From dh-review
Digital humanities peer review simulation and supervisor feedback parsing. Simulates technical, humanistic, and interdisciplinary reviewer perspectives calibrated to your target venue. Parses unstructured supervisor feedback into a structured revision roadmap. The DH-specific "bridge review" evaluates whether your computational and humanistic work genuinely connect.
How this skill is triggered — by the user, by Claude, or both
Slash command
/dh-review:dh-reviewThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
`dh-review` provides two types of assistance:
agents/devil_advocate_agent.mdagents/humanities_reviewer_agent.mdagents/intake_agent.mdagents/interdisciplinary_reviewer_agent.mdagents/supervisor_feedback_agent.mdagents/technical_reviewer_agent.mdreferences/dh_review_criteria.mdreferences/reviewer_calibration_guide.mdreferences/supervisor_feedback_parsing_protocol.mddh-review provides two types of assistance:
Review simulation: Evaluates your DH paper from three perspectives — technical (methodology, data, reproducibility), humanistic (argument, scholarship, interpretation), and interdisciplinary (the DH bridge). Also includes devil's advocate challenge.
Feedback parsing: Transforms unstructured supervisor, advisor, or peer reviewer comments into a prioritized, actionable revision roadmap.
| Mode | Trigger | What Happens |
|---|---|---|
full-review | /dh-review or /dh-review full-review | Complete multi-perspective review (all 4 review agents) |
supervisor-feedback | /dh-review supervisor-feedback | Parse supervisor/reviewer comments → revision roadmap |
technical-focus | /dh-review technical-focus | Computational methodology and reproducibility review only |
humanities-focus | /dh-review humanities-focus | Humanistic argument and scholarly engagement review only |
interdisciplinary | /dh-review interdisciplinary | DH bridge review only — does the paper justify being DH? |
quick-assess | /dh-review quick-assess | Rapid high-level assessment; ~10 minutes |
Always start with intake: Read agents/intake_agent.md to detect input type and configure review balance.
Route by mode:
full-review: Execute all four review agents in sequence, then compile the Reviewer Decisionsupervisor-feedback: Execute supervisor_feedback_agent.md onlytechnical-focus: Execute technical_reviewer_agent.md onlyhumanities-focus: Execute humanities_reviewer_agent.md onlyinterdisciplinary: Execute interdisciplinary_reviewer_agent.md onlyquick-assess: Execute all four agents; limit each to 150 words; compile a condensed reportBefore the review, read references/reviewer_calibration_guide.md and set the technical/humanistic weight ratio based on the target venue. The balance shifts from 35/65 (DHQ) to 65/35 (Cultural Analytics).
After full-review, produce a single consolidated output:
## Reviewer Decision
**Paper**: [title]
**Target Venue**: [venue]
**Review Date**: [date]
**Overall Recommendation**: [Accept (minor revisions) | Minor Revisions | Major Revisions | Reject/Resubmit]
**Scores**:
| Criterion | Score (1–5) |
|---|---|
| Research Question Quality | [n] |
| Corpus Integrity | [n] |
| Methodological Soundness | [n] |
| Interpretive Depth | [n] |
| Scholarly Engagement | [n] |
| DH Bridge | [n] |
| **Overall** | **[n]** |
**Summary**: [2–3 paragraph integrated review — synthesis of all three reviewer perspectives]
**Revision Roadmap**: [Tier 1 / Tier 2 / Tier 3 items, consolidated from all reviewers]
After full-review: the Reviewer Decision + Revision Roadmap is detected by dh-write's intake agent when passed into a revision mode session.
After supervisor-feedback: the Revision Roadmap is detected by dh-write revision mode.
In dh-pipeline, Stage 5.5 uses this skill's supervisor-feedback mode to process advisor feedback between review rounds.
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
npx claudepluginhub shuke1999/digital-humanity-skills --plugin dh-review