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Install in OpenClaw
/install adaptive-rag-engine
Description
Adaptive RAG 引擎 — 从线性检索到自主认知循环。集成胶囊预筛选、智能路由、CRAG纠错、L3校验。当需要搜索记忆/检索信息/回答复杂问题时触发。关键词：RAG、检索、记忆搜索、向量检索、Agentic RAG、CRAG。
README (SKILL.md)
🧠 Adaptive RAG Engine v1.0
不是管道，是认知循环。
核心能力
| 能力 | 说明 | 触发方式 |
|---|---|---|
| Adaptive Router | 判断是否需要检索，简单问题直接答 | 自动（每次 memory_search 前） |
| Capsule Pre-filter | 42个胶囊标题预匹配，缩小范围 90% | 自动（Router 通过后） |
| Vector Search | bge-m3 向量检索 Top-20 | memory_search 工具 |
| LLM Re-rank | 语义重排序 Top-20 → Top-5 | 检索后自动 |
| CRAG Evaluator | 质量评估 + 低分补搜 | 检索后自动 |
| L3 Gatekeeper | 输出前与核心洞察校验 | 生成前自动 |
| Memory Bridge | Active Memory ↔ Phoenix 双向桥接 | 对话结束时 |
使用流程
对于 CEO（小鸟文书）
此 Skill 是协议层，不需要显式调用。它通过以下方式生效：
- 读取协议文件:
rules/adaptive-rag-protocol.md— 获取完整决策树 - 读取胶囊索引:
memory/topics/.capsule-index.json— 获取 42 个胶囊元数据 - 按决策树执行: 每次需要记忆时，走 Router → Pre-filter → Search → Rank → CRAG → Generate → Verify
对于 SubAgent
SubAgent 在执行任务时：
- 先判断任务类型 → 决定是否需要检索
- 需要检索时 → 先做胶囊预筛选
- 检索结果 → 自行判断质量（CRAG 思维）
- 输出前 → 自检是否与已知信息矛盾
关键文件
| 文件 | 用途 |
|---|---|
rules/adaptive-rag-protocol.md |
完整协议（决策树/分类/bridge/CRAG/L3） |
memory/topics/.capsule-index.json |
42个胶囊的结构化索引 |
scripts/build-capsule-index.py |
重建胶囊索引脚本 |
scripts/rag-evaluate.py |
CRAG 质量评估脚本 |
快速命令
# 重建胶囊索引
python3 ~/.openclaw/workspace/skills/adaptive-rag-engine/scripts/build-capsule-index.py
# 评估检索质量
python3 ~/.openclaw/workspace/skills/adaptive-rag-engine/scripts/rag-evaluate.py --query "xxx" --results "result1, result2"
与其他 Skill 的关系
- dynamic-rag-capsule — Context 管理层面的胶囊化（对话太长时压缩）
- phoenix-memory — 记忆存储和衰减管理（四层架构）
- 本 Skill — 检索策略和质量控制（怎么搜、搜到后怎么办）
三者关系：phoenix-memory 是仓库，dynamic-rag-capsule 是打包器，adaptive-rag-engine 是导航仪
Usage Guidance
This skill appears to be a local orchestration/protocol layer for retrieval and is internally consistent, but before enabling you should: (1) Inspect the contents of rules/adaptive-rag-protocol.md and the files under ~/.openclaw/workspace/memory/topics to ensure they don't contain sensitive secrets you don't want the agent to read. (2) Note that vector search and memory bridging are not implemented here — the skill expects external components (e.g., a memory_search tool, bge‑m3 backend, Phoenix memory) to be present and may rely on their credentials; verify how those integrations are wired elsewhere. (3) Be aware of the SKILL.md claim that it is a protocol layer that 'does not require explicit invocation' — confirm how your platform will activate the skill (it is not always:on by default). (4) If you plan to run the included scripts, run them in a safe environment or inspect them (they are small and readable) — they only read/write files under your ~/.openclaw workspace and do not perform network I/O. If you need tighter assurance, request the full protocol/routing implementation or provenance for the vector/memory backends the skill expects.
Capability Analysis
Type: OpenClaw Skill
Name: adaptive-rag-engine
Version: 1.0.0
The 'adaptive-rag-engine' skill is a legitimate implementation of a Retrieval-Augmented Generation (RAG) system. Its components, including the indexing script (build-capsule-index.py) and the evaluation script (rag-evaluate.py), operate strictly within the expected OpenClaw workspace directory (~/.openclaw/workspace) to manage local memory files. The logic is transparent, lacks any signs of data exfiltration or malicious execution, and the instructions in SKILL.md are consistent with the stated purpose of improving search and retrieval quality.
Capability Tags
Capability Assessment
Purpose & Capability
Name/description (Adaptive RAG engine) match the provided artifacts: two small Python helper scripts (index builder and relevance evaluator) plus an instruction file describing routing, pre-filtering and evaluation. The skill claims integrations (vector search, bge‑m3, Phoenix memory bridge) but does not embed any external-client code — it acts as an orchestration/protocol layer and expects existing tools to provide vector search and memory storage. This design is coherent, though some high-level capabilities (e.g., bge‑m3 search, Active Memory ↔ Phoenix bridge) are described but not implemented in the included files — they are expected to exist elsewhere.
Instruction Scope
SKILL.md explicitly instructs the agent to read local protocol and memory files: rules/adaptive-rag-protocol.md and memory/topics/.capsule-index.json, and to run scripts under ~/.openclaw/workspace/skills/adaptive-rag-engine/scripts. Reading those files is expected for a RAG/navigation skill, but it is file-system access to user workspace data. Also SKILL.md states it is a "protocol layer" that "does not require explicit invocation" while registry flags show always:false — this is a minor inconsistency in intended activation semantics (not an immediate security problem, but worth noting).
Install Mechanism
No install spec (instruction-only plus two simple scripts) — low install risk. Scripts are small, readable Python files with no downloads or network calls. Minor manifest mismatch: registry metadata lists no required binaries, but the SKILL.md header declares python3 as required; python3 is reasonable for the provided scripts.
Credentials
The skill requests no environment variables or external credentials. The only resources accessed are local files under the user's OpenClaw workspace (~/.openclaw/workspace/memory and rules). That level of filesystem access is proportionate to the stated purpose of building/reading a capsule index and evaluating retrieval quality.
Persistence & Privilege
Skill is not marked always:true and does not request to modify other skills or system settings. Autonomous invocation (disable-model-invocation:false) is platform default; the SKILL.md's claim of acting as a protocol layer without explicit invocation contrasts with the actual flags but does not create extra privileges in the bundle itself.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install adaptive-rag-engine - After installation, invoke the skill by name or use
/adaptive-rag-engine - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Adaptive RAG Engine v1.0.0 — Initial Release
- Introduces an end-to-end adaptive retrieval engine moving from linear search to autonomous cognitive loops.
- Integrates capsule pre-filtering, adaptive search routing, CRAG error correction/evaluation, and L3 output verification.
- Features vector search (bge-m3), semantic re-ranking, and active memory bridge integration.
- Provides protocol-driven workflows with automated module triggering for complex queries and memory retrieval.
- Includes scripts to rebuild capsule index and evaluate retrieval quality.
- Clarifies relationships with phoenix-memory (storage) and dynamic-rag-capsule (compression), positioning this skill as the strategy/controller.
Metadata
Frequently Asked Questions
What is Adaptive Rag Engine?
Adaptive RAG 引擎 — 从线性检索到自主认知循环。集成胶囊预筛选、智能路由、CRAG纠错、L3校验。当需要搜索记忆/检索信息/回答复杂问题时触发。关键词：RAG、检索、记忆搜索、向量检索、Agentic RAG、CRAG。 It is an AI Agent Skill for Claude Code / OpenClaw, with 100 downloads so far.
How do I install Adaptive Rag Engine?
Run "/install adaptive-rag-engine" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Adaptive Rag Engine free?
Yes, Adaptive Rag Engine is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Adaptive Rag Engine support?
Adaptive Rag Engine is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Adaptive Rag Engine?
It is built and maintained by luaqnyin (@luaqnyin); the current version is v1.0.0.
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