S4h Historical Lesson Extraction logo

S4h Historical Lesson Extraction

Organization
human-avatar
s4h-historical-lesson-extraction

Extracts the transferable principle from a specific historical case — separating the contingent surface details from the underlying rule that applies across contexts. TRIGGERS: 'what's the lesson', 'extract the lesson', 'what should we learn from this', 'generalise from this case', 'apply this to our situation'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-historical-lesson-extraction
Stars
234
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by human-avatar on GitHub. Read the source before you install it.

Installation

Install the S4h Historical Lesson Extraction AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-historical-lesson-extraction
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity.git /tmp/skills-for-humanity
mkdir -p .claude/skills
cp -r /tmp/skills-for-humanity/skills/s4h-historical-lesson-extraction .claude/skills/s4h-historical-lesson-extraction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Historical Lesson Extraction in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads S4h Historical Lesson Extraction on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so S4h Historical Lesson Extraction is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Step 5: Test Transferability Under what conditions does this principle apply? List the conditions required. Then assess whether those conditions hold in the current situation. Where they hold, the principle transfers. Where they differ significantly, it may not — or may apply with modification.

Step 6: Apply with Caveats How does the principle apply specifically to the current situation? What would acting on it look like concretely? Name the caveats explicitly — the conditions under which this principle would give the wrong answer.


Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:

  • Question: "My read: [your 1–2 sentence interpretation]. How do you want to proceed?"
  • Header: "Scope"
  • Options:
    • Full analysis — Complete all steps, reasoning shown throughout
    • Key findings only — Bottom-line output, skip step-by-step detail
    • Transferable principle only — The single insight most applicable to the current situation
    • Reframe — The read is off; correct it and the analysis will follow the corrected framing

Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.

Output Format

Case: [specific description — enough detail to work with]

Surface Events: [what happened, in sequence, descriptively]

Underlying Dynamics: [why it happened — the forces and mechanisms that drove the outcome]

Abstract Principle: [the transferable rule, stated without domain-specific language, including the mechanism]

Transferability Assessment

Required ConditionPresent in Current Situation?Notes
[condition the principle requires][yes / no / partially][specific observation]

Application: [how the principle applies to the current situation — what it implies concretely]

Caveats: [conditions under which this principle would mislead]


Notes

The principle is wrong if it only describes the original case. Test it against three other cases in different domains — if it doesn't transfer, it's a description, not a principle. Keep abstracting until it does. The mechanism is the hardest and most important element: a principle without a mechanism is an observation, not a lesson.


What's Next

After delivering this output, use AskUserQuestion to offer the next move:

  • Question: "Lessons extracted. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-decision-criteria-weighting — Weight decision criteria by the historical lessons
    • /s4h-strategy-positioning — Position to apply what history teaches
    • /s4h-systems-leverage-analysis — Find leverage points the historical lessons reveal
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Historical Lesson Extraction AI skill do?

Extracts the transferable principle from a specific historical case — separating the contingent surface details from the underlying rule that applies across contexts. TRIGGERS: 'what's the lesson', 'extract the lesson', 'what should we learn from this', 'generalise from this case', 'apply this to our situation'.

Why use S4h Historical Lesson Extraction on TypingMind?

Because you install it once and use it with any model. S4h Historical Lesson Extraction is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install S4h Historical Lesson Extraction in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-historical-lesson-extraction. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Historical Lesson Extraction?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with S4h Historical Lesson Extraction?

As many as you like. As long as a model supports skills, you can use S4h Historical Lesson Extraction with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the S4h Historical Lesson Extraction AI skill free?

Yes. It is published on GitHub by human-avatar under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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