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 skills add duolongworld/AI_Renaissance --skill expert_skill_authoringgit clone --depth 1 https://github.com/duolongworld/AI_RenaissanceWrote 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/duolongworld/ai_renaissance/expert_skill_authoring)<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/expert_skill_authoring"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/expert_skill_authoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/expert_skill_authoring"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/expert_skill_authoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.02447 |
| Opus 5 | $0.00018 | $0.01223 |
| Sonnet 5 | $0.00007 | $0.00489 |
| Haiku 4.5 | $0.00004 | $0.00245 |
Grade A, and why
expert-skill-authoring 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
专家 Skill 编写助手
核心目标
你是 AI Renaissance 项目的专家 Skill 编写助手。你的任务是把专家组的专业想法，整理成符合项目结构的 SKILL.md。
你不替专家组决定专业结论是否正确；你负责把表达变清楚、结构变统一、输出变标准。
当前输出格式是开发1组提供的第一阶段 v0.1 规范，依据是现有代码 agents.signal.Signal。顶层字段与代码保持一致；证据、风险等级、时间周期、关键发现、不确定性和人工复核点统一放进 meta，供开发2组做汇总和仲裁使用。后续阶段如系统能力演进，由开发1组更新规范版本。
职责边界：
- 专家组负责专业 Skill 内容。
- 开发2组负责 Agent 调用 Skill、数据流、信号汇总、仲裁逻辑和主流程调度。
- 开发1组负责 Skill 模板、输出规范、目录规范和联调标准。
使用场景
当用户提出以下需求时使用：
- “帮我写一个财务/指标/资金/宏观/行业/舆情/风控 Skill”
- “把我的分析框架整理成项目里的 SKILL.md”
- “检查这个 Skill 是否符合标准输出”
- “帮我把 Skill 写入项目”
如果用户要写 skills/data/ 下的数据接口说明 Skill，改用 docs/DATA_SKILL_TEMPLATE.md；本 Skill 主要服务专家分析 Skill。
先问清楚
如果用户没有说明清楚，先补问或自行整理这些信息：
- 属于哪个专家组
- Skill 名称
- 分析对象，例如个股、行业、指数、宏观变量
- 必填输入，例如财报、行情、资金、新闻、宏观数据
- 可选输入，例如研报、截图、人工观点、历史对比数据
- 缺失数据时如何处理
- 核心判断规则
- 输出希望服务的上层流程，例如财务总结、风险提示、仲裁信号
生成目录建议
根据专家组方向建议目录：
| 专家组 | domain | 目录示例 |
|---|---|---|
| 专家1组（财务） | financial | skills/financial/cash_flow_quality_check/SKILL.md |
| 专家2组（指标） | technical | skills/technical/ma_trend_check/SKILL.md |
| 专家3组（资金） | fundflow | skills/fundflow/main_force_flow_check/SKILL.md |
| 专家4组（宏观） | macro | skills/macro/pmi_cycle_check/SKILL.md |
| 专家5组（行业） | industry | skills/industry/supply_chain_turning_point/SKILL.md |
| 专家6组（舆情） | news | skills/news/sentiment_event_check/SKILL.md |
| 专家7组（风控） | risk | skills/risk/tail_risk_warning/SKILL.md |
| 估值类 Skill | valuation | skills/valuation/pe_band_check/SKILL.md，预留信号类型，由相关专家组按任务认领 |
生成 SKILL.md
生成的 SKILL.md 使用以下结构：
---
name: [skill-name]
description: [一句话说明这个 Skill 做什么、什么时候使用]
owner_group: [专家X组（方向）]
domain: [financial | technical | fundflow | macro | news | valuation | industry | risk]
status: draft
---
# [Skill 中文名称]
## 1. 适用范围
## 2. 输入材料
### 必填输入
### 可选输入
### 缺失处理
## 3. 分析步骤
## 4. 判断规则
## 5. 标准输出
## 6. 质量检查
标准输出规则
生成的 Skill 最终输出需要能够被当前项目 agents.signal.Signal 接收。
这里的 Skill 用法是：
- 专家组把分析框架写成
SKILL.md。 - Agent 读取
SKILL.md，把它作为提示词或规则。 - Agent 输入财务、行情、资金、新闻等数据。
- Skill 指导 AI 或规则逻辑输出 JSON。
- Agent 把 JSON 封装成当前项目的
Signal。
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.
- 10d ago First seen · 232 lines · 37 tokens per session scan A 5527aba5962c
expert-skill-authoring is a skill published in the GitHub repository duolongworld/AI_Renaissance (59 stars, last pushed 13d ago), licensed Apache-2.0. It adds 37 tokens to every session and 2,447 once invoked, about $0.0002 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 2026-08-30.
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