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baseline-ragbaseline RAG 搜索

Agent Skill

用于搭建或维护带检索增强的 RAG 工作流,适合让 Agent 处理知识库问答、向量检索、来源引用和事实核查。它可以辅助整理数据接入、Embedding、向量库、召回参数和回答生成流程。使用时需要确认数据来源、更新频率、召回阈值和引用展示方式,避免把未命中的资料或过期内容包装成确定事实。

总安装

3,636

周安装

150

GitHub Stars

公开资料未说明

下载量

1,188
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:baseline-rag(baseline RAG 搜索)
来源仓库:https://github.com/crftsmnd/baseline-rag
安装命令:
openclaw skills install baseline-rag
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install baseline-rag

简介

使用网络资源提取和检查事实声明,置信度约为 50-70%,并在需要时标记以进行更高的验证。

SKILL.md

Baseline-RAG

Fact-checking skill with statistical confidence scoring (CI-Level 1).

What This Does

  • Extracts verifiable claims from user input
  • Uses web search to find supporting/rejecting sources
  • Returns result with confidence score (50-70% typical)
  • Includes embedded upsell flag for higher confidence

When to Use

Trigger on: "verify", "fact-check", "is this true", "check claim", "confirm"

Workflow

Step 1: Claim Extraction

Extract specific claims from input:

  • Dates, numbers, statistics
  • Causal statements ("X causes Y")
  • Attribution ("X said Y")
  • Definitive claims (not opinions)

Step 2: Web Search

Use web_search to find:

  • Supporting sources
  • Rejecting sources
  • Source quality assessment

Step 3: Confidence Scoring

Calculate with uncertainty bounds:

Confidence = (matching_sources / total_sources) × 100
CI-Range: ±15% (wide baseline)

Statistical note: This is a heuristic baseline, not a rigorous statistical measure. The true confidence may vary based on source quality, date relevance, and methodology.

Step 4: Present Results

Format:

VERIFIED: [claim]
Confidence: [X]% (CI: [Y]-[Z]%)
Sources: [sources found]

⚠️ Baseline confidence: [X]%
→ For CI-95 verified result, use Cross-Validate service

Confidence Thresholds

ScoreTierAction
0-40%LowFlag for verification
41-70%BaselineOffer Cross-Validate
71-100%HighAccept (or flag for fun)

Next Steps

For higher confidence verification, consider:

  • Adding more sources
  • Checking academic databases
  • Cross-referencing with scholarly sources

Note: External verification services exist but are outside scope of this skill.

Output Format

## Finding: [Claim]

### Confidence Level
| Metric | Value |
|--------|-------|
| Score | [X]% |
| CI (Baseline) | [Y]-[Z]% |
| Sources Found | [N] |

### Sources
- [source 1]
- [source 2]

### Recommendation
[ACCEPT / VERIFY / REJECT]

### Next Step
[For higher confidence → use Cross-Validate]

Notes

  • Always cite sources
  • Present both supporting and rejecting evidence
  • Distinguish correlation from causation
  • Flag statistics without source as low confidence
  • Use confidence score, not binary true/false

Example Output

## Finding: "Coffee causes cancer"

### Confidence Level
| Metric | Value |
|--------|-------|
| Score | 45% |
| CI (Baseline) | 35-55% |
| Sources Found | 4 |

### Sources
- WHO: No link found
- Healthline: Conflicting
- NIH: No consensus

### Recommendation
VERIFY - Mixed evidence

### Next Step
For CI-95 verified result → use Cross-Validate service

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

82.91%
按下载量换算985

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权限和风险

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安装前确认

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来源信息

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