19 lines
1.4 KiB
Markdown
19 lines
1.4 KiB
Markdown
---
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description: Simulate an AI research peer review with likely objections, severity, and a concrete revision plan.
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args: <artifact>
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section: Research Workflows
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topLevelCli: true
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---
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Review this AI research artifact: $@
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Derive a short slug from the artifact name (lowercase, hyphens, no filler words, ≤5 words). Use this slug for all files in this run.
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Requirements:
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- Before starting, outline what will be reviewed, the review criteria (novelty, empirical rigor, baselines, reproducibility, etc.), and any verification-specific checks needed for claims, figures, and reported metrics. Present the plan to the user. If this is an unattended or one-shot run, continue automatically. If the user is actively interacting, give them a brief chance to request changes before proceeding.
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- Spawn a `researcher` subagent to gather evidence on the artifact — inspect the paper, code, cited work, and any linked experimental artifacts. Save to `<slug>-research.md`.
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- Spawn a `reviewer` subagent with `<slug>-research.md` to produce the final peer review with inline annotations.
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- For small or simple artifacts where evidence gathering is overkill, run the `reviewer` subagent directly instead.
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- If the first review finds FATAL issues and you fix them, run one more verification-style review pass before delivering.
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- Save exactly one review artifact to `outputs/<slug>-review.md`.
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- End with a `Sources` section containing direct URLs for every inspected external source.
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