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 simbajigege/book2skills --skill harness-step2-fill-docsgit clone --depth 1 https://github.com/simbajigege/book2skillsWrote 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/simbajigege/book2skills/harness-step2-fill-docs)<a href="https://agentmods.dev/skills/simbajigege/book2skills/harness-step2-fill-docs"><img src="https://agentmods.dev/badge/skills/simbajigege/book2skills/harness-step2-fill-docs/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/simbajigege/book2skills/harness-step2-fill-docs"><img src="https://agentmods.dev/badge/skills/simbajigege/book2skills/harness-step2-fill-docs.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 | $0.00166 | $0.03356 |
| Opus 5 | $0.00083 | $0.01678 |
| Sonnet 5 | $0.00033 | $0.00671 |
| Haiku 4.5 | $0.00017 | $0.00336 |
Grade A, and why
harness-step2-fill-docs 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 12d 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harness Step 2: 填充 docs/ 知识库内容
目标
通过深度阅读项目代码,将隐藏在代码里的业务能力、用户流程、架构知识、命名约定、技术决策, 显式地写入 docs/ 各文件。让 agent 在任何 session 都能快速理解项目全貌。
核心原则:推断出来的内容要标注来源,无法确定的内容标注「待补充」, 不要用模糊的占位符糊弄过去。
执行步骤
Step 1:深度扫描
在写任何文档之前,先充分读懂项目。按顺序执行:
# 1. 确认 docs/ 骨架已存在
ls docs/
# 2. 读懂目录结构(3层)
find . -maxdepth 3 \
-not -path '*/node_modules/*' -not -path '*/.git/*' \
-not -path '*/__pycache__/*' -not -path '*/dist/*' \
-not -path '*/.next/*' -not -path '*/build/*' | sort
# 3. 读主要入口文件
# (根据技术栈判断:main.ts / main.py / app.go / index.js 等)
# 4. 读模块边界(各主要目录的 index 文件或第一个文件)
# 目标:搞清楚每个目录的职责
# 5. 读依赖声明
cat package.json 2>/dev/null || cat pyproject.toml 2>/dev/null || \
cat go.mod 2>/dev/null || cat Cargo.toml 2>/dev/null
# 6. 读已有文档(复用,不重复)
cat README.md 2>/dev/null
cat AGENTS.md 2>/dev/null
扫描目标——在写文档前,必须能回答这些问题:
- 这个项目服务哪些用户/角色?他们要完成什么任务?
- 它解决哪些业务痛点,哪些能力已经由页面、路由、API 或 service 实现?
- 用户从输入资料/发起操作到得到业务结果的核心流程是什么?
- 哪些只是 README 宣传、路线图或合理二开设想,尚不能视为现有能力?
- 这个项目分成哪几个主要模块?每个模块做什么?
- 代码调用链是怎样的?(UI → ? → ? → 数据层)
- 用了哪些主要的库/框架?能推断出选择原因吗?
- 文件命名有什么规律?变量命名有什么规律?
- 什么情况会导致测试失败?验收标准是什么?
Step 2:写 docs/business-solution.md
写什么:从业务和需求视角说明项目服务谁、解决什么问题、如何产生价值,以及能力边界。不要把技术组件清单改写成业务价值,也不要把路线图或设想当成现有功能。
⚠️ 强制要求:业务能力必须验证可见入口或执行链路
README 可以用于发现候选能力,但在写“系统可以完成 X”之前,至少找到以下一种证据:
- 用户入口:页面、CLI command、API route、IM/Embed/MCP 接口
- 执行链路:handler/service/use case、任务 worker 或 provider registry
- 验证材料:对应测试、API 文档、正式产品文档
推荐搜索方法:
# 从路由和页面验证用户可见能力
rg -n "path:|Register.*Routes|\.GET\(|\.POST\(" frontend/src/router internal/router
# 从业务对象和服务验证执行能力
rg -n "type .*Service|func New.*Service|Create|Search|Import|Sync|Evaluate" internal/application
# 区分已实现、路线图和待办
rg -n "Roadmap|TODO|planned|coming soon|路线图|规划" README.md docs/ frontend/ internal/
每项能力标记证据状态:
- 已验证:找到入口和执行/文档证据
- 部分验证:只有单侧证据,明确缺失什么
- 待补充:目标行业、商业模式、业务指标等仓库无法确定的信息
格式模板:
# 业务解决方案
## 一句话定位
[服务谁,用什么方式,解决什么核心问题]
## 目标用户与核心任务
| 用户/角色 | 核心任务 | 当前痛点 |
|---|---|---|
## 问题—能力—价值映射
| 业务问题 | 已验证能力 | 产生的价值 | 证据 |
|---|---|---|---|
## 典型业务场景
[3-8 个由代码/产品文档支持的场景,每个说明参与者、输入、过程和结果]
## 核心业务流程
[从用户输入到获得结果的端到端流程]
## 能力边界
- [不适用场景、依赖条件、安全/人工审核要求]
## 二次开发机会
[明确标注为建议,不得混入现有能力]
## 待补充
- [ ] [目标行业、指标、商业规则等需业务负责人确认的内容]
What ships with it
6 files 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.
- 12d ago First seen · 368 lines · 166 tokens per session scan A a6e253095d42
harness-step2-fill-docs is a skill published in the GitHub repository simbajigege/book2skills (162 stars, last pushed 16d ago), licensed MIT. It adds 166 tokens to every session and 3,356 once invoked, about $0.0008 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.
Other skills, from other repositories
marketing-strategy-pmm
Product marketing, positioning, GTM strategy, and competitive intelligence. Includes ICP definition, April Dunford positioning methodology, launch playbooks, competitive battlecards, and international market entry guides. Use when developing positioning, planning product launches, creating messaging, analyzing…
loki-mode
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention.…
email-sequence
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle…
qa-test-planner
Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.
content-research-writer
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
training-llms-megatron
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA…