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 XRenSiu/claude-code-forge --skill techdocgit clone --depth 1 https://github.com/XRenSiu/claude-code-forgeWrote 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/xrensiu/claude-code-forge/techdoc)<a href="https://agentmods.dev/skills/xrensiu/claude-code-forge/techdoc"><img src="https://agentmods.dev/badge/skills/xrensiu/claude-code-forge/techdoc/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/xrensiu/claude-code-forge/techdoc"><img src="https://agentmods.dev/badge/skills/xrensiu/claude-code-forge/techdoc.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00170 | $0.02762 |
| Opus 5.5 | $0.00068 | $0.01105 |
| Sonnet 5.5 | $0.00034 | $0.00552 |
| Haiku 4.5 | $0.00017 | $0.00276 |
Grade A, and why
techdoc 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 1mo 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
techdoc
输入一个 brief（要解决什么、已知什么、给谁看），输出一份呈现资深工程师方案形状的文档，
并且已经过 /humanize 的三道闸门（humanlint / factdiff 对 brief / cold-reader）。
缺口（deletion 测试）
撤掉本 skill，让引擎"写个缓存优化方案"，它交出的是 ../humanize/fixtures/ai-zh.md 那种东西：
背景段零事实、四条加粗目标、首先其次最后、"可能面临一定风险"、综上所述。不是引擎不会写方案，
是它的默认形状是综述而不是决定。缺的是：
- φ（产物序）：一份方案必须呈现的顺序与必有件——触发事件、一句话决定、被杀掉的替代、全部后果、
灰度回滚、未决问题。这是产物性质，
scripts/verify_techdoc.py在出口检。 - Σ（关于读者）：方案写给两年后接手的新人和今天来挑刺的评审，不是写给审批人； 读者从不该感到意外——每个反对意见在他想到之前已被回答。
- γ：写完必须过
/humanize的闸门，且事实只能来自 brief 与 materials。
Σ：一份方案的形状
十个动作、体裁变体、语气规则、"AI 默认 → 资深工程师"对照表，全部在 references/skeleton.md。
写第一份前先读它和 ../humanize/fixtures/human-zh.md（或 human-en.md）——先见过对的样子。
核心形状（proposal / design）：
- 触发事件 + 具体痛点，一段，带日期 / 数字 / 专名。
- 决定，一句话，主动语态；附预算或胃口。
- 目标 / 非目标（唯一该用 bullet 的地方）。
- 设计：先全貌后细节，只写影响取舍的部分；一个有名有姓的走查场景。
- 为什么是它而不是那两个明显的替代——每个死于一个具体原因；再加一段"为什么不该做"。
- 后果，全部；每个风险有触发条件、负责人、兜底。
- 灰度、回滚、监控。
- 未决问题 + 尝试性答案；什么证据会推翻这个决定。
ADR 只要 1 / 2 / 5 / 6；postmortem 是时间线 + 根因 + 为什么防线没拦住 + 带 owner 与日期的行动项； memo 全散文、目标之外禁 bullet。
事实的三种来路（沿用 psl 的诚实公理）：brief 与 materials 里有的 → 用；领域常识（"标准 SQS 不保序"）→ 用并可被查证；
两者都没有但方案必须有的（当前 QPS、故障日期、预算）→ 问，或标 [需核实：…]。没有第四种来路。
一份带 [需核实] 的方案比一份数字全是编的方案更像人写的——人不会编自己系统的 QPS。
φ：什么算写对了
verify_techdoc.pyverdict ≠ reject（形状齐全：第一段有锚点、前 3 段有决定句、有被否决的替代、有未决 / 下一步 / 回滚、无总结式收尾）。- 每个替代方案的死因是具体的（数字、先例、改动面），不是"复杂度较高"。
- 每个风险三件套：触发条件、负责人（或角色）、兜底动作。
- 至少一个非目标是"读者会合理以为你要做"的事。
- 走查场景有具名主体和真实量级的数值（来自 brief / materials，或标
[需核实]）。 /humanize的全部 φ 同时成立（见../humanize/SKILL.md）：文档层 / 段落层 / 句子层 / 事实层 / 语域层。- factdiff 以 brief + materials 为 source：方案里的每个数字在来源里有，或被标
[需核实]。
Π：原语
scripts/verify_techdoc.py <doc> [--genre …]：产物序预门；reject 五条、flag 五条，见脚本 docstring。../humanize/scripts/humanlint.py、../humanize/scripts/factdiff.py、cold-readeragent：见../humanize/SKILL.md的 Π 节，用法相同。references/skeleton.md：十动作 / 体裁表 / 语气规则 / 对照表。references/example.md：一个 brief → 方案的完整推导（含 verify / humanlint / cold-reader 输出），先读再写。- 有证据的生成技巧：先写每段的主张句骨架（一段一句，第一句就是答案），确认骨架顺序符合产物序，再展开成散文；
开头段生成 5 个候选并丢掉概率最高的；有
voice.md时以 completion 方式续写。
What ships with it
5 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.
- 1mo ago Changed bb3936f17444
- 1mo ago First seen · 118 lines · 170 tokens per session scan A a839d7babb7b
techdoc is a skill published in the GitHub repository XRenSiu/claude-code-forge (2 stars, last pushed 29d ago), licensed MIT. It adds 170 tokens to every session and 2,762 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-09-05.
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