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 PANGKAIFENG/ai-product-manager-skills --skill decision-loopgit clone --depth 1 https://github.com/PANGKAIFENG/ai-product-manager-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/pangkaifeng/ai-product-manager-skills/decision-loop)<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/decision-loop"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/decision-loop/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/pangkaifeng/ai-product-manager-skills/decision-loop"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/decision-loop.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 | $0.00082 | $0.00573 |
| Opus 5.5 | $0.00033 | $0.00229 |
| Sonnet 5.5 | $0.00016 | $0.00115 |
| Haiku 4.5 | $0.00008 | $0.00057 |
Grade A, and why
decision-loop 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.
What it actually says
决策闭环
这是 loop 的 Codex Runtime 入口，不是新的研究 Skill。先读取同目录 LOOP.md，再使用现有 research-topic-compiler 和 decision-research 关闭一个具体决策。
目标与输入
目标是关闭一个具体决策，而不是扩张研究范围。Entry Gate 开始前必须有：
- 一个明确的
decision_question； - 当前候选项或判断方向；
- 会影响结论的证据缺口；
- gap 的关闭条件。
如果用户只是想系统理解一个领域，使用 research-topic-compiler。如果问题本身还未定义，使用 ai-collaboration-calibration。
工作流
- 建立或恢复
LOOP.md定义的状态，max_cycles固定为 3。 - 使用
decision-research判断当前推荐、置信度和唯一 material gap。 - 只有 gap 同时可研究、可关闭且会改变决策时，才使用
research-topic-compiler获取 Evidence Delta。 - 把 Evidence Delta 交回
decision-research更新推荐；不允许 Research 自己宣布决策完成。 - 满足关闭条件时输出
decision_ready；达到停止条件时输出human_gate或blocked。
输出
每轮返回 cycle、决策问题、当前推荐、置信度、active gap、Evidence Delta、保留项、状态和恢复点。不要复制已有研究全文，也不要发起任何外部写入。
完成定义
只有 closure criterion 已被新证据满足且 decision-research 更新了推荐、置信度和颠覆条件，才输出 decision_ready。三轮上限、连续两轮无有效 Evidence Delta 或业务取舍不可推断时进入 Human Gate。
资源与验证
LOOP.md是状态字段、轮次和停止条件的权威合同，每次执行或恢复前读取。evals/evals.json覆盖证据 gap、cycle 2 恢复、开放式研究分流和业务取舍回归；修改入口后运行这些评测并保留结果。
What ships with it
3 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 First seen · 43 lines · 82 tokens per session scan A c52069e50bd7
decision-loop is a skill published in the GitHub repository PANGKAIFENG/ai-product-manager-skills (16 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 573 once invoked, about $0.0003 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 2026-08-31.
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