agentleFS
45ck45ck/video-evaluator/skills/package-review-prompt/SKILL.md

package-review-prompt

Skill2 starsChanged 7 months agoMITView on GitHub

Assemble a grounded review prompt from a local artifact bundle so Codex or Claude Code can inspect the right files and ask the right questions.

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 6 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this when you want a reviewer agent to sound grounded in the real run artifacts rather than hallucinating the review surface.

    line 8
  2. Very specific:

    Use the same bundle locators as video-artifact-intake, plus optional review context:

    line 19
  3. Very specific:

    When video.shots.json exists, the prompt includes a short shot-structure preview.

    line 39
  4. Very specific:

    Run after video-artifact-intake or review-bundle if you need to delegate a review.

    line 59
  5. Distinctive:

    Use timeline evidence to ground ordering, caption, transcript, and action claims before relying on sparse storyboard frames.

    line 70
  6. Distinctive:

    Use shot evidence to choose which video segments or representative frames need closer inspection.

    line 73
  7. Distinctive:

    Use segment evidence as the fastest routing layer for which parts have usable, weak, or empty evidence.

    line 76
  8. Distinctive:

    Do not use this skill to summarize artifacts you have not generated or found.

    line 81
  9. Distinctive:

    Do not ask a reviewer to evaluate claims that are unsupported by the bundle, unless they are explicitly framed as open questions.

    line 82

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Package Review Prompt

Use this when you want a reviewer agent to sound grounded in the real run artifacts rather than hallucinating the review surface.

This skill produces:

  • bundle summary
  • artifact-aware prompt text
  • explicit review focus areas

Inputs

Use the same bundle locators as video-artifact-intake, plus optional review context:

{
  "latestPointerRoot": "./output",
  "specPath": "./specs/demo-review.md",
  "focus": ["caption readability", "failure trace"]
}

focus defaults to an empty array. specPath is optional and should point to a local spec or brief the reviewer should consider.

Outputs

The result contains a grounded prompt assembled from discovered artifacts, recommended focus areas, and optional spec/focus inputs. It is designed to be handed to Codex or Claude Code for the next review step. When timeline.evidence.json exists, the prompt includes a short timeline preview. When video.shots.json exists, the prompt includes a short shot-structure preview. When segment.evidence.json exists, the prompt includes a short segment-evidence preview. When canonical analyzer files exist at the bundle root, such as analyzer.report.json, media-probe.json, quality-gates.json, or caption-artifact.json, the prompt lists them as report evidence so the reviewer starts from the shared analyzer output.

The prompt also routes common reviewer failure modes:

  • quality-gates.json failures and warnings, with first timestamped evidence
  • quality.json failures and warnings from consumer repos such as demo-machine
  • demo-visual-review.diff.json and golden-frame.diff.json mismatch summary
  • legacy visual-diff-report.json regression summaries when present
  • demo-capture-evidence.json or screenshots/manifest.json screenshot evidence
  • caption-artifact.json readability or sync risk plus caption-related gates
  • layout-safety.report.json caption, safe-zone, OCR, and overlap issues
  • an explicit suggested first artifact to inspect next

Sequencing Guidance

Run after video-artifact-intake or review-bundle if you need to delegate a review. If storyboard artifacts are needed but absent, run storyboard-extract, storyboard-ocr, and optionally storyboard-transitions/storyboard-understand before packaging the prompt.

Interpretation Notes

The generated prompt is a routing aid, not a review verdict. It should point the next agent at real files and questions. If the bundle is sparse, the prompt should preserve uncertainty rather than invent missing evidence.

Use timeline evidence to ground ordering, caption, transcript, and action claims before relying on sparse storyboard frames.

Use shot evidence to choose which video segments or representative frames need closer inspection. Do not treat shot boundaries as semantic proof by themselves.

Use segment evidence as the fastest routing layer for which parts have usable, weak, or empty evidence.

Abstention Rules

Do not use this skill to summarize artifacts you have not generated or found. Do not ask a reviewer to evaluate claims that are unsupported by the bundle, unless they are explicitly framed as open questions.

Failure Modes

Wrong bundle locators produce a grounded prompt for the wrong run. Missing specPath should not fail the run if omitted, but an invalid provided path can make the prompt incomplete or misleading. If discovered artifacts are sparse, run intake/storyboard steps first.

Repo-side runner:

node --import tsx scripts/harness/package-review-prompt.ts

Installed-pack runner:

node ./.video-evaluator/agent/run-tool.mjs package-review-prompt

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.