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-ladder-of-inference-checkgit 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-ladder-of-inference-check)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-ladder-of-inference-check"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-ladder-of-inference-check/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-ladder-of-inference-check"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-ladder-of-inference-check.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 | $0.00071 | $0.00806 |
| Opus 5 | $0.00036 | $0.00403 |
| Sonnet 5 | $0.00014 | $0.00161 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
think-ladder-of-inference-check 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ladder of Inference Check
People move from observation to action up an invisible, near-instant ladder: all available data, the data they select, the meaning they add, the assumptions they make, the conclusion they draw, the action they take. The leaps feel like facts. This skill slows the climb back down for a given conclusion: it reconstructs the rungs, exposes where selection and interpretation crept in, flags the riskiest leap, and tests at least one alternative interpretation of the same data. The output is an annotated reasoning trace, not prose.
When to Use
- A conclusion feels certain but actually rests on interpretation.
- A disagreement traces to two people reading the same situation differently.
- Auditing a contested inference, including the agent's own conclusion.
- As a step in a reasoning-audit workflow, often after an evidence/inference sort.
When NOT to Use
- The conclusion follows from direct, verifiable data with no real inferential leap.
- To generate ideas or options (wrong tool).
- On trivial matters where the climb does not matter.
- As a way to dress up and defend the conclusion you already hold.
Instructions
When asked to check the ladder of inference, follow these steps:
- State the conclusion being examined, in one sentence.
- List the observable data available - everything that could have been noticed, not just what was used.
- Identify the data actually selected - which subset the conclusion was built on, and what was left out.
- Surface the meaning and assumptions added - the interpretation laid on the selected data, and the assumptions that interpretation requires.
- Flag the riskiest rung - the single leap most likely to be wrong or selective.
- Test an alternative interpretation - give at least one credible different reading of the same data, and what it would imply.
- Emit the reasoning trace 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 · 65 lines · 71 tokens per session scan A 4480bb5c2946
think-ladder-of-inference-check 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 71 tokens to every session and 806 once invoked, about $0.0004 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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