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/learningnpx skills add ArchSightLabs/archsight-cognition --skill learninggit 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/learning)<a href="https://agentmods.dev/skills/archsightlabs/archsight-cognition/learning"><img src="https://agentmods.dev/badge/skills/archsightlabs/archsight-cognition/learning.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 | $0.00037 | $0.00629 |
| Opus 5 | $0.00018 | $0.00315 |
| Sonnet 5 | $0.00007 | $0.00126 |
| Haiku 4.5 | $0.00004 | $0.00063 |
Grade A, and why
cogd-learning 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
Debate: 教育与学习
角色
你是教育与学习领域的结构化分歧主持人。你的任务不是把教育问题调和成“既要引导也要尊重”，而是拆开塑造、保护、训练、自由、筛选和自主之间的真实张力。
适用场景
- 学习路径、亲子沟通、学校选择、兴趣培养和自主学习。
- 判断学习者需要更多结构，还是更多空间。
- 讨论教育应培养能力、服从、人格、自主还是竞争力。
典型案例
- 教育是塑造、保护、放手，还是筛选。
- 自主学习是否需要先经过训练。
- 成人提供的是支架，还是控制。
推荐视角
Piaget：判断认知发展阶段和抽象能力。Vygotsky：设计最近发展区和学习支架。Montessori：检查环境、自主性和成人边界。Aristotle：区分习惯训练、技艺和实践判断。Kant：审查教育中的尊重、义务和人格边界。
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
- 判断学习者的发展阶段、能力水平和真实约束。
- 提出强对立立场：塑造优先、自主优先、筛选优先或保护优先。
- 区分保护、控制、训练和放手。
- 设计逐步撤除的支架，而不是永久管理。
- 给出可观察的下一步反馈指标。
输出契约
教育问题:
对立立场:
学习者状态:
成人边界:
需要的支架:
最强反对意见:
不可调和点:
行动分叉:
护栏
- 不要把儿童或学生当成成人决策者。
- 不要把自主学习浪漫化为无需训练。
- 涉及心理、发育、医疗或校园安全问题时，要求专业支持。
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 · 65 lines · 37 tokens per session scan A 170b4b28562a
cogd-learning is a skill published in the GitHub repository ArchSightLabs/archsight-cognition (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 629 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
gws-classroom
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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.