nature-data

Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.

Quality

77%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/nature-data/SKILL.md

Quality

Content

62%

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

A well-designed router with a clear sequenced protocol and an explicit FAIR-audit validation checkpoint, but its core-layer load instructions point to files missing from the bundle and it carries some justificational padding. As shipped in this bundle, the primary actionable path is incomplete.

Suggestions

Ship the referenced manifest.yaml and static/core/*.md files (or repoint the 'always_load' instructions to files that exist) so the router's step 1 actually resolves.

Cut the 'Why this split' justification section (especially the sibling-skill name list) to keep the router purely operational and improve token efficiency.

Inline a minimal fallback of the eight-step workflow or the default stance in the router so the skill remains actionable even when core fragments cannot be loaded.

Dimension	Reasoning	Score
Conciseness	The router is mostly lean and operational, but the 'Why this split' section ('This structure mirrors nature-writing, nature-polishing...') and redundant reload instructions are justificational padding, so not every token earns its place.	2 / 3
Actionability	Concrete routing steps, seven named access routes, and a guardrail list ('Do not invent DOIs, accession numbers, repository names...') give specific guidance, but the executable substance is deferred to manifest.yaml and static/core files that are absent from the actual bundle, leaving the guidance incomplete as-is.	2 / 3
Workflow Clarity	A clearly sequenced four-step routing protocol with a summarized eight-step workflow and an explicit validation checkpoint ('run the FAIR/metadata audit') plus quality guardrails, matching 'clear sequence with explicit validation steps'.	3 / 3
Progressive Disclosure	The six references/ files are real and well-signaled with per-file purposes at one level deep, but the primary 'always_load' paths (manifest.yaml, static/core/stance.md, chinese-mode.md, workflow.md) do not exist in the bundle, so navigation from step 1 is broken and structure is only partially reliable.	2 / 3
	Total	9 / 12 Passed

Description

92%

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

A strong, third-person description with explicit what/when triggers and excellent natural-term coverage, including Chinese phrasings. Its only weakness is intentional scope-broadening to non-Nature and general academic-writing needs, which raises conflict risk with sibling writing/citation skills.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Prepare, audit, or revise') applied to specific objects ('Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists'), matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both what ('Prepare, audit, or revise Nature-ready Data Availability statements...') and when ('Use when the user asks about... Also trigger on...'), satisfying the 'clearly answers both what AND when' anchor.	3 / 3
Trigger Term Quality	Dense coverage of natural user terms ('Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets') plus Chinese phrasings users would actually say.	3 / 3
Distinctiveness Conflict Risk	The core niche (Nature data availability) is distinct, but the broadened triggers ('general academic-writing data needs even without the word Nature', 'for any journal', 'dataset citations') increase overlap with generic academic-writing and nature-citation skills, fitting 'somewhat specific but could still overlap'.	2 / 3
	Total	11 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 1 missing	Warning

	Total	14 / 16 Passed

Repository: Yuan1z0825/nature-skills
Path: skills/nature-data/SKILL.md
Commit: 703c150

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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