data-management-plan-creator

Automatically generate NIH 2023-compliant Data Management and Sharing Plan (DMSP) drafts following FAIR principles

Quality

52%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/Academic Writing/data-management-plan-creator/SKILL.md

Quality

Discovery

40%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description identifies a clear, specialized niche (NIH data management plans) with good domain-specific terminology, making it distinctive. However, it lacks explicit trigger guidance ('Use when...') and could benefit from listing more concrete actions beyond just 'generate drafts'. The single-action focus and missing usage triggers significantly limit its effectiveness for skill selection.

Suggestions

Add a 'Use when...' clause with trigger scenarios like 'Use when preparing NIH grant submissions, creating data sharing plans, or when the user mentions DMSP, data management requirements, or NIH compliance'

Expand concrete actions beyond 'generate drafts' to include related capabilities like 'review existing plans', 'check compliance', or 'update plans for resubmission'

Include common term variations users might say: 'data sharing plan', 'DMS plan', 'NIH grant data requirements', 'R01 data plan'

Dimension	Reasoning	Score
Specificity	Names the domain (NIH DMSP) and one action (generate drafts), but doesn't list multiple concrete actions like reviewing, editing, or validating plans. The mention of 'NIH 2023-compliant' and 'FAIR principles' adds some specificity.	2 / 3
Completeness	Describes what it does (generate DMSP drafts) but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this has no 'when' component at all.	1 / 3
Trigger Term Quality	Includes relevant terms like 'NIH', 'Data Management and Sharing Plan', 'DMSP', and 'FAIR principles' that domain experts would use, but misses common variations like 'data sharing plan', 'DMS plan', 'grant data plan', or 'NIH grant requirements'.	2 / 3
Distinctiveness Conflict Risk	Very specific niche targeting NIH 2023 DMSP with FAIR principles compliance. Unlikely to conflict with other skills due to the highly specialized domain and explicit regulatory/compliance context.	3 / 3
	Total	8 / 12 Passed

Implementation

64%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill provides excellent actionable guidance with concrete CLI and Python examples, but is undermined by excessive boilerplate sections that don't help Claude execute the task. The core functionality is well-documented, but the document would benefit from removing template sections and adding validation steps for the generated DMSP output.

Suggestions

Remove boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that don't provide actionable guidance for generating DMSPs

Add a validation workflow: steps to verify the generated DMSP meets NIH requirements before finalizing

Move FAIR Principles Implementation details to a separate reference file, keeping only a brief summary inline

Remove the Overview paragraph that explains what NIH policy and FAIR principles are - Claude already knows this

Dimension	Reasoning	Score
Conciseness	The skill contains significant boilerplate sections (Risk Assessment, Security Checklist, Evaluation Criteria, Lifecycle Status) that add little value for Claude. The Overview section restates information Claude already knows about NIH policy and FAIR principles.	2 / 3
Actionability	Provides fully executable command-line examples, Python module usage with concrete parameters, and a comprehensive parameter table. The code examples are copy-paste ready with realistic values.	3 / 3
Workflow Clarity	While usage modes are clear (CLI, interactive, module), there's no validation workflow for the generated DMSP. Missing steps for reviewing output against NIH requirements or handling generation errors.	2 / 3
Progressive Disclosure	References external files (nih_dmp_template.md) appropriately, but the main document is bloated with sections that could be separate (Risk Assessment, Security Checklist, Evaluation Criteria). The FAIR principles section could be a reference rather than inline.	2 / 3
	Total	9 / 12 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: aipoch/medical-research-skills
Commit: 4a48721

Reviewed: about 10 hours ago

Table of Contents

Discovery Implementation Validation

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