LLM Mart Basic

缺陷修复方法论唯一入口：口喷 bug 定位直修；implement-test-review 循环内的反馈修复也走本 skill（循环内分支，原 impl worker 执行）。触发：修 bug / 有个 bug / 报错了 / 行为不对 / fix / /eo-fix。 NOT FOR: 明确的业务变更（走 /eo-change）。

在 /clear 之前生成最小可恢复快照到 tmp/eo/handoff/<topic>.md，让下一个会话载入这一个文件就能从当前节点继续。优先记录任务状态、关键口径、下一步分叉，主动丢弃探索过程。触发：handoff / 存一下进度 / 我要 clear / /eo-handoff。 NOT FOR: 机械压缩对话流（用内置 /compact）。

按 change.md 的 TODO 分批落地代码，批末自验对应 AC。触发：实现 / 写代码 / implement / /eo-implement。 NOT FOR: bug 与反馈修复（口喷 bug 与 test/review/acceptance 循环内反馈一律走 /eo-fix）；变更起草（走 /eo-change）。

eo 流程总控：按用户意图圈一段（入口节点 → 出口节点 → 收敛标准），把 eo-change / eo-implement / eo-archive 及可选闸门（eo-change-review / eo-test / eo-review）派发到可插拔执行基底上推进至收敛，窗口化汇报进度。触发：eo-loop / 串起来跑 / 循环推进到收敛 / 总控调度 / /eo-loop。 NOT FOR: 单点动作（直接调对应 eo-* skill）；派出去不再监督的完全交接（orca-cli full handoff）；bug 口喷（/eo-fix）。

eo-skills 在当前仓库的总入口：生成 .eo-project.json、初始化项目管理侧（roadmap）和代码侧最小骨架（eo-doc/），以及 agent 配置注入。触发：启动项目 / 初始化项目 / 新建项目 / /eo-project-init。

项目记忆的统一写入口：经验教训（lessons/）与关键决策（decisions/），带 INDEX 与检索锚点，供 eo-change / eo-fix / eo-recall 消费。通过 .eo-project.json 定位。触发（仅用户明确要求记录时）：把这个坑记下来 / 记条经验 / lesson learned / 把这个决策记下来 / 记录决策 / reindex lessons / reindex decisions / /eo-project-record。NOT FOR: 对话提到踩坑或决策但用户未要求记录；待办类（走 /eo-bac

只读的回忆与解释入口，分层作答带出处。触发：这个功能当时怎么设计的 / 这段逻辑怎么实现的 / 当初为什么这么定 / 帮我回忆 / recall / /eo-recall。 NOT FOR: 修 bug（/eo-fix）、发起变更（/eo-change）、维护文档（/eo-doc-manager）。

按需代码审查：风险信号命中或用户点名时，对已实施代码做 AC 逐条核对 + 代码质量审查，产简版 review.md（P0/P1/P2）。触发：review / 代码审查 / 再找双眼睛看看 / /eo-review。 NOT FOR: change 方案审查（/eo-change-review，代码还没写时用）；默认主路（无信号时不强制）。

按需独立测试视角：风险信号命中或用户点名时，对已实现 change 做测试审计、补缺与重验证，产简版 test.md。严禁修改业务代码。触发：找双新眼睛跑测试 / 独立验证 / 给这个 change 补测试 / /eo-test。 NOT FOR: 与 change 无关的日常跑测试或补单测（直接做即可，不产报告）；默认主路的自验（归 eo-implement）。

接口级测试时使用——从 OpenAPI/Swagger 文档或用例 Schema 中可自动化的接口用例出发，覆盖参数、边界、鉴权、幂等、并发、错误响应与数据一致性，产出可执行的 API 测试脚本与运行结果；含接口压测承接（k6，类型矩阵轴 1 执行层）。不用于：Web UI 流程（automated-e2e-testing）、手动用例编写（test-case-writing）。

将手动测试用例转为 Playwright E2E 测试并执行时使用；含写自动化前的业务熟悉踩点、Page Object/Helper 编写、执行中的 Bug 证据收集与报告条目记录。不用于：纯 API 接口测试（api-testing）、以理解系统为目的的独立探索会话（exploratory-testing）、已确认 Bug 的根因分析（bug-analysis）。

对已确认的 Bug 做根因定位、影响分析、回归建议时使用——复现 → 读代码定位根因 → 影响五面分析 → 回归建议，条目（根因/影响/Severity 依据/修复建议）追加进测试报告。不用于：仅收集 Bug 证据（automated-e2e-testing / api-testing）、疑似未定性缺陷（test-case-writing 的 Cx 记录）。

qa-skills 共享知识库，安装依赖单元（非触发 skill）：承载被其余 10 个 skill 以相对路径引用的方法、规则、模板与脚本（可执行性标准、证据分级、风险模型、类型决策矩阵等）。仅在 qa-skills 系列 skill 工作流中被引用读取；任何具体测试任务都不要独立触发本 skill，独立使用无意义。通过 npx skills 等安装器单独安装其他 qa-skills skill 时，必须同时安装本 skill，否则引用路径断裂。

需求不完整、系统陌生、文档不足时，发起以理解系统/发现风险为目的的独立探索式测试会话时使用——charter 驱动（目标 → 探索 → 记录），产出探索笔记（系统理解/风险清单/测试想法）作为需求建模输入或独立交付。不用于：为写自动化踩点的小规模探索（automated-e2e-testing 工作流零）、按既有用例执行（执行类 skill）。

端到端测试的唯一入口：用户说"帮我测试这个需求/功能"、"把这个功能完整测一遍"时，编排需求理解→测试策略（风险与类型决策）→用例→审查→执行→Bug 分析→回归→报告的完整流水线，产出落盘、可断点续跑。只要单阶段产出（如"帮我审一下这份用例"）→ 直接用对应阶段 skill，不用本 skill。

代码变更（diff/Bug 修复/需求变更）后判断应回归哪些测试时使用——沿"改动文件 → 改动函数 → 受影响功能 → 受影响用例"分析链，基于用例 Schema 的追溯映射产出分级回归清单。不用于：用例文件本身的增量修改（test-case-writing）、长期回归策略（test-strategy）。

系统性建模某个需求/系统时使用——从 PRD、设计/API 文档、Bug、Issue、代码中提炼目标、范围、角色、规则、异常、依赖与不明确项，产出结构化需求模型（含澄清记录与用户裁决）。不用于：已有需求模型直接写用例（test-case-writing）、"怎么测"的策略决策（test-strategy）、端到端流水线（qa）。

审查已有测试用例（存量资产、他人编写、AI 产出）的覆盖与可执行性时使用——先建可测点基准，再独立评估覆盖、可执行性与正确性，直接修订用例文件并留审查记录。不用于：从零写用例（test-case-writing）、写时自审（其阶段四）、端到端流水线（qa）。

从需求文档、API 文档、Bug 报告或代码仓库产出可执行的手动测试用例（markmap）时使用——代码优先：索取仓库、先代码审查找潜在 bug 再写用例，并抽取机器可读 Schema。不用于：需求建模（requirement-analysis）、测试策略（test-strategy）、独立审查（test-case-review）、自动化脚本（automated-e2e-testing / api-testing）。

回答"这个功能应该怎么测"时使用——风险评级挂证据（Risk Map），翻译成功能域+类型域两域范围与深度：类型域十轴全轴必答（脚本扫描信号+预填修订），include挂信号、exclude挂理由、full有预算上限。不用于：已有策略直接写用例（test-case-writing）、需求建模（requirement-analysis）、端到端流水线（qa）。

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)

Triage raw inbox notes into reviewed repository destinations without deleting their sources.

Audit and rewrite content to remove AI writing patterns

Log a completed piece of content to content/log.md after the user confirms it was published.

Validate a dream artifact, review each proposal, and apply only individually accepted changes.

Run a curator pass against the validated memory directory and produce a proposal artifact.

End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work

Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.

Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.

Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.

Scan multi-session drift and offer individually reviewed fixes only after explicit approval.

Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.

Guided onboarding or import for durable workspace context

Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers

Create a morning heartbeat from repository state and update the local heartbeat log.

Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session

Maintainer-only. Find every file that would newly ship to adopters, classify each one against the written distribution-boundary categories, default to withhold on no clean match, and ask the maintainer only where the taxonomy does not settle it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.

Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.

Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.

Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.

Open-source MCP servers for Latin American commerce — Pix, NF-e, banking, fiscal, logistics, and messaging across Brazil, Mexico, Argentina, Colombia, Chile, an…

pi coding agent in a technicolor web trenchcoat

Open-source AI Search Intelligence Platform — track, analyze, and improve AI visibility, citations, prompts, competitors, and content opportunities across ChatG…

Open-source toolkit for the QVeris capability routing network: CLI, MCP server, Python SDK, skills, and REST API docs for agents to discover, inspect, call, and…

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Offline Codex Desktop migration for Windows and macOS.

LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashbo…

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The LLM Anti-Framework

A general-purpose AIGC video engine: script to finished film in one pipeline — dramas, ads, product videos, otome games, and more. | 通用 AIGC 视频引擎 —— 从剧本到成片一条流水线…

GTM agent for technical founders. Pay-per-result. Signed receipts. Two surfaces: terminal CLI + local web dashboard.

Desktop automation MCP server — computer use for any AI agent: control screen, windows, mouse/keyboard, and Chrome via Model Context Protocol (stdio)

DeepSeek Harness (dsh) Windows desktop client - bundled Node.js + dsh CLI, one-click launch

Open-source, real-time collaborative AI canvas - notes, mini-apps, and agents on one infinite board.

MCPSafari: Native Safari MCP Server for AI Agents

197 bioinformatics & life science skills for Claude Code and AI agents — BixBench 92.0% accuracy. RNA-seq, single-cell, drug discovery, proteomics, and more. Po…

The AI agent with a wallet — spends USDC autonomously to get real work done. Apache-2.0, TypeScript.

The fastest browser for AI agents to run browser automation, built for sharing your logged-in browser state with your AI agents, like Codex or Claude Code, with…

Ouroboros — self-creating AI agent. Born Feb 16, 2026.