Agent skill

Hum Historical Analogy

by asgard-ai-platform in asgard-ai-platform/skills

Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations.

MITAuto-check passedLegal & Compliance

Install Hum Historical Analogy

skills CLI
$ npx skills add asgard-ai-platform/skills --skill hum-historical-analogy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills hum-historical-analogy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/hum-historical-analogy .claude/skills/hum-historical-analogy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hum-historical-analogy
GitHub stars
242
Token cost
~1.4k tokens
SKILL.md length
472 words
Files
3 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations.

  • Works in 6 steps: State the analogy explicitly: "Situation… → Map structural similarities: What causal… → Map structural differences: What is… → …
  • The user draws on historical precedent to justify a strategy
  • SKILL.md covers Overview, Framework, Output Format and Examples, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Hum Historical Analogy is an agent skill from asgard-ai-platform/skills. Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations. Use this skill when the user draws on historical precedent to justify a strategy, needs to evaluate whether a historical comparison is valid, or wants to learn from past events — even if they say 'this is like the dotcom bubble', 'history repeats itself', or 'what can we learn from how X handled this'.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `examples/sample_scenario.md` and `references/thinking-in-time.md`).

It sits in Legal & Compliance, covering Legal research. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user draws on historical precedent to justify a strategy
  • Needs to evaluate whether a historical comparison is valid
  • Wants to learn from past events — even if they say this is like the dotcom bubble
  • History repeats itself

Example prompts

  • “this is like the dotcom bubble”
  • “history repeats itself”
  • “what can we learn from how X handled this”
  • “/hum-historical-analogy”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. State the analogy explicitly: "Situation A is like historical event B because..."
  2. Map structural similarities: What causal mechanisms, dynamics, or patterns are shared?
  3. Map structural differences: What is fundamentally different?
  4. Assess the balance: Do similarities outweigh differences for the specific question at hand?
  5. Extract lessons carefully: What specific, actionable insight does the analogy provide?
  6. Identify the analogy's limits: Where does the analogy break down?

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hum Historical Analogy loads about 1.4k tokens when it runs, and up to ~4.7k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 472 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 472 words, ~1,429 tokens.

Download SKILL.mdSave it as .claude/skills/hum-historical-analogy/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
hum-historical-analogy
description
Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations. Use this skill when the user draws on historical precedent to justify a strategy, needs to evaluate whether a historical comparison is valid, or wants to learn from past events — even if they say 'this is like the dotcom bubble', 'history repeats itself', or 'what can we learn from how X handled this'.
metadata.category
WP-19 文學院/人文
metadata.tags
humanities, historical-analogy, strategic-thinking

Historical Analogy

Overview

Historical analogies apply lessons from past events to current decisions. When used rigorously, they provide pattern recognition and foresight. When used carelessly, they mislead by overfitting superficial similarities and ignoring structural differences.

Framework

IRON LAW: Structural Similarity, Not Surface Similarity

A valid analogy requires shared STRUCTURAL features (causal mechanisms,
power dynamics, systemic patterns), not just surface resemblance.

"This startup is the next Apple" because the founder wears turtlenecks =
surface similarity (worthless). "This market has the same demand-side
network effects as early smartphone adoption" = structural similarity (useful).
Analogy Evaluation Steps
  1. State the analogy explicitly: "Situation A is like historical event B because..."
  2. Map structural similarities: What causal mechanisms, dynamics, or patterns are shared?
  3. Map structural differences: What is fundamentally different?
  4. Assess the balance: Do similarities outweigh differences for the specific question at hand?
  5. Extract lessons carefully: What specific, actionable insight does the analogy provide?
  6. Identify the analogy's limits: Where does the analogy break down?
Common Analogy Traps
TrapDescriptionExample
Cherry-pickingSelecting only the historical case that supports your conclusion"Kodak failed to adapt, so we must pivot" (ignoring cases where staying the course was right)
Outcome biasUsing the historical outcome to validate the analogy"Amazon survived the dotcom bust, so we will too" (survivorship bias)
False precisionExpecting history to repeat exactly"The 2008 crisis took 18 months to recover, so this one will too"
PresentismJudging past decisions by present knowledge"They should have seen the crisis coming" (they didn't have today's data)

Output Format

markdown
# Historical Analogy Assessment: {Current Situation} ↔ {Historical Event}

## The Analogy
"{Current situation} is like {historical event} because..."

## Structural Similarities
| Feature | Historical | Current | Similarity |
|---------|-----------|---------|-----------|
| {mechanism} | {how it worked then} | {how it works now} | Strong/Moderate/Weak |

## Structural Differences
| Feature | Historical | Current | Impact on Analogy |
|---------|-----------|---------|------------------|
| {factor} | {then} | {now} | Weakens/Neutral/Strengthens |

## Validity Assessment
- Overall analogy strength: Strong / Moderate / Weak
- Valid for: {what aspects of the decision the analogy informs}
- Invalid for: {where the analogy breaks down}

## Lessons (with caveats)
1. {lesson} — caveat: {where this might not apply}

Examples

Correct Application

Scenario: "AI in 2025 is like the Internet in 1995"

Structural SimilarityInternet 1995AI 2025Strength
General-purpose technology enabling many applications✓✓Strong
Early hype cycle with inflated expectations✓ (dotcom)✓ (AI bubble concerns)Strong
Infrastructure buildout phase (broadband then, GPU/data centers now)✓✓Strong
Structural DifferenceInternet 1995AI 2025Impact
Deployment speedYears for broadband rolloutAI accessible via API in minutesWeakens (faster adoption)
Incumbent responseIncumbents slow to respond (Blockbuster, newspapers)Incumbents adopting aggressively (Microsoft, Google)Weakens (harder for startups)
Regulatory environmentMinimal regulationActive AI regulation globally (EU AI Act)Weakens (more constraints)

Verdict: Moderate analogy — valid for understanding the hype cycle pattern and infrastructure investment phase, but invalid for predicting startup vs incumbent dynamics ✓

Show full SKILL.md (148 more words)Show less
Incorrect Application
  • "AI is like the Internet, so all AI companies will succeed" → Cherry-picks the winners (Google, Amazon) and ignores that 90%+ of dotcom companies failed. Survivorship bias + surface similarity only. Violates Iron Law.

Gotchas

  • Multiple analogies exist: For any current situation, multiple historical parallels can be drawn — and they may suggest opposite conclusions. Consider 2-3 analogies, not just the most popular one.
  • The most popular analogy is often the worst: "This is like the dotcom bubble" is thrown around because it's familiar, not because the structural similarities are strong. Popularity ≠ validity.
  • Analogies work best for pattern recognition, not prediction: "This pattern has led to X before" is useful. "This will lead to X again" is overconfident.
  • Cultural and institutional context changes: Lessons from US business history may not apply to Taiwan's institutional environment. Account for systemic differences.

References

  • For Neustadt & May's "Thinking in Time" methodology, see references/thinking-in-time.md

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in hum-historical-analogy of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/thinking-in-time.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Hum Historical Analogy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Hum Historical Analogy compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Hum Historical Analogy this skillasgard-ai-platform/skills242—~1.4kAutomated safety check: PassMIT
Tw Legal RAGaa0101181514/tw-legal-rag328—~580Automated safety check: PassCustom licence
Design Award SearchSeanJ1ang/design-judge-skills712—~3kAutomated safety check: PassApache-2.0
China Lawyer AnalystCSlawyer1985/china-lawyer-analyst196—~3.3kAutomated safety check: PassNone
Billing And Litigation BudgetTHUYRan/Legal-Skills-Chinese874—~5kAutomated safety check: PassNone
Legal Issue ResearchGolden2002/legal-research-skill156—~6.8kAutomated safety check: PassMIT

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Questions about Hum Historical Analogy

What does Hum Historical Analogy do?

Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations. Hum Historical Analogy is an agent skill from asgard-ai-platform/skills. Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations.

When should I use Hum Historical Analogy?

Hum Historical Analogy fits situations like: the user draws on historical precedent to justify a strategy; needs to evaluate whether a historical comparison is valid; wants to learn from past events — even if they say this is like the dotcom bubble; history repeats itself.

How do I install Hum Historical Analogy in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill hum-historical-analogy -a claude-code`. Or copy the skill folder (hum-historical-analogy in asgard-ai-platform/skills) into .claude/skills/hum-historical-analogy in your project. Claude Code loads it when a task matches its description.

How do I install Hum Historical Analogy in Codex?

Run `npx skills add asgard-ai-platform/skills --skill hum-historical-analogy -a codex`. Or copy the skill folder (hum-historical-analogy in asgard-ai-platform/skills) into .agents/skills/hum-historical-analogy in your project. Codex loads it when a task matches its description.

Can I use Hum Historical Analogy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill hum-historical-analogy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hum-historical-analogy, .gemini/skills/hum-historical-analogy, .github/skills/hum-historical-analogy and .opencode/skills/hum-historical-analogy in your project.

What does Hum Historical Analogy need to run?

SKILL.md names no scripts, command-line tools or credentials: Hum Historical Analogy is instructions for the agent only.

Does Hum Historical Analogy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hum Historical Analogy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hum Historical Analogy use?

Hum Historical Analogy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hum Historical Analogy use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.2k tokens, read only when the agent opens those files.

What are the alternatives to Hum Historical Analogy?

Skills that share tags, products or a category with Hum Historical Analogy: Tw Legal RAG (aa0101181514/tw-legal-rag, 328 stars), Design Award Search (SeanJ1ang/design-judge-skills, 712 stars), China Lawyer Analyst (CSlawyer1985/china-lawyer-analyst, 196 stars) and Billing And Litigation Budget (THUYRan/Legal-Skills-Chinese, 874 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hum Historical Analogy?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.