nature-paper-card

Build a source-grounded deep-reading Paper Card for one scientific paper, preprint, PDF, DOI, arXiv page, publisher article, or pasted paper text. Use when the user asks for a Paper Card, deep-reading literature card, single-paper deep analysis, module-by-module analysis, experiment-to-claim evidence chain, conclusion-boundary audit, critical analysis, knowledge connections, or candidate research ideas. Produce the fixed Sections 01-16 covering bibliographic position, research question, background route, pain point, core insight, method and module logic, essential formulas, experiment-to-claim evidence, conclusion boundaries, author-stated limitations, critical analysis, learned knowledge, knowledge connections, and testable research ideas. Do not use for full-paper bilingual translation, formal peer-review reports, batch literature monitoring, academic-English collection, comprehension quizzes, or public-article writing.

Quality

90%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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Securityby

Low

Low-risk findings worth noting

Quality

Content

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

The content is a well-sequenced, actionable router with strong validation feedback loops and a clear checklist. Its main weakness is the missing manifest/static bundle layer that the routing protocol critically depends on, leaving a structural gap.

Suggestions

Bundle the manifest.yaml and static/core + static/fragments/paper_type files that the routing protocol references, so the skill is self-contained and the 'Load the manifest and core layer' step can actually execute.

Consolidate the 'Script red lines' section with the inline prohibitions already stated in step 2 to remove duplicated guidance and trim token weight.

Replace command placeholders (INPUT, WORKDIR) with a concrete one-line example showing actual substituted values, to make the bundled-script invocation fully copy-paste ready.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient and procedural with no concept-explanation padding, but the 'Script red lines' section partially restates prohibitions already given in step 2, a minor instance of trimming opportunity.	4 / 5
Actionability	Provides concrete executable command templates for prepare_paper.py and audit_paper_card.py with explicit SKILL_DIR resolution, but commands use placeholders (INPUT, WORKDIR) requiring substitution rather than being fully copy-paste ready.	4 / 5
Workflow Clarity	A 6-step ordered routing protocol with explicit validation checkpoints ('Treat audit errors as blockers'), a locator state machine, a fallback loop, and a verification checklist matches the level-5 anchor with feedback loops for error recovery.	5 / 5
Progressive Disclosure	Good structure with clearly signaled one-level-deep references (three references/ files and two scripts verified present and linked at specific steps), but the manifest.yaml and static/core + static/fragments layers the router critically depends on are absent from the bundle.	4 / 5
	Total	17 / 20 Passed

Description

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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 is comprehensive and well-structured, clearly stating what the skill does and when to use it with extensive natural trigger terms. Explicit in-scope and out-of-scope boundaries sharply reduce conflict with adjacent skills.

Dimension	Reasoning	Score
Specificity	Enumerates many concrete actions (build a Paper Card covering bibliographic position, research question, core insight, evidence chain, conclusion boundaries, etc.) with comprehensive coverage of 16 sections, matching the level-5 anchor.	5 / 5
Completeness	Explicitly answers both 'what' (build a source-grounded Paper Card with Sections 01-16) and 'when' ('Use when the user asks for a Paper Card...'), with concrete trigger phrases matching the level-5 anchor.	5 / 5
Trigger Term Quality	Lists numerous natural trigger phrases users would say ('Paper Card', 'deep-reading literature card', 'module-by-module analysis', 'conclusion-boundary audit') plus input-type triggers (PDF, DOI, arXiv), matching the comprehensive level-5 anchor.	5 / 5
Distinctiveness Conflict Risk	Clear niche with distinct triggers and an explicit exclusion list ('Do not use for full-paper bilingual translation, formal peer-review reports, batch literature monitoring...') minimizing conflict with adjacent skills.	5 / 5
	Total	20 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 1 missing	Warning

	Total	15 / 16 Passed

Repository: Yuan1z0825/nature-skills
Path: skills/nature-paper-card/SKILL.md
Commit: 7e626a8

Reviewed: 5 days ago

Table of Contents

Discovery Implementation Validation

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.