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-evidence-vs-inference-sortgit 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-evidence-vs-inference-sort)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-evidence-vs-inference-sort"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-evidence-vs-inference-sort/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-evidence-vs-inference-sort"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-evidence-vs-inference-sort.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 | $0.00076 | $0.00906 |
| Opus 5 | $0.00038 | $0.00453 |
| Sonnet 5 | $0.00015 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00091 |
Grade A, and why
think-evidence-vs-inference-sort 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 11d 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.
Evidence vs Inference Sort
Reasoning degrades when evidence (what is actually observed or verifiable) is blended with inference (what is deduced) and assumption (an unstated premise). Models are especially prone to this: they present fluent inference in the same confident register as fact. This skill separates them: it labels each claim in a body of text as evidence, inference, or assumption, records the basis for each, attaches a confidence level to inferences, and flags anything uncited. The output is an evidence/inference ledger. Note the boundary: this classifies claim type; it does not verify that the evidence is true (that is a separate, fact-checking job).
When to Use
- A recommendation, plan, or conclusion must be trusted before it is acted on.
- High-stakes contexts: legal, medical, financial, safety, architecture and planning.
- Auditing the reasoning behind a conclusion, including the agent's own.
- As a step in a reasoning-audit workflow.
When NOT to Use
- As a fact-checker. It labels what kind of claim something is, not whether it is true.
- On creative or exploratory work where rigor is not the point.
- On trivial claims, where sorting produces only noise.
- When the claims are already well-sourced and the leaps are already explicit.
Instructions
When asked to sort evidence from inference, follow these steps:
- Collect the claims. Break the prompt, document, or proposed conclusion into discrete claims. Keep each to one assertion.
- Label each claim. Mark it Evidence (observed or verifiable, with a source), Inference (deduced from other claims), or Assumption (an unstated premise it depends on).
- Record the basis. For evidence, name the source or observation. For inference, name what it is inferred from. For assumption, state the premise plainly.
- Rate inference confidence. For each inference, assign high / medium / low and say why. Do not treat plausibility as verification.
- Flag the gaps. Mark anything presented as fact but uncited, and any load-bearing assumption that is unexamined.
- Surface the load-bearing unknowns. List the few unsupported claims that most need verification before the conclusion is trusted.
- Emit the ledger 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.
- 11d ago First seen · 65 lines · 76 tokens per session scan A 43710577e03b
think-evidence-vs-inference-sort 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 76 tokens to every session and 906 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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