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 product-on-purpose/thinking-framework-skills --skill think-far-analogy-ideationgit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote 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/product-on-purpose/thinking-framework-skills/think-far-analogy-ideation)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-far-analogy-ideation"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-far-analogy-ideation/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/product-on-purpose/thinking-framework-skills/think-far-analogy-ideation"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-far-analogy-ideation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00065 | $0.00884 |
| Opus 5 | $0.00032 | $0.00442 |
| Sonnet 5 | $0.00013 | $0.00177 |
| Haiku 4.5 | $0.00006 | $0.00088 |
Grade A, and why
think-far-analogy-ideation 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 12d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Far-Analogy Ideation
Most ideation transfers solutions from near domains (products like yours), which yields obvious, low-novelty ideas. Far-analogy ideation deliberately reaches to distant domains - nature, other industries, games, history - and transfers the deep relational structure of a working solution there, not its surface features. The originality comes from the distance; the validity comes from mapping structure, not surface similarity. The output is a far-analogy transfer sheet of candidate mechanisms to adapt. The failure to avoid: surface-matching ("both involve networks"), which produces cute-but-useless analogies and carries none of the benefit.
When to Use
- Near, obvious solutions are exhausted or all look alike.
- You want genuinely original approaches, not incremental variations.
- The problem has a clear underlying structure that can be stated abstractly.
When NOT to Use
- An obvious near solution already exists and works (far analogy is overkill and riskier).
- When you need to converge and decide (use a decision skill).
- When only a surface match is available (a forced, surface-level analogy is worse than none).
- Execution tasks with no real ideation need.
Instructions
When asked to ideate by far analogy, follow these steps:
- State the deep structure. Abstract the problem to its relational core, stripped of domain surface ("an entity must attract the right partners at low cost, then convert low commitment to high"). This is the step that makes the analogy valid.
- Reach to distant domains. Find 2 to 3 domains far from the problem where that same structure is solved (biology, other industries, games, history). Deliberately avoid near, same-industry sources.
- Map mechanism to mechanism. For each, describe how that domain solves the structure - the mechanism, not the surface. Flag if a mapping is structural vs at risk of being surface-level.
- Transfer and adapt. Turn each mechanism into a concrete candidate idea for the actual problem.
- Shortlist as candidates. Select the most promising, flagged as candidates to test (not answers), noting what would have to be true.
- Emit the transfer sheet per
references/TEMPLATE.md.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 62 lines · 65 tokens per session scan A f4f3297e6afa
think-far-analogy-ideation is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed today), licensed Apache-2.0. It adds 65 tokens to every session and 884 once invoked, about $0.0003 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 2026-08-30.
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