agentii.constitution — the spec 046 kit command. Use the Skill tool to run agentii:constitution on this workspace.

agentii.converge — the spec 046 kit command. Use the Skill tool to run agentii:converge on this workspace.

agentii.implement — the spec 046 kit command. Use the Skill tool to run agentii:implement on this workspace.

agentii.plan — the spec 046 kit command. Use the Skill tool to run agentii:plan on this workspace.

agentii.specify — the spec 046 kit command. Use the Skill tool to run agentii:specify on this workspace.

agentii.tasks — the spec 046 kit command. Use the Skill tool to run agentii:tasks on this workspace.

Drive a PR to merge — rebase, fix CI, resolve review comments, update title/description, merge when green

Your car as a chat-room agent: Raspberry Pi 5 + dashcam + local AI. CodeWatch's sibling for the garage.

The enterprise gateway for autonomous agents. Identity management, per-channel isolation, credential vault, per-session tamper-evident audit log.

AI agent for Home Assistant — talk to your home, create automations in plain language, analyze cameras with face recognition, and get proactive anomaly alerts.…

A desktop app to prototype agent ideas, inspect every harness step, replay failures, and evaluate performance, all in one place. Local-first, cloud-ready for ma…

Local-first AI memory you can see, edit, and override — portable across Claude Code, Codex, Cursor, Windsurf, and other MCP coding tools.

Web & desktop (Electron) client for the OCTO open workplace — one React + TypeScript codebase shipping browser and PC surfaces, with first-class AI agent UX.

同花顺官方 A股金融数据服务，提供股票实时行情、历史行情、财务报表、指数、板块、涨停等数据，适用于 AI Agent、量化研究和应用开发，支持 API、MCP、CLI 和 Python。Official Tonghuashun (HiThink) A-share financial data service provi…

Open-source desktop SQL workspace for PostgreSQL, MySQL/MariaDB, SQLite and 15+ more databases like DuckDB, ClickHouse, Redis and Firestore. Built-in MCP server…

🤖 Local-assist BOSS Zhipin CLI for AI agents — search, welfare filtering, shortlist, JSON-envelope output; low-risk & compliant by default.

The local-first sidebar AI agent for ComfyUI — runs on your own Claude OR ChatGPT subscription (no API keys, no extra LLM costs). Drives your live graph: edits,…

Persistent memory for Claude Code — identity, context, and continuity across sessions

🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI…

A curated list of plugins, skills, MCP servers, patch/profile layers, orchestrators & UIs for DeepSeek Harness (DSH). Visualization · PPT · Coding · Agents · Lo…

World Memory Protocol.

Run your own organization of agents.

The most RAM efficient harness

AI-native, free, open-source alternative to Jira, Trello, ClickUp & Monday. Built for Scrum teams where humans and AI agents collaborate as equals — on the same…

Free open-source desktop app that scrapes JAV metadata and generates NFO + cover art for Jellyfin, Emby & Kodi. No Docker, no CLI — one-click install on Windows…

A cross-platform desktop app to manage Agent Skills in one place and sync them to multiple AI coding tools’ global skills directories — “Install once, sync ever…

天枢 (Tianshu) 是一个基于harness工程的终端编程智能体运行时（Tui X Gui），针对DeepSeek V4 做了前缀缓存工程优化（长会话实测稳态命中率 97–99%）和深度适配。它跳出了传统 AI 编程助手把大模型仅当成“工具”的局限，基于认知虚拟机 (CVM)、自感知层和信息素（Stigmergy…