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 human-avatar/skills-for-humanity --skill s4h-historical-lesson-extractiongit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-historical-lesson-extraction)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-historical-lesson-extraction"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-historical-lesson-extraction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-historical-lesson-extraction"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-historical-lesson-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.01201 |
| Opus 5.5 | $0.00029 | $0.00480 |
| Sonnet 5.5 | $0.00015 | $0.00240 |
| Haiku 4.5 | $0.00007 | $0.00120 |
Grade A, and why
s4h-historical-lesson-extraction 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Historical Lesson Extraction
Every case contains multiple lessons. Most people extract the wrong one — the surface action rather than the underlying principle. "They moved fast" is not a lesson. "Speed of iteration was decisive because the cost of a wrong assumption exceeded the cost of an incomplete product, making information the binding constraint" is a lesson. This skill separates what happened from why it happened, and from that derives a principle that transfers to contexts the original case never anticipated.
Your Process
Step 1: Describe the Case What happened? Who was involved, what decisions were made, what were the outcomes? Provide enough specifics to work with — the analysis depends on the case having real texture, not just a summary.
Framing check: Confirm the specific historical case before continuing. State what you've identified — the case, the key actors or decisions involved, and the outcome being examined — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the specific case and outcome]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Surface Events What happened at the observable level — the actions taken, the decisions made, the sequence of events from beginning to outcome? Keep this purely descriptive. No interpretation, no causation claims yet. Just what an observer would have recorded.
Step 3: Underlying Dynamics Why did this happen? What forces, incentives, constraints, beliefs, or structural conditions drove the observable events? Ask: what would have had to be different for the outcome to change? The answer identifies the causal variables.
Step 4: Abstract the Principle Strip away names, technologies, industries, time period, and geographic context. What is the underlying rule this case illustrates? State it as a transferable principle: "When [conditions], [variable] tends to produce [outcome] — because [mechanism]." The mechanism is the crucial part — without it the principle can't be tested or applied intelligently.
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.
- 1mo ago First seen · 121 lines · 73 tokens per session scan A 9228773b1261
s4h-historical-lesson-extraction is a skill published in the GitHub repository human-avatar/skills-for-humanity (236 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 1,201 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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