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extract-from-pdfs

This skill should be used when extracting structured data from scientific PDFs for systematic reviews, meta-analyses, or database creation. Use when working with collections of research papers that need to be converted into analyzable datasets with validation metrics.

Quality

82%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

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

A well-structured, highly actionable skill body with a clearly sequenced pipeline, genuine validation feedback loops, and correct progressive disclosure to real bundle files. The weaknesses are modest: some padding that teaches concepts Claude already knows, and two small accuracy gaps (a wrong line number for the filter TODO and a missing config-template copy step).

Suggestions

Fix the 'line 74' reference for the TODO in scripts/02_filter_abstracts.py — the actual TODO prompt is around line 119 — and add a 'cp assets/api_config_template.json my_api_config.json' step before the Step 5 command.

Remove explanations of concepts Claude already knows, e.g., the precision/recall/F1 definitions in Quality Assurance and the Purpose section that restates the frontmatter description.

Trim duplication: references/setup_guide.md is pointed to twice, and the Available Scripts/Assets listings largely repeat what the pipeline commands and Customization sections already show.

DimensionReasoningScore

Conciseness

Mostly efficient and command-dense, but includes unnecessary explanation: defines precision/recall/F1 ('F1 score: Harmonic mean of precision and recall') which Claude already knows, restates the description in a Purpose section, re-references references/setup_guide.md twice, and re-lists scripts already shown in the pipeline. Not anchor 4 because there are several trimmable padded spots rather than a couple of minor ones.

3 / 5

Actionability

Copy-paste-ready bash commands with flags for all 8 pipeline steps, real template/example paths (verified present in assets/ and references/), and concrete backend/cost guidance. Not anchor 5 due to minor gaps: the 'line 74' pointer to the filter TODO is wrong (actual TODO is around line 119), and Step 5 uses my_api_config.json without instructing to copy it from assets/api_config_template.json.

4 / 5

Workflow Clarity

Clearly sequenced 6-step pipeline plus optional validation steps 7-9, with explicit feedback loops ('Revise schema, prompts, or model selection... Re-extract and re-validate... Compare metrics') and per-field metrics for error diagnosis. This satisfies the batch-operation validation requirement at full strength — not merely present, but structured as an iterative improvement cycle.

5 / 5

Progressive Disclosure

The body is an overview with well-signaled one-level-deep references (setup_guide.md, workflow_guide.md, validation_guide.md, api_reference.md — all exist and contain real content, no further nesting), examples in assets/, and scripts in scripts/. Content is appropriately split and navigation is easy.

5 / 5

Total

17

/

20

Passed

Description

78%Weight 40%Scale 1-5

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 description with explicit 'what' and 'when' clauses and good natural trigger terms for the research-audience. Its main weakness is specificity: the actions are phrased abstractly ('structured data', 'validation metrics') without naming the concrete capabilities the skill actually performs.

Suggestions

Enumerate 2-3 concrete capabilities instead of the abstract 'extracting structured data', e.g., 'Filter papers by abstract, extract schema-based data from PDFs, enrich with external databases (GBIF, PubChem), and calculate precision/recall validation metrics'.

Add common synonyms and extensions users would naturally say, such as '.pdf', 'literature review', or 'data extraction', to strengthen trigger matching.

State the concrete output formats (Python, R, CSV, SQLite) in the description so the 'what' is fully concrete.

DimensionReasoningScore

Specificity

Names the domain ('scientific PDFs', 'systematic reviews, meta-analyses') and 1-2 actions ('extracting structured data', 'converted into analyzable datasets'), but 'structured data' remains abstract and the concrete pipeline capabilities (filtering, schema design, enrichment) are not enumerated. It does not reach anchor 4 because 'systematic reviews, meta-analyses, database creation' are use cases rather than several distinct concrete actions.

3 / 5

Completeness

Explicitly answers both what ('extracting structured data from scientific PDFs... converted into analyzable datasets with validation metrics') and when ('Use when working with collections of research papers that need to be converted into analyzable datasets') with concrete trigger phrases, matching the anchor-5 exemplar structure.

5 / 5

Trigger Term Quality

Good natural-keyword coverage for the target audience: 'systematic reviews', 'meta-analyses', 'collections of research papers', 'analyzable datasets', 'database creation'. Not anchor 5 because common variations and extensions are missing (e.g., '.pdf', 'literature review', 'data extraction').

4 / 5

Distinctiveness Conflict Risk

Clear niche (systematic-review/meta-analysis database building from scientific PDFs) with distinct triggers; only minor overlap risk with generic PDF text-extraction skills. Not 5 because 'extracting structured data from scientific PDFs' could still collide with a general PDF-extraction skill.

4 / 5

Total

16

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

brunoasm/my_claude_skills

Path

extract_from_pdfs/SKILL.md

Reviewed

Table of Contents

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