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Dual Retrieval

v1.0.0

Combines M-Flow graph topology and QMD BM25+vector retrieval to merge and rank results for precise multi-hop and semantic search.

0· 88·0 current·0 all-time
bysune@sora-mury

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Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Dual Retrieval" (sora-mury/dual-retrieval) from ClawHub.
Skill page: https://clawhub.ai/sora-mury/dual-retrieval
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

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OpenClaw CLI

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openclaw skills install dual-retrieval

ClawHub CLI

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npx clawhub@latest install dual-retrieval
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Purpose & Capability
The name and description match the code: it combines M-Flow and QMD search results and implements merging strategies. However, the skill expects local QMD artifacts and a local 'm-flow' skill directory (loaded dynamically) even though SKILL.md and registry metadata declare no config paths or credentials. The requirement to access a local QMD DB and a sibling skill directory is not declared in the metadata.
!
Instruction Scope
SKILL.md describes the high-level pipeline but does not mention reading the user's local QMD SQLite DB or writing a report to disk. The included Python code directly opens C:/Users/Administrator/.cache/qmd/index.sqlite, issues arbitrary SQL, prints sample rows, and the test writes a report to a knowledge/ directory. Those file accesses and outputs are outside what the SKILL.md explicitly documents and could expose private data.
Install Mechanism
There is no installer (instruction-only from registry perspective). That reduces supply-chain risk. However, the skill includes Python code files that will be executed when the skill is used; no network downloads or third-party package installations are declared in the install metadata.
!
Credentials
The skill requests no environment variables or credentials, yet it reads a hard-coded, absolute Windows path (C:/Users/Administrator/.cache/qmd/index.sqlite) and attempts to load a .env from a sibling m_flow directory. Access to a user's local document index (QMD DB) is equivalent to requesting access to potentially sensitive data and should have been declared as a required config path or credential. Dynamic importing of a local 'm-flow' skill directory also implies access to code and env that belong to other skills without declaration.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges. It does write a report file in the test code (knowledge/dual-retrieval-report.md) and may create directories, but it does not modify other skills' configurations or request persistent agent-level privileges.
What to consider before installing
This skill does what it claims (combines M-Flow and QMD results) but the implementation reads a hard-coded local QMD SQLite database and dynamically loads a local 'm-flow' module without declaring those accesses. Before installing or running: - Treat this as potentially accessing private local documents (QMD DB). Do not run it in an environment with sensitive data unless you reviewed or sandboxed it. - Inspect or run the scripts in a safe, isolated environment (disposable VM or container). Confirm the C:/Users/Administrator/.cache/qmd/index.sqlite path and any data it contains. - If you expect to use it, ask the author to parameterize the DB path and m-flow location (don’t hard-code C:\Users\Administrator...), declare required config paths in metadata, and avoid printing or writing raw document content. - Verify the dynamically loaded m-flow code is from a trusted source before allowing the skill to import it. - If you cannot confirm these changes, consider the skill suspicious and avoid granting it access to your real data or environment.

Like a lobster shell, security has layers — review code before you run it.

latestvk97386pbg0pxeh0vfejt1bs6mx849d2d
88downloads
0stars
1versions
Updated 3w ago
v1.0.0
MIT-0

Dual Retrieval Skill - Phase 4

描述

双重检索:M-Flow(图拓扑检索)+ QMD(BM25+向量检索)优势互补。

M-Flow vs QMD 对比

特性M-FlowQMD
检索方式图拓扑 + Bundle SearchBM25 + 向量 + rerank
适合场景精确问答、多跳推理关键词搜索、语义相似
记忆结构四层 Cone Graph多 Collection
优势时间推理、关联推理灵活、已配置

工作流程

Query → 
  ├── M-Flow.search() → Episode + Facet + Entity
  └── QMD search → 文件 + 片段
      ↓
结果合并 → 去重 → 排序 → 返回

文件结构

dual-retrieval/
├── SKILL.md
├── scripts/
│   ├── __init__.py      # DualRetrievalPipeline
│   └── test_dual.py     # 测试

依赖

  • m-flow-memory skill (MFlowMemory)
  • QMD (qmd tools)

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