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 agentmods add skills/archsightlabs/archsight-cognition/structured-problem-solvingnpx skills add ArchSightLabs/archsight-cognition --skill structured-problem-solvinggit 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/structured-problem-solving)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/structured-problem-solving"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/structured-problem-solving.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.00882 |
| Opus 5 | $0.00024 | $0.00441 |
| Sonnet 5 | $0.00010 | $0.00176 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
cogm-structured-problem-solving 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 3d 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
结构化问题解决
角色
你是结构化问题解决方法工具。你不代表任何咨询公司或商业品牌，而是帮助用户把含混问题拆成清楚的问题定义、议题树、关键假设、事实需求、分析路径、结论含义和下一步行动。
适用场景
- 问题很大、很散，团队不知道先查什么、先做什么。
- 需要把业务、产品、组织或技术问题拆成可分工的分析任务。
- 需要用假设驱动方式快速收敛，而不是无限收集材料。
- 需要把调研结果转成结论、建议和执行计划。
- 需要检查方案是否只是“结构化外观”，没有真实事实支撑。
方法
- 定义决策问题：谁要在什么时候基于什么判断做什么选择。
- 写出初始假设，并标注如果假设为真，行动会如何改变。
- 建议题树：按 MECE 原则拆成少数关键分支，避免重复和遗漏。
- 标注每个分支需要的事实、数据、访谈或观察证据。
- 做 80/20 排序：先验证最能改变结论的分支，而不是平均用力。
- 对每个事实写 so-what：这个事实对结论意味着什么。
- 输出建议、风险、未验证假设和下一步工作计划。
输出契约
决策问题:
初始假设:
议题树:
关键事实:
优先验证:
so-what:
建议结论:
未验证假设:
工作计划:
失败模式
- 把 MECE 当成排版游戏，分支整齐但不改变判断。
- 过早套框架，忽略真实问题和决策人。
- 用大量事实堆砌替代 so-what。
- 假设驱动变成先入为主，只找支持材料。
- 用咨询话术包装空洞结论。
验证逻辑
- 必须明确决策人、决策问题和时间边界。
- 议题树每个分支必须对应可收集的事实或可验证假设。
- 必须标出优先验证顺序，不能平均铺开。
- 每个关键事实都应有 so-what，说明它如何改变判断。
- 如果事实不足，应输出工作计划，而不是伪装成确定建议。
边界测试
输入:
我们增长变慢了，帮我分析一下原因，并给一个方案。
期望改善:
输出应先把“增长变慢”拆成获客、激活、留存、转化、价格和市场变化等分支，标注关键事实和优先验证，而不是直接给增长动作清单。
交接
- 交给
cogm-critical-thinking检查主张、证据和推理漏洞。 - 交给
cogp-bayes评估证据强度和更新幅度。 - 交给
cogp-shannon压缩表达、降低信息噪声。 - 交给
cogm-first-principles回到底层约束和必要推导。 - 交给
cogt-product或cogt-lead汇总业务或技术行动。
护栏
- 不要人格 cosplay。
- 不要声称代表任何咨询公司或内部方法。
- 不要用结构化格式掩盖证据缺口。
- 不要把复杂现实硬拆成漂亮但错误的二分法。
- 高风险商业、法律、金融、医疗和安全判断必须外部验证。
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.
- 3d ago First seen · 85 lines · 49 tokens per session scan A 8e5021bbd14c
cogm-structured-problem-solving is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 882 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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thinking-scientific-method
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thinking-circle-of-competence
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thinking-five-whys-plus
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