什么是 Methodology Extractor？

Methodology Extractor 是一款专业级工具，专为需要从海量文献中合成实验方案的研究人员和数据科学家设计。作为 Openclaw Skills 集合的核心组件，该工具促进了方法的系统化提取，使用户能够以高精度和技术准确性执行证据洞察任务。它有助于识别科学文档中的明确假设和限定范围，确保每项发现都有可验证的数据支持。

通过利用此技能，用户可以生成可重复的输出格式，用于比较多项研究之间的差异。这确保了方案优化和可重复性评估建立在记录在案的证据之上。Openclaw Skills 提供了在整个研究生命周期中保持一致性和可评审性所需的框架，使其成为循证科学探索的重要资产。

下载入口:https://github.com/openclaw/skills/tree/main/skills/aipoch-ai/methodology-extractor

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Methodology Extractor 应用场景

• 实验室工作流程和实验设计的方案优化。
• 需要对多篇同行评审研究进行详细方法比较的系统评价。
• 对已发表实验数据进行可重复性评估，以验证科学主张。
• 批量提取特定实验参数，如抗体浓度、封闭条件或洗涤时间。

1. 用户通过提供论文 ID 列表并指定目标方法类型（例如 Western Blot 或 qPCR）来定义目标。
2. 该技能根据 Openclaw Skills 框架内记录的范围和约束条件验证请求。
3. AI 代理启动位于 scripts/main.py 的封装 Python 脚本的执行。
4. 系统处理输入的论文，以识别整个数据集中的参数变化和方案基准。
5. 生成结构化结果，将假设、交付物和识别出的风险分开，供用户审阅。

Methodology Extractor 配置指南

要开始使用此技能，请确保您的环境符合 Python 3.10+ 要求。导航到技能目录并使用以下命令验证脚本完整性：

cd "scientific-skills/Evidence Insight/methodology-extractor"
python -m py_compile scripts/main.py

要查看 Openclaw Skills 中 Methodology Extractor 的配置选项和参数，请运行帮助命令：

python scripts/main.py --help

Methodology Extractor 数据架构与分类体系

该技能将研究数据和提取结果组织成结构化格式，以便进行清晰的比较。以下架构定义了输入和输出结构：

name: methodology-extractor description: Batch extraction of experimental methods from multiple papers for protocol. license: MIT skill-author: AIPOCH

Methodology Extractor

Extract and compare experimental protocols across papers.

When to Use

• Use this skill when the task needs Batch extraction of experimental methods from multiple papers for protocol.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Batch extraction of experimental methods from multiple papers for protocol.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260318/scientific-skills/Evidence Insight/methodology-extractor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Use Cases

• Protocol optimization
• Methods comparison for systematic reviews
• Reproducibility assessment

Parameters

• paper_ids: List of papers to analyze
• method_type: Target method (e.g., "Western Blot", "qPCR")

Returns

• Comparison table of protocols
• Parameter variations across studies
• Best practice recommendations

Example

Input: 50 papers, method_type="Western Blot" Output: Table showing antibody concentrations, blocking conditions, wash times

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of methodology-extractor and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

methodology-extractor only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.