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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。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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：