| name | triaging-module-requests |
| description | Triage MultiQC `module: new` GitHub issues: calculate 0-100 priority scores, apply priority labels, post analysis comments with score breakdowns, and give contributors actionable feedback to improve their request. Use when a new `module: new` issue is opened, when a user comments `@claude analyze-module` on a request, during weekly bulk triage, or when manually re-evaluating a module request.
|
Triage MultiQC Module Requests
Invoked by .github/workflows/module-requests.yml on new module: new
issues, on @claude analyze-module comments, and on a weekly schedule
(Mondays 9 AM UTC). Also runs on manual workflow dispatch.
Workflow
- Pick a mode from context:
analyze-single — one issue (new issue or on-demand request)triage-all — every open module: new issue (weekly batch)dry-run — calculate and print, but make no GitHub changes
- Fetch issue data with
gh issue view / gh issue list. See
github-actions.md for the exact commands.
- Fetch tool metrics via
scripts/fetch-tool-metrics.js (GitHub stars,
PyPI/Conda downloads, last commit date).
- Calculate the priority score using the rubric in
scoring-criteria.md.
- Apply the priority label (see Priority bands below). Remove any
existing priority labels first; see github-actions.md.
- Post the analysis comment using
analysis-templates.md. Always show the score
breakdown, what's good, and concrete improvements (with
+X points
tags where they apply).
Priority bands
| Score | Band | Label |
|---|
| ≥70 | 🔴 High | module: prio-high |
| 40–69 | 🟡 Medium | module: prio-medium |
| 20–39 | 🟢 Low | module: prio-low |
| <20 | ⚪ Hold | module: prio-hold |
The five score categories (full rubric in scoring-criteria.md):
- Tool Popularity (25) — GitHub stars + maintenance bonus
- Package Downloads (15) — PyPI / Conda / Bioconda monthly
- Community Engagement (35) — reactions, comments, duplicates
- Request Quality (20) — completed fields + example files
- Technical Feasibility (15) — output format, metric clarity, parsing
Feedback principles
Be specific (point to exact fields), be encouraging (acknowledge strengths
first), be consistent (apply the rubric uniformly), be transparent (show
the calculation). Cache API results when batch-processing to stay under
rate limits.
