video-evidence-review

video-evidence-review is a skill for Claude Code, Codex from Akakaui/visual-browser-agent. It costs 35 tokens per session (865 once invoked), scanned A, original, MIT.

A method for reviewing long video recordings as searchable sections instead of replaying the whole file. It starts with a quick index of frames, then examines only sections that may matter.

In plain words
What is it for?
Use it to locate events in interaction recordings, verify what happened during an automated session, and create storyboards for multi-step flows.
Why use it?
It reduces the time and context needed to find specific events in long recordings. It also avoids reviewing an entire recording when the first scan finds nothing relevant.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to locate events in interaction recordings, verify what happened during an automated session, and create storyboards for multi-step flows.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/akakaui/visual-browser-agent/video-evidence-review
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 Akakaui/visual-browser-agent --skill video-evidence-review
Clone the repo
git clone --depth 1 https://github.com/Akakaui/visual-browser-agent

Made for: Claude Code, Codex.

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 video-evidence-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/akakaui/visual-browser-agent/video-evidence-review/github.svg)](https://agentmods.dev/skills/akakaui/visual-browser-agent/video-evidence-review)
Your own site
<a href="https://agentmods.dev/skills/akakaui/visual-browser-agent/video-evidence-review"><img src="https://agentmods.dev/badge/skills/akakaui/visual-browser-agent/video-evidence-review/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 video-evidence-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/akakaui/visual-browser-agent/video-evidence-review"><img src="https://agentmods.dev/badge/skills/akakaui/visual-browser-agent/video-evidence-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 865 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.
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 $0.00035 $0.00865
Opus 5 $0.00017 $0.00432
Sonnet 5 $0.00007 $0.00173
Haiku 4.5 $0.00003 $0.00086

Measured 10d ago against content hash 745b8685611a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

video-evidence-review 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 10d 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.

skills/video-evidence-review/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Video Evidence Review Skill

When to Use

Use this skill to analyze recordings without blowing up context:

  • Long interaction captures (over 30 seconds)
  • Locating a specific event inside a long recording
  • Building storyboards of multi-step flows
  • Verifying what happened during an automated session

Core Principle

A recording is an INDEXABLE DATASET, not one giant prompt. Never feed an entire recording through review at once - chunk it, index it, then zoom in selectively.

Instructions

Step 1 - Chunking

Split the recording into 30-120 second windows:

  • Fixed-size chunks are fine for uniform captures
  • Prefer natural boundaries (navigations, idle gaps) when known
  • Assign stable IDs: chunk-001, chunk-002, ...

Step 2 - Three Review Levels

Escalate detail only where it pays off:

  1. Thumbnail index (always): one frame per chunk → cheap map of where things happen
  2. Selected frames: pull individual frames only from relevant chunks
  3. Full clip replay: ONLY for ambiguous or high-value events

If pass 1 (thumbnail index) finds nothing relevant, NEVER escalate to reviewing the whole recording - report "no relevant events found" and stop.

Frame Selection Policy

When sampling frames inside a chunk:

  • Static interval (no visual change): keep ONE frame for the stretch
  • Scene change (cut, navigation, modal): keep three frames - just BEFORE the cut, AT the cut, and one AFTER things stabilize
  • Fast motion: sample densely around that window only, sparsely elsewhere

Storyboard Concept

Assemble reviewed frames into a storyboard artifact:

  • Ordered keyframes carrying chunk ID + timestamp
  • One-line caption per frame describing state/action
  • The storyboard doubles as a shareable summary and an index back into the raw recording

Output

{
  "recording": "clips/checkout-flow.webm",
  "chunks": [
    { "id": "chunk-003", "window": "60-150s", "relevant": true },
    { "id": "chunk-004", "window": "150-210s", "relevant": false }
  ],
  "keyframes": [
    {
      "chunkId": "chunk-003",
      "timestamp": "96.5s",
      "reason": "scene-change",
      "caption": "Payment modal opens after Pay click"
    }
  ],
  "conclusion": "Checkout succeeds; one transient error toast at 148s self-clears."
}

Read the full file on GitHub · 102 lines

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. 10d ago First seen · 102 lines · 35 tokens per session scan A 745b8685611a

Subscribe to this mod's changes

video-evidence-review is a skill published in the GitHub repository Akakaui/visual-browser-agent (0 stars, last pushed 16d ago), licensed MIT. It adds 35 tokens to every session and 865 once invoked, about $0.0002 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 2026-08-31.

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