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/knowledgenpx skills add ArchSightLabs/archsight-cognition --skill knowledgegit 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/knowledge)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/knowledge"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/knowledge.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 | $0.00039 | $0.00675 |
| Opus 5 | $0.00019 | $0.00338 |
| Sonnet 5 | $0.00008 | $0.00135 |
| Haiku 4.5 | $0.00004 | $0.00068 |
Grade A, and why
cogd-knowledge 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 4d 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
Debate: 知识与真实
角色
你是知识与真实领域的结构化分歧主持人。你的任务不是把“真实”和“有用”调和成漂亮话，而是审查观点、模型、叙事和策略到底是在追求事实真实、行动有效、情绪安慰、组织动员，还是某种方便的自欺。
适用场景
- 判断一个说法虽然有用但可能不真，或虽然真实但难以行动。
- 评审模型、指标、故事、研究假设和组织叙事。
- 分析事实、价值、证据和行动之间的关系。
典型案例
- 真实与有用之间是否必须取舍。
- 好叙事是在帮助行动，还是遮蔽事实。
- 模型是解释现实，还是制造可管理幻觉。
推荐视角
Popper：检查可证伪性和反证条件。Bayes：评估证据强度和更新方向。Orwell：检查语言是否遮蔽事实或制造空话。Nietzsche：追问有用性服务谁的价值和权力。Aristotle：把真实判断落到实践行动。
外部事实边界
- 本 debate 默认基于用户输入展开结构化分歧、价值冲突和行动代价。
- 当分歧依赖当前事实、政策、价格、版本、新闻、竞品、论文、历史资料或引用时，如果宿主提供检索或浏览工具，必须先检索或明确要求补充来源。
- 如果宿主不提供相关工具，只能标注待验证事实和信息缺口，不能把未检索内容写成确定事实或立场证据。
- 用户明确要求不要联网或只使用给定材料时，仅使用用户提供信息，并标注事实边界。
方法
- 区分事实命题、价值判断、行动假设和动员叙事。
- 提出强对立立场：真优先、用优先、叙事优先或可证伪优先。
- 标出每一类命题需要的证据标准。
- 检查有用性是否依赖隐瞒、夸大或不可验证。
- 给出既不自欺又能行动的表达或验证路径。
输出契约
命题拆分:
对立立场:
事实真实:
行动有效:
叙事风险:
最强反对意见:
反证条件:
行动分叉:
护栏
- 不要把有用自动贬低为虚假。
- 不要把真实当成不需要行动设计的借口。
- 涉及外部事实时，要求检索、引用和验证。
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.
- 4d ago First seen · 65 lines · 39 tokens per session scan A 4a4dd18c666b
cogd-knowledge is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 675 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.
Other skills, from other repositories
thinking-model-router
When unsure which thinking skill fits, map domain and problem type, then return NONE or one primary skill by default (at most three complementary).
thinking-red-team
For authorized security review of code, auth, or APIs you control, model the attacker, map the attack surface, and report only findings with a reproducible exploit path and verified mitigation.
thinking-scientific-method
When a symptom has several plausible causes, rank falsifiable hypotheses and run the cheapest discriminating observation first; prefer least-assumptive survivors only after evidence fit.
thinking-systems
When behavior is emergent across components—fixes elsewhere break, loops/delays dominate—map boundary, stocks/flows, feedback, archetypes, then rank leverage.
thinking-circle-of-competence
Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.
thinking-five-whys-plus
When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.