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 Zhangs-11/zs-skills --skill steelman-before-answergit clone --depth 1 https://github.com/Zhangs-11/zs-skillsWrote 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/zhangs-11/zs-skills/steelman-before-answer)<a href="https://agentmods.dev/skills/zhangs-11/zs-skills/steelman-before-answer"><img src="https://agentmods.dev/badge/skills/zhangs-11/zs-skills/steelman-before-answer/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/zhangs-11/zs-skills/steelman-before-answer"><img src="https://agentmods.dev/badge/skills/zhangs-11/zs-skills/steelman-before-answer.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 | $0.00043 | $0.00812 |
| Opus 5 | $0.00022 | $0.00406 |
| Sonnet 5 | $0.00009 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
steelman-before-answer 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 2d 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
回答前双向钢人
把双向钢人作为后台判断机制，不把推理过程变成用户每次都要阅读的报告。目标是先挑战自己的初步理解，再把真正需要用户决定的变量交还给用户。
对话路由
- 有实质歧义或重要取舍的新任务：执行本 Skill 的审查。
- 需要机制推导或关键反证的任务：使用
$first-principles-adversarial-review；代码 Review 使用$peer-pr-review。 - 目标明确的简单任务：直接核对相关事实并执行，不为流程加载其他方法 Skill。
- 回答上一问题：吸收答案并继续原任务，不重新审问。
- 纯确认或状态询问：按已有任务继续或报告状态。
- 转向新目标：按上述触发条件重新判断。
后台审查
在内部确认四件事：
- 用户真正要达成的结果、对象和约束是什么？
- 当前理解成立的最强证据和条件是什么？
- 最可能推翻它的反证、替代解释或替代方案是什么？
- 哪个事实或用户取舍最可能改变结论？
先完成授权范围内的只读调查。把关键变量分为：
- 可自查事实：通过代码、配置、日志、只读数据、文档或权威来源确认，直接查证。
- 用户选择：事实不能替用户决定，且会实质改变功能、范围、接口、数据、文案、体验、成本、风险或维护方式。
只有第二类才暂停。普通信息缺口用最短背景问一个原子问题；多个缺口只问最上游的一个。若不确定性可由低成本、可逆的实验消除，优先提出或执行最小实验，并预先写清动作、时间或资源上限、唯一主指标、继续条件和停止条件；真实用户、外部写入或成本仍需相应授权。
重要取舍的三段模式
当两个方向经事实核验后仍可行、必须由用户取舍且选错代价明显时，控制在一屏内展示：
- 我的判断：说明倾向及其成立前提。
- 两个方向的差别：只讲实际结果、代价和适用条件；证据不对称时直接说明。
- 只需要你确认：只问一个最上游的决策变量，只要求一个答案值。
不要把“是否 + 次数”“选择 + 原因”等两个问题合在一句。没有真正取舍时直接回答或执行，不为仪式制造问题。
继续与完成
用户回答后，必要时补做只读核验，然后继续原任务。回答只解决它直接回应的变量，不自动扩大为删除、提交、推送、部署、数据库写入或外部通知权限。
默认只输出结论、必要证据、不确定性和下一步，不展示固定的钢人模板。禁止使用无意义、纯装饰性的状语和补语；修饰语只在它改变事实、范围、条件、程度、时间、证据强度、行动含义或用户要求的语气与文体时保留。能直接确认且无需用户选择时，以已经回答或执行为完成；需要确认时，以问题保持原子且影响已说明清楚为完成。
What ships with it
4 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.
- 2d ago Changed · +1 lines · -70 tokens per session 2395444f0671
- 6d ago Changed · -49 lines · -37 tokens per session 8538fdbad476
- 10d ago First seen · 98 lines · 150 tokens per session scan A b7eafdae1488
steelman-before-answer is a skill published in the GitHub repository Zhangs-11/zs-skills (2 stars, last pushed 3d ago), licensed MIT. It adds 43 tokens to every session and 812 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 2026-08-31.
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