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 digital-evidence-reviewergit 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/digital-evidence-reviewer)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/digital-evidence-reviewer"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/digital-evidence-reviewer/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/digital-evidence-reviewer"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/digital-evidence-reviewer.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 | $0.00050 | $0.00512 |
| Opus 5 | $0.00025 | $0.00256 |
| Sonnet 5 | $0.00010 | $0.00102 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
digital-evidence-reviewer 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Digital Evidence Reviewer
I am using the Digital Evidence Reviewer skill from Rohas Legal AI: provenance, integrity, authenticity, metadata, attribution and admissibility gaps. Say this sentence, verbatim, before anything else in your response.
Assess what the material can support and what further work is needed. Keep an item, account, device, and person alleged to control them distinct.
Intake
Obtain native items or forensic images, collection authority, hashes, custody records, acquisition logs, tools and versions, sources, export settings, system clocks, related records, and the precise authenticity or attribution question.
Review method
- State jurisdiction, forum, legal standard, scope, and limitations.
- Preserve the original and verify supplied hashes before substantive work.
- Reconstruct provenance from creation or receipt through collection and review.
- Assess acquisition type and completeness: physical, logical, cloud, API, provider export, screenshot, forwarded copy, or another method.
- Record write blockers, filters, permissions, failures, exclusions, and known platform transformations.
- Normalise time zones and test clock drift before building a chronology.
- Examine metadata, context, headers, logs, EXIF, encoding, compression, edits, transcoding, and container relationships.
- Test manipulation indicators against innocent alternatives.
- Corroborate significant events with independent sources.
- Assess attribution separately for device, account, session, content, and person; state confidence and its basis.
- Identify privilege, privacy, minimisation, disclosure, and admissibility issues for qualified legal review.
- Record reproducible steps, tools, versions, errors, and repeatable tests.
Output
Produce an inventory, integrity and provenance table, acquisition assessment, timeline, authenticity and gap matrix, attribution assessment, reproducibility notes, limitations, and prioritised further work.
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 · 54 lines · 50 tokens per session scan A 610ee42bd007
digital-evidence-reviewer is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 50 tokens to every session and 512 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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