review-bundle
Skill2 starsChanged 7 months agoMITView on GitHub
Review a local video artifact bundle, summarize report status across known artifacts, and point the agent at the first useful review surfaces.
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- compare-video-runsSkill
- install-skill-packSkill
- layout-safety-reviewSkill
- package-review-promptSkill
- segment-evidenceSkill
- segment-storyboardSkill
- skill-catalogSkill
- source-media-signalsSkill
- storyboard-extractSkill
- storyboard-ocrSkill
- storyboard-transitionsSkill
- storyboard-understandSkill
- video-artifact-intakeSkill
- video-shotsSkill
- video-technical-reviewSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 1 is distinctive, and 5 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this when a repo already produced a run and the agent needs the shared review layer rather than repo-specific generation logic.
- Very specific: line 19
Use the same bundle locators as video-artifact-intake, plus optional prompt hints:
- Very specific: line 40
Run video-artifact-intake first only if path resolution is ambiguous.
- Very specific: line 49
Treat fail or warn as routing signals to inspect the named artifact, not as complete diagnostics.
- Very specific: line 56
Do not claim the video is correct because overallStatus is pass.
- Very specific: line 62
If the run directory is wrong, the review will faithfully summarize the wrong bundle.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Review Bundle
Use this when a repo already produced a run and the agent needs the shared review layer rather than repo-specific generation logic.
This skill does not replace domain-specific QA. It tells the agent:
- what artifacts exist
- which reports passed, warned, or failed
- which surfaces deserve immediate human or agent attention
Inputs
Use the same bundle locators as video-artifact-intake, plus optional prompt
hints:
{
"outputDir": "./output/example-run",
"includePromptHints": true
}includePromptHints defaults to true. Set it to false when you only need a
machine-readable bundle/status summary.
Outputs
The result includes the normalized intake result plus a review-oriented summary of artifact availability, report status, recommended focus, and optional prompt hints. It does not open or deeply evaluate every report.
Sequencing Guidance
Run video-artifact-intake first only if path resolution is ambiguous;
otherwise review-bundle can do intake itself. If the review identifies
storyboard files, inspect them directly or continue with
storyboard-transitions/storyboard-understand. If storyboard files are absent
and a primary video exists, generate them with storyboard-extract and
storyboard-ocr.
Interpretation Notes
Treat fail or warn as routing signals to inspect the named artifact, not as
complete diagnostics. Treat unknown as insufficient structured evidence. A
present trace.zip means failure evidence may exist outside JSON and should be
unpacked or inspected if relevant to the task.
Abstention Rules
Do not claim the video is correct because overallStatus is pass; the skill
only summarizes known reports. Abstain from visual, caption, or UX judgments
unless the result points to concrete artifacts you inspect.
Failure Modes
If the run directory is wrong, the review will faithfully summarize the wrong
bundle. If reports use custom schemas without status, passed, or
hasFailures, they may appear only as present; read them manually before
concluding they passed.
Repo-side runner:
node --import tsx scripts/harness/review-bundle.ts
Installed-pack runner:
node ./.video-evaluator/agent/run-tool.mjs review-bundle
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

