digital-evidence-reviewer

digital-evidence-reviewer is a skill for Claude Code, Codex from rohasnagpal/legal-ai-skills. It costs 50 tokens per session (512 once invoked), scanned A, original, MIT.

A review tool for checking whether digital material can be trusted and used as evidence. It examines where the material came from, whether it was changed, who controlled it, its timing, and gaps that may affect whether a court accepts it.

In plain words
What is it for?
Use it to review evidence from devices, email, cloud services, screenshots, media, logs, or documents, and identify missing originals, hashes, custody records, acquisition details, or attribution proof.
Why use it?
It helps separate what files, messages, images, logs, or exports actually prove from claims that still need verification.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the rohas-legal-ai plugin — 149 skills shipped together

Good fit Use it to review evidence from devices, email, cloud services, screenshots, media, logs, or documents, and identify missing originals, hashes, custody records, acquisition details, or attribution proof.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rohasnagpal/legal-ai-skills/digital-evidence-reviewer
Install

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.

Any agent
npx skills add rohasnagpal/legal-ai-skills --skill digital-evidence-reviewer
Clone the repo
git clone --depth 1 https://github.com/rohasnagpal/legal-ai-skills

Made for: Claude Code, Codex.

Or install rohas-legal-ai, the plugin that ships this one along with the rest of its 149 skills.

Wrote 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.

agentmods badge for digital-evidence-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/digital-evidence-reviewer/github.svg)](https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/digital-evidence-reviewer)
Your own site
<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.

agentmods 80×15 button for digital-evidence-reviewer

Your own site · 80×15
<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>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 512 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash 610ee42bd007, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

plugins/rohas-legal-ai/skills/digital-evidence-reviewer/SKILL.md · 54 lines

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

  1. State jurisdiction, forum, legal standard, scope, and limitations.
  2. Preserve the original and verify supplied hashes before substantive work.
  3. Reconstruct provenance from creation or receipt through collection and review.
  4. Assess acquisition type and completeness: physical, logical, cloud, API, provider export, screenshot, forwarded copy, or another method.
  5. Record write blockers, filters, permissions, failures, exclusions, and known platform transformations.
  6. Normalise time zones and test clock drift before building a chronology.
  7. Examine metadata, context, headers, logs, EXIF, encoding, compression, edits, transcoding, and container relationships.
  8. Test manipulation indicators against innocent alternatives.
  9. Corroborate significant events with independent sources.
  10. Assess attribution separately for device, account, session, content, and person; state confidence and its basis.
  11. Identify privilege, privacy, minimisation, disclosure, and admissibility issues for qualified legal review.
  12. 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.

Read the full file on GitHub · 54 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 54 lines · 50 tokens per session scan A 610ee42bd007

Subscribe to this mod's changes

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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