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 metago-ai/metagolifeform --skill metago-thought-05-policy-tractiongit clone --depth 1 https://github.com/metago-ai/metagolifeformWrote 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/metago-ai/metagolifeform/metago-thought-05-policy-traction)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-05-policy-traction"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-05-policy-traction/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/metago-ai/metagolifeform/metago-thought-05-policy-traction"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-05-policy-traction.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 | $0.00067 | $0.00961 |
| Opus 5.5 | $0.00027 | $0.00384 |
| Sonnet 5.5 | $0.00013 | $0.00192 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
metago-thought-05-policy-traction 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
政策牵引律 V4.0
描述
通过技术可行性、经济合理性、制度兼容性、政策导向性四维对齐评估，量化方案与政策环境的契合度，指导方案设计与政策风险防控。
触发条件
- 评估方案的政策合规性与落地可行性
- 政策风险分析与防控
- 方案需要政府/监管审批
- 需要论证方案的政策正当性
前置条件
- 明确的技术方案
- 可获取的相关政策文件与法规
元思想核心
四维对齐模型：
政策牵引力 P = T * E * I * D
| 维度 | 符号 | 评估内容 | 通过阈值 |
|---|---|---|---|
| 技术可行性 | T | 方案技术路径是否成熟可实现 | T >= 0.6 |
| 经济合理性 | E | 方案投入产出比是否合理 | E >= 0.6 |
| 制度兼容性 | I | 方案是否与现行法规制度兼容 | I >= 0.7 |
| 政策导向性 | D | 方案是否符合当前政策导向 | D >= 0.7 |
关键特性：
- P 为乘法关系：任一维度为 0，整体牵引力为 0
- I 和 D 为硬约束（阈值更高），T 和 E 为软约束
- P >= 0.25 为可推进；P >= 0.5 为优先推进；P < 0.15 为高风险
政策窗口期：
W(t) = D(t) * Window(t)
政策导向性会随时间变化，需识别窗口期。
推理框架
步骤 1：技术可行性评估（T）
- 技术成熟度（TRL 1-9 级）
- 技术路径是否有验证案例
- T = TRL/9 * 验证度
步骤 2：经济合理性评估（E）
- 投入产出比分析
- 成本可承受性
- E = min(ROI合理性, 成本可承受性)
步骤 3：制度兼容性评估（I）
- 逐条比对现行法规
- 识别合规风险点
- I = 1 - 合规风险密度
步骤 4：政策导向性评估（D）
- 比对国家/行业政策文件
- 识别政策支持条款
- D = 政策支持度 * 导向匹配度
步骤 5：综合牵引力计算
- P = T * E * I * D
- 识别最薄弱维度（木桶效应）
- 评估政策窗口期 W(t)
步骤 6：优化策略
- 若 T 低：技术攻关或引入成熟方案
- 若 E 低：优化成本结构或扩大收益
- 若 I 低：调整方案设计规避合规风险
- 若 D 低：等待窗口期或调整方向
验证方法
- 四维评估是否有客观证据（非主观打分）
- T 评估是否参考了 TRL 标准或等效成熟度模型
- I 评估是否逐条比对了具体法规条款
- D 评估是否引用了具体政策文件
- P 计算是否正确（乘法关系）
- 最薄弱维度的识别是否准确
- 政策窗口期 W(t) 是否有时间维度分析
- 用已审批通过/被拒的方案回测：P 值排序是否与审批结果一致
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 · 95 lines · 67 tokens per session scan A 0cb92323333e
metago-thought-05-policy-traction is a skill published in the GitHub repository metago-ai/metagolifeform (5 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 961 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 2026-09-03.
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