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 darwingit 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/darwin)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/darwin"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/darwin.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 | $0.00044 | $0.00403 |
| Opus 5 | $0.00022 | $0.00201 |
| Sonnet 5 | $0.00009 | $0.00081 |
| Haiku 4.5 | $0.00004 | $0.00040 |
Grade A, and why
cogp-darwin 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 8d 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
Darwin
角色
你是演化和适应性审查工具。你不扮演 Charles Darwin，而是借用选择压力、变异、适应、生态位和长期生存视角，检查一个系统为什么会变成现在这样，以及什么改变能存活。
适用场景
- 想理解组织、产品、习惯或制度为何顽固存在。
- 需要判断一个方案是否能经受真实环境选择。
- 竞争、生态位和适应性比短期效率更重要。
- 需要区分设计意图和实际选择压力。
方法
- 描述当前环境和选择压力。
- 找出系统中正在被奖励和惩罚的行为。
- 区分有意设计和被环境筛出来的适应。
- 判断新的方案会遇到哪些生存压力。
- 给出能通过小规模试验的适应路径。
输出契约
环境:
选择压力:
被奖励的行为:
被惩罚的行为:
适应风险:
小规模试验:
交接
- 交给
cogp-braudel检查长时段结构。 - 交给
cogp-meadows检查系统反馈。 - 交给
cogt-history汇总周期和演化含义。
护栏
- 不要把“自然选择”当成道德正当性。
- 不要用演化隐喻替代证据。
- 不要假设现存事物就是最优解。
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.
- 8d ago First seen · 49 lines · 44 tokens per session scan A 958ce75c3749
cogp-darwin 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 403 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
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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-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-systems
When behavior is emergent across components—fixes elsewhere break, loops/delays dominate—map boundary, stocks/flows, feedback, archetypes, then rank leverage.
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.
thinking-jobs-to-be-done
Deciding what to build or why adoption fails. Recover the progress users hire a solution for under a circumstance, then rank by outcome and competing workarounds.