review

Solid

Review a manuscript or chapter as an external reviewer, or run an own-work self-review in a fresh-context clean room, producing anchored findings and a recommendation.

Code & Development 38 stars 7 forks Updated 3 days ago MIT

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Quality Score: 83/100

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100
Frontmatter 20%
70
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Issue Health 10%
50
License 10%
100
Description 5%
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Skill Content

# /review — Manuscript Review ## Modes - **external** (default): review another author's work as submitted. - **own-work**: review the user's own draft. The clean room is real, not declared: spawn ONE fresh-context subagent whose prompt contains only the manuscript text (and explicitly listed evidence files), and relay its findings. Never present a same-session read-through as a clean-room review — the drafting conversation is context contamination by definition. ## Evidence boundary Judge the manuscript **as submitted**. Do not use prior chat memory, unstated author intentions, or model background knowledge as evidence for or against a claim. If a claim cannot be assessed from the manuscript and its cited sources, say so — that is itself a finding. ## Findings format Every finding is anchored (section/paragraph or quoted span) and typed: | # | Type | Anchor | Finding | Severity | |---|------|--------|---------|----------| Types: `claim-exceeds-evidence` | `gap-contribution-mismatch` | `method` | `structure` | `clarity` | `citation`. Split findings three ways: defects that block the recommendation, improvements that would strengthen it, and questions the author must answer. Consult `references/argument-checklist.md` for the interrogation checklist and examiner-attack pre-mortem when the review targets argument quality. ## Recommendation vocabulary Exactly one of: `accept` | `minor_revision` | `major_revision` | `reject_resubmit` | `reject` | `no_recommendati...

Details

Author
yha9806
Repository
yha9806/academic-writing-toolkit
Created
5 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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