Agent skill

authorship-credit-gen

Use when determining author order on research manuscripts, assigning CRediT contributor roles for transparency, documenting individual contributions to collaborative projects, or resolving authorship disputes in multi-institutional research. Generates fair and transparent authorship assignments following ICMJE guidelines and CRediT taxonomy. Helps research teams document contributions, resolve disputes, and ensure equitable credit distribution in academic publications.

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill authorship-credit-gen --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: none
Version: 1.0
Allowed tools: ReadWriteBashEdit
Path: skills/analysis/authorship-credit-gen/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Research Authorship and Contributor Credit Generator ## When to Use This Skill - determining author order on research manuscripts - assigning CRediT contributor roles for transparency - documenting individual contributions to collaborative projects - resolving authorship disputes in multi-institutional research - preparing contributor statements for journal submissions - evaluating contribution equity in research teams ## Quick Start ```python from scripts.main import AuthorshipCreditGen # Initialize the tool tool = AuthorshipCreditGen() from scripts.authorship_credit import AuthorshipCreditGenerator generator = AuthorshipCreditGenerator(guidelines="ICMJEv4") # Document contributions contributions = { "Dr. Sarah Chen": [ "Conceptualization", "Methodology", "Writing - Original Draft", "Supervision" ], "Dr. Michael Roberts": [ "Data Curation", "Formal Analysis", "Writing - Review & Editing" ], "Dr. Lisa Zhang": [ "Investigation", "Resources", "Validation" ] } # Generate fair authorship order authorship = generator.determine_order( contributions=contributions, criteria=["intellectual_input", "execution", "writing", "supervision"], weights={"intellectual_input": 0.4, "execution": 0.3

What's inside
Steps it walks through
  1. When to Use This Skill
  2. Quick Start
  3. Core Capabilities
  4. 1. Generate Fair Authorship Orders
  5. 2. Assign CRediT Roles
  6. 3. Detect Contribution Inequities
  7. 4. Generate Journal-Ready Statements
  8. Command Line Usage
  9. Best Practices
  10. Quality Checklist
  11. References
Ships with 1 file
  • metadata.json
Commands it runs
python scripts/main.py --contributions contributions.json --guidelines ICMJE --output authorship_order.json
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About this skill
What does the authorship-credit-gen skill do?

Use when determining author order on research manuscripts, assigning CRediT contributor roles for transparency, documenting individual contributions to collaborative projects, or resolving authorship disputes in multi-institutional research. Generates fair and transparent authorship assignments following ICMJE guidelines and CRediT taxonomy. Helps research teams document contributions, resolve disputes, and ensure equitable credit distribution in academic publications.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill authorship-credit-gen --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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