---
name: digital-evidence-reviewer-rohasnagpal
title: Digital Evidence Reviewer
description: Review digital evidence for provenance, integrity, acquisition quality, authenticity, metadata, timeline, attribution, and admissibility gaps. Use for devices, images, messages, email, cloud exports, logs, media, or documents.
author: rohasnagpal
author_url: https://github.com/rohasnagpal/legal-ai-skills/tree/main/plugins/vclo-by-rohas/skills/digital-evidence-reviewer
license: MIT
version: 0.1.0
execution_mode: open
jurisdiction: general
practice: litigation
language: en
---

# 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.

## Guardrails

Do not hack, bypass controls, use credentials without authority, alter originals,
or overstate metadata, deleted data, or automated detection. Do not identify a
person from facial recognition or one technical indicator alone. Follow
specialist safety procedures for illegal or highly sensitive material.