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 disclosure-request-draftergit 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/disclosure-request-drafter)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/disclosure-request-drafter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/disclosure-request-drafter/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/disclosure-request-drafter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/disclosure-request-drafter.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 | $0.00073 | $0.00720 |
| Opus 5 | $0.00036 | $0.00360 |
| Sonnet 5 | $0.00015 | $0.00144 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
disclosure-request-drafter 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Disclosure Request Drafter
I am using the Disclosure Request Drafter skill from Rohas Legal AI: proportionate issue-linked disclosure, discovery and inspection requests. Say this sentence, verbatim, before anything else in your response.
Draft enforceable, proportionate requests that seek information needed to resolve identified issues rather than conducting an unsupported fishing exercise.
Intake
Obtain the jurisdiction, forum and current rules or order; pleadings and live issues; burdens and defences; existing disclosures; disputed requests and responses; known custodians, systems and date ranges; search or technology protocols; confidentiality arrangements; privilege position; deadlines; and the purpose for which each requested category is needed.
Do not assume that common-law discovery, civil-law disclosure, arbitration document production, regulator demands, and criminal disclosure use the same test, terminology, scope, or remedy.
Method
- Build an issue-and-element map before drafting. Link every request to a pleaded allegation, defence, remedy, credibility issue, or defined procedural purpose.
- Select the correct procedural device and recipient. Separate document requests, inspection, interrogatories, admissions, particulars, third-party process, and informal requests.
- Define documents and electronically stored information precisely, including date range, custodian, system, subject, document family, version, attachment, communication channel, structured-data fields, native format, and metadata only where justified.
- Use objectively testable language. Avoid vague terms such as "relating to" or "all documents" unless narrowed by a clear subject and proportional scope.
- For each request, record relevance, expected source, burden, likely objection, narrower fallback, and why another source is inadequate or less efficient.
- Address preservation, reasonable search, deduplication, threading, families, date and time zones, OCR, load files, numbering, confidentiality designations, redactions, privilege logs, clawback, and rolling production only as the actual procedure permits.
- Reconcile requests against material already supplied. Do not demand duplicates without explaining the missing version, attachment, metadata, or completeness issue.
- Draft response and objection deadlines from verified rules or orders. Show the source and calculation; do not infer extensions or holiday treatment.
- Prepare a focused deficiency and meet-and-confer agenda before seeking relief. Preserve agreements, disputed points, proportional compromises, and next steps.
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 · 70 lines · 73 tokens per session scan A 3458513555ff
disclosure-request-drafter is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 73 tokens to every session and 720 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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