Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/samqin123/claude_skill_pool/extractnpx skills add samqin123/Claude_skill_pool --skill extractgit clone --depth 1 https://github.com/samqin123/Claude_skill_poolWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/samqin123/claude_skill_pool/extract)<a href="https://agentmods.dev/skills/samqin123/claude_skill_pool/extract"><img src="https://agentmods.dev/badge/skills/samqin123/claude_skill_pool/extract.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.00812 |
| Opus 5 | $0.00033 | $0.00406 |
| Sonnet 5 | $0.00013 | $0.00162 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
extract scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Extract - 研究方法论反向提取器
Overview
从已完成的深度研究报告中反向提取研究方法论的"元框架"（how to think），而非具体结论（what to think），实现跨场景迁移复用。
Workflow
1. 文档结构分析
- 读取指定的研究文档。
- 识别章节结构，标注每章的研究目的。
- 绘制研究逻辑流程图。
2. 六维度框架提取
逐一分析并提取以下维度的方法论模板：
- 宏观分析框架 — 历史对标、时代特征、关键时间节点
- 资产/对象分类框架 — 分类逻辑、驱动因素、配置角色
- 驱动逻辑验证框架 — 必要条件、充分条件、逻辑共振、转折信号
- 三重验证体系 — 宏观逻辑 + 产业格局 + 财务健康
- 风险预警框架 — 上行/下行双向评估、监测指标、动态调整机制
- 专家观点整合框架 — 核心专家矩阵、观点交叉验证、分歧处理
每个维度区分：
- 常量（方法论骨架）：分析步骤、验证逻辑、思考框架
- 变量（场景化内容）：具体数据、时间、标的、事件
3. 模板参数化
- 识别模板中的变量插槽。
- 为每个变量设定取值范围。
- 建立变量之间的依赖关系。
4. 跨场景验证
- 换时间、换主题验证框架适用性。
- 标记通用部分（直接复用）和定制部分（需调整）。
5. 生成最终输出
输出到 research_frameworks/ 目录：
research_frameworks/
└── [提取时间]_[原研究主题]/
├── 00_研究方法论总览.md
├── 01_宏观分析框架模板.md
├── 02_资产分类框架模板.md
├── 03_驱动逻辑验证框架模板.md
├── 04_三重验证体系模板.md
├── 05_风险预警框架模板.md
├── 06_专家观点整合框架模板.md
├── 07_研究工作流程.md
├── 08_Prompt模板库.md
└── 09_使用指南.md
Quality Checklist
完整性：是否覆盖六个核心维度？每个维度分析步骤是否清晰？ 通用性：模板是否去除场景特定内容？变量是否明确标注？ 可操作性：Agent 能否直接使用这些模板？是否提供使用示例？
Guardrails
- 提取方法论（how），不是结论（what）。
- 生成的框架需要人工审核后使用。
- 每次提取创建带时间戳的新目录，旧版本保留。
- 适用于任何有逻辑深度的分析报告，不限于投资领域。
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 73 lines · 66 tokens per session scan A d8bdb3e7778d
extract is a skill published in the GitHub repository samqin123/Claude_skill_pool (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 812 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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