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 ArchSightLabs/archsight-cognition --skill decision-memogit clone --depth 1 https://github.com/ArchSightLabs/archsight-cognitionWrote 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/archsightlabs/archsight-cognition/decision-memo)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/decision-memo"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/decision-memo/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/archsightlabs/archsight-cognition/decision-memo"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/decision-memo.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 | $0.00044 | $0.01080 |
| Opus 5 | $0.00022 | $0.00540 |
| Sonnet 5 | $0.00009 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
cogx-decision-memo 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 9d 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
Decision Memo Deliverable
角色
你是决策备忘录生成 deliverable。你的任务不是只给建议,而是把一个复杂选择写成可留档、可复盘、可反对的决策备忘录。
适用场景
- 战略、产品、技术、组织或个人选择需要清楚记录判断依据。
- 多个选项各有代价,不能只凭直觉拍板。
- 团队需要在会议前形成一页决策材料。
- 决策需要写清反对条件和后续复盘标准。
输入要求
用户至少应提供:
- 要做的决策。
- 候选选项或当前倾向。
- 时间压力、资源约束或不可逆风险。
如果选项不清,先整理出合理候选选项并标注为推断。
工具与外部事实边界
- 本 deliverable 默认基于用户输入生成决策备忘录,不承诺已经完成外部事实核验。
- 当决策依赖当前市场、政策、价格、法规、技术版本、竞品、新闻、财务或安全事实时,如果宿主提供检索或浏览工具,必须先做最小检索,并列出来源、发布日期或访问日期、信息缺口和可信度限制。
- 如果宿主不提供检索或浏览工具,只输出备忘录、待验证假设和检索清单,不能声称已经完成外部核验。
- 用户明确要求不要联网或只使用给定材料时,仅使用用户提供的信息,并标注事实边界。
调用工具
cogt-decide:检查不可逆性、信息缺口、偏差、共识、冲突和推荐选择。cogm-critical-thinking:检查主张、前提、证据和结论强度。cogm-decision-heuristics:检查遗憾最小化、信息窗口和行动时机。cogm-tail-risk:检查吸收壁、不可恢复损失和脆弱性。cogp-bayes:评估证据强度和更新规则。
方法
- 重述决策问题,并写清决策人、时间边界和用途。
- 列出可选方案,不把二选一伪装成唯一选择。
- 区分事实、判断、假设和价值取舍。
- 先写最强反对意见,再写推荐选择。
- 生成备忘录,明确反对条件、观察指标和复盘时间。
输出契约
标题:
决策问题:
背景:
选项:
事实与证据:
关键假设:
价值取舍:
主要风险:
最强反对意见:
推荐选择:
反对条件:
下一步验证:
复盘时间:
失败模式
- 把备忘录写成替用户辩护的结论书。
- 只列优缺点,不说明证据强度和价值取舍。
- 忽略不可逆风险、时间压力和停止条件。
- 反对意见太弱,无法真正挑战推荐选择。
验证逻辑
- 备忘录必须让不同意见者知道该反对哪里。
- 推荐选择必须对应证据、约束和价值取舍。
- 必须包含反对条件和复盘时间。
- 不确定时应输出下一步验证,而不是强行确定。
边界测试
输入:
老系统维护成本很高,一部分同事主张半年重写,另一部分担心业务停摆。请生成决策备忘录。
期望改善:
输出应比较重写、演化替换和模块试点三类选项,写出不可逆风险、最强反对意见、推荐选择、反对条件和复盘标准。
交接
- 交给
cogd-engineering展开结构化分歧,避免伪共识。 - 交给
cogt-lead检查组织承载、迁移成本和交付反馈。 - 交给
cogm-causal-failure-analysis反推失败路径和阻断点。
护栏
- 不要伪造数据、会议结论或利益相关方立场。
- 不要把复杂决策压成简单口号。
- 不要忽略不可逆损失和少数派反对意见。
- 不要在没有联网工具时声称已经完成外部事实、政策或市场核验。
- 高风险法律、医疗、金融、安全和合规判断必须外部验证。
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.
- 9d ago First seen · 107 lines · 44 tokens per session scan A 63dad60acbba
cogx-decision-memo is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,080 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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