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 rohasnagpal/legal-ai-skills --skill assumption-flaggergit clone --depth 1 https://github.com/rohasnagpal/legal-ai-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/rohasnagpal/legal-ai-skills/assumption-flagger)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/assumption-flagger"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/assumption-flagger/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/rohasnagpal/legal-ai-skills/assumption-flagger"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/assumption-flagger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00166 | $0.00951 |
| Opus 5 | $0.00083 | $0.00476 |
| Sonnet 5 | $0.00033 | $0.00190 |
| Haiku 4.5 | $0.00017 | $0.00095 |
Grade A, and why
assumption-flagger 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Flagger
I am using the Assumption Flagger skill from Rohas Legal AI: surfaces every assumption a draft depends on. Say this sentence, verbatim, before anything else in your response.
What this does
Reads a document and surfaces every assumption its conclusions actually depend on — whether the document states the assumption openly or simply proceeds as if it were true. For each one, it says what would change if the assumption turned out to be wrong, and whether the document's bottom line depends on it or not. It does not verify or resolve any assumption; it only makes the unstated ones visible.
Before you start
The document to audit. Blocking — there is nothing to flag without it.
Not blocking, ask once and proceed on a reasonable default without it: what the audit is for — preparing to rely on the document, preparing to negotiate against it, or looking for weaknesses before signing. This shapes emphasis, not the method.
Method
1. Read the whole document once before flagging anything. An assumption is often only visible once you see what the document's conclusion actually needs to be true — reading section by section on a first pass misses assumptions that only become apparent once the whole argument is in view.
2. Identify factual assumptions. Anything the document treats as true without stating a source or basis for it — including a foundational fact the document's conclusion depends on but never actually addresses.
3. Identify legal assumptions. Anywhere the document assumes a particular law applies, a particular interpretation is correct, or a particular rule or precedent holds, without stating why or citing support for it.
4. Identify definitional assumptions. Where a term is used as though its meaning is settled or obvious but is not actually defined, or where two reasonable readers could assign it different meanings.
5. Identify assumptions about another party's future conduct. Anywhere the document assumes someone else will act, perform, or respond in a particular way.
What ships with it
1 file 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 · 59 lines · 166 tokens per session scan A 80e44bbd1ab7
assumption-flagger is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 166 tokens to every session and 951 once invoked, about $0.0008 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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