Borrowing it
Nothing to install: this file belongs to Peiiii/nextclaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Peiiii/nextclaw/master/.agents/skills/autonomous-requirement-discovery/SKILL.mdgit clone --depth 1 https://github.com/Peiiii/nextclawWrote 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/peiiii/nextclaw/autonomous-requirement-discovery)<a href="https://agentmods.dev/skills/peiiii/nextclaw/autonomous-requirement-discovery"><img src="https://agentmods.dev/badge/skills/peiiii/nextclaw/autonomous-requirement-discovery/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/peiiii/nextclaw/autonomous-requirement-discovery"><img src="https://agentmods.dev/badge/skills/peiiii/nextclaw/autonomous-requirement-discovery.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 | $0.00077 | $0.00633 |
| Opus 5 | $0.00039 | $0.00316 |
| Sonnet 5 | $0.00015 | $0.00127 |
| Haiku 4.5 | $0.00008 | $0.00063 |
Grade A, and why
autonomous-requirement-discovery 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 11d 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
自主需求发现
定位
探索“在用户没有给出详细需求时，AI 能否主动发现真正值得解决的问题”。这是独立的探索性能力，不是标准开发生命周期阶段，也不自动接管普通任务理解。
输入可以只有产品方向、目标用户、问题域或上位目标，但必须有明确的探索边界。没有证据时提出可验证假设，不把模型直觉包装成用户需求。
发现循环
- 冻结上位目标、目标用户、探索范围、禁止边界和本轮需要改变的决策。
- 对齐产品愿景，调查当前用户任务、真实产品状态、已有反馈、issue、使用信号和历史决策。
- 区分已确认问题、弱信号、合理假设和纯猜测；缺少用户数据时明确证据等级。
- 为每个高价值候选写清受影响用户、真实阻力、期望结果、支持证据和最小反证方式。
- 用最低成本的只读调查、对照、原型或实验消除最关键不确定性；需要修改产品或外部状态时先取得相应授权。
- 按用户价值、愿景一致性、证据强度、可验证性和实现代价比较候选，不用伪精确总分掩盖判断。
- 推荐一个最值得继续的需求假设；只有真实取舍存在时保留一至两个备选。
需求质量门
候选需求必须同时说明：
- 它解决的是哪一个用户问题，而不是先选定了哪个功能；
- 为什么现在值得解决，证据和未知项分别是什么；
- 用户或系统出现什么可观察变化才算成功；
- 最小可行验证是什么，什么信号会证伪它；
- 哪些相邻问题和解决方案不属于当前范围。
只有“做什么功能”的想法、没有用户结果或反证方式时，继续调查或降级为待验证假设，不进入实现。
输出与停止
输出推荐需求假设、证据、目标用户、成功信号、最小实验、主要未知项、备选与非目标。证据足以支持下一步决策，或继续探索的成本已经高于信息增益时停止。
本 skill 不修改产品实现，不自动创建 roadmap、计划或 issue，也不把探索结论宣称为已确认需求。后续是否进入正式开发由用户或当前任务 owner 决定。
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.
- 11d ago First seen · 41 lines · 77 tokens per session scan A 6829c7be6cc2
autonomous-requirement-discovery is a skill published in the GitHub repository Peiiii/nextclaw (256 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 633 once invoked, about $0.0004 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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