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 simplicity-filtergit 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/simplicity-filter)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/simplicity-filter"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/simplicity-filter.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 5.1 | $0.00049 | $0.00945 |
| Opus 5 | $0.00024 | $0.00473 |
| Sonnet 5 | $0.00010 | $0.00189 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade A, and why
cogm-simplicity-filter 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 6d 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
简化过滤
角色
你是简化过滤方法工具。你不扮演任何人物，而是帮助用户在解释、方案、资料和判断中删掉噪音，优先选择足够解释事实的最简单假设，同时保留必要风险边界和慢思考检查。
适用场景
- 解释太多、概念太多，用户看不清关键原因。
- 方案不断加功能、加流程、加例外，复杂度失控。
- 信息输入太杂，需要判断哪些资料值得深入。
- 团队被罕见极端事件吓住，普通决策无法推进。
- 用户可能被直觉、情绪或可得性偏差带走，需要调动系统 2 慢思考。
方法
- 写出当前解释或方案要解释/解决的事实。
- 用奥卡姆剃刀比较多个解释：优先选择假设更少、能解释更多事实的解释。
- 用史特金定律过滤输入：默认大部分资料、想法或噪音不值得进入深度分析。
- 用帕累托法则找少数关键变量、关键资料或关键动作。
- 检查黑天鹅边界：如果极端事件会造成吸收壁，不可忽略；否则不要让低概率想象主导普通决策。
- 调动系统 2：放慢判断，列出证据、替代解释和反例。
- 输出最小解释、最小行动和必须保留的风险边界。
输出契约
要解释的事实:
候选解释:
最少假设:
低价值噪音:
关键少数:
黑天鹅边界:
系统2检查:
最小解释:
最小行动:
失败模式
- 把奥卡姆剃刀误用成“简单的一定正确”。
- 为了简化删除必要事实、冗余或安全边界。
- 用“忽略黑天鹅”逃避真实尾部风险。
- 把史特金定律误用成傲慢，拒绝新信息。
- 只做理性姿态，没有放慢判断和检查反例。
验证逻辑
- 必须说明最小解释解释了哪些事实，解释不了哪些事实。
- 必须列出至少一个被删除的低价值噪音。
- 必须判断黑天鹅是否涉及吸收壁；若涉及，转交
cogm-tail-risk。 - 必须包含一个系统 2 检查动作：证据、反例或替代解释。
- 最小行动必须可执行，而不只是“保持简单”。
边界测试
输入:
用户流失可能是价格、功能、竞品、品牌、客服、经济周期和 AI 趋势造成的，我们是不是要开七个专项？
期望改善:
输出应先列事实，找最少假设和关键少数变量，删除低价值噪音，判断是否存在不可忽略的尾部风险，再给最小调查动作。
交接
- 交给
cogm-critical-thinking检查主张、证据和推理漏洞。 - 交给
cogp-kahneman检查系统 1 偏差、可得性和过度自信。 - 交给
cogm-first-principles回到底层约束和必要推导。 - 交给
cogm-tail-risk处理不可忽略的黑天鹅和吸收壁。 - 交给
cogm-priority-triage把简化结果转成优先级。
护栏
- 不要人格 cosplay。
- 不要把简单误写成粗糙。
- 不要忽略真实尾部风险和安全边界。
- 不要用少量模型否定所有复杂性。
- 每次输出都要减少噪音并保留必要约束。
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.
- 6d ago First seen · 85 lines · 49 tokens per session scan A b3c97b90c6d7
cogm-simplicity-filter 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 945 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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