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知识库管理、摄取、同步和跨多个存储层（本地文件、MCP内存、向量存储、Git仓库）的检索。当用户想要保存、组织、同步、去重或搜索其知识系统时使用。
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Use after a complex task, failure, or when reviewing what was learned. Teaches how to write growth logs that extract reusable patterns — not diary entries.
Design a goal-oriented agent loop, and review it for the ways loops go wrong — spinning and burning tokens, Goodhart-gaming the verifier, or running a wrong answer to completion. Two actions: (1) WRITE a loop — gate whether to build it, define a machine-decidable goal, pick the loop type, pick a skeleton; (2) REVIEW a loop — run it past five failure modes plus decidability, boundaries, fallback, judge independence, and keep-judgment-with-the-human red lines. Use when designing an autonomous agent loop, or when you already have one and worry it will spin, cheat, or run a wrong answer to the end. Complements the mechanism-layer loop skills (autonomous-loops, continuous-agent-loop) by covering the judgment layer they don't. 中文触发：写 loop、设计 loop、做一个 loop、检查 loop 对不对、loop 体检、loop 会不会跑飞、可判定目标、五个崩法、plan build judge。English triggers: design an agent loop, write a loop, check a loop, loop review, prevent a runaway loop, goal-oriented loop, decidable goal, plan/build/judge.
Stop hook that blocks Claude from finishing until quality checks pass. Detects rationalization patterns (surface text heuristics), stale learning logs (filesystem mtime), and low disk space. Complements self-audit by mechanically enforcing learning capture habits.
React Native and Expo app patterns — Expo Router navigation, state separation (server/client/route/form), TanStack Query data fetching with Zod, performant lists, NativeWind/StyleSheet styling, native APIs, and secure storage. Use when building or editing React Native / Expo screens, components, navigation, or data layers.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Use this skill when writing new features, fixing bugs, or refactoring code. Enforces test-driven development with 80%+ coverage including unit, integration, and E2E tests.
| name | knowledge-ops |
| description | 知识库管理、摄取、同步和跨多个存储层（本地文件、MCP内存、向量存储、Git仓库）的检索。当用户想要保存、组织、同步、去重或搜索其知识系统时使用。 |
| origin | ECC |
管理一个多层知识系统，用于跨多个存储库进行知识的摄取、组织、同步和检索。
推荐使用实时工作区模型：
~/.claude/projects/*/memory/当需要捕获新知识时：
这是什么类型的知识？
检查此知识是否已存在：
写入适当的层级：
更新任何相关的索引或摘要文件。
定期将会话历史同步到知识库：
将重要的工作区配置和脚本镜像到知识库：
当信息影响活跃执行时：
将来自多个来源的知识汇集到一处：
# 短期：当前会话上下文
使用 TodoWrite 进行会话内任务追踪
# 中期：项目记忆文件
写入 ~/.claude/projects/*/memory/ 以实现跨会话回溯
# 长期：GitHub / Linear / 知识库
将活跃执行事实置于 GitHub + Linear
将持久化综合上下文置于知识库仓库
# 语义层：MCP 知识图谱
使用 mcp__memory__create_entities 创建永久结构化数据
使用 mcp__memory__create_relations 进行关系映射
使用 mcp__memory__add_observations 添加关于已知实体的新事实
使用 mcp__memory__search_nodes 查找已有知识
在完成任何知识操作之前：