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nature-literature-pipeline

Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.

56

Quality

64%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/nature-literature-pipeline/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

70%

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

The body is well-structured with a clear sequenced workflow and excellent progressive disclosure into real reference files. Its weakness is actionability: it stays at the orchestration level and delegates concrete commands and code to the reference files rather than including any inline.

Suggestions

Add one concrete inline example per stage — e.g. the actual cron schedule syntax for the daily trigger, or a sample scoring invocation — so the body is actionable without always reaching for a reference file.

Merge the redundant Architecture and References tables (both enumerate the same six files) into a single table showing layer, file, and purpose to remove duplication.

Include a short worked example of a daily push output (a sample digest message) so the delivery format is concrete in the overview.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — no padding with basic concepts — but the architecture table and the References table enumerate the same six files, a redundancy that could be tightened.

2 / 3

Actionability

Quick Start gives concrete user prompts, but the body is orchestration/overview only with no executable code or concrete commands (no cron syntax, no scoring invocation), delegating all specifics to reference files.

2 / 3

Workflow Clarity

The five-stage SEARCH→FILTER→READ→DELIVER→ARCHIVE flow is clearly sequenced with explicit validation checkpoints (score validation, triple de-dup, graceful degradation) and a pitfalls section covering error modes.

3 / 3

Progressive Disclosure

A clear overview points to six well-signaled one-level-deep reference files (verified to exist), organized into Engine/Application layers with purpose tables — easy to navigate with no nested references.

3 / 3

Total

10

/

12

Passed

Description

60%

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 specific about the pipeline's concrete stages but lacks an explicit "Use when" trigger clause and natural user keywords, leaving both completeness and trigger quality at 2. Distinctiveness is moderate given the named overlapping sibling skills.

Suggestions

Add an explicit 'Use when...' clause naming natural user phrasings, e.g. 'Use when the user wants a daily automated literature digest, recurring paper recommendations, or to monitor a research area.'

Surface concrete trigger terms users actually say — 'find papers', 'daily paper digest', 'arXiv monitoring', 'literature review' — instead of leaning on platform names like Feishu/Telegram.

Sharpen distinctiveness versus the named sibling skills (nature-academic-search, arxiv) by stating what only this skill does — scheduled multi-source scoring + delivery + archival — rather than co-listing them.

DimensionReasoningScore

Specificity

Lists multiple concrete pipeline actions — "multi-source search", "six-dimension scoring", "fine reading", "formatted delivery", "archival" — matching the score-3 anchor of several specific concrete actions.

3 / 3

Completeness

Clearly answers what the skill does (the staged pipeline) but provides no "Use when…" trigger clause, so the when is only implied, capping completeness at 2 per the rubric guideline.

2 / 3

Trigger Term Quality

Contains relevant terms ("literature", "search", "scoring", "delivery") but omits natural user phrasings like "find papers", "daily paper digest", "arXiv", or "literature review", and leans on platform names rather than common variations.

2 / 3

Distinctiveness Conflict Risk

The daily automated literature pipeline is a fairly distinct niche, but it names sibling skills (nature-academic-search, arxiv) it overlaps with, so it could still trigger for the wrong skill.

2 / 3

Total

9

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

Repository
Yuan1z0825/nature-skills
Reviewed

Table of Contents

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