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sci-data-extractor

Extract structured data from scientific literature PDFs using AI-powered OCR and LLM analysis. Supports enzyme kinetics, experimental results, and literature review templates. Use when researchers need to parse tables, charts, or text from paper PDFs into Markdown or CSV. Triggers: extract data from PDF, scientific data extraction, parse paper tables, enzyme kinetics extraction, batch PDF extraction, literature data, research data from paper, OCR scientific paper, convert PDF to structured data.

Quality

89%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

82%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: executable commands for all common cases, explicit anti-patterns with validation and recovery guidance, and proper deferral of setup detail to a reference file. The main issues are a dead reference to a nonexistent requirements.txt, validation checkpoints separated from the workflow steps rather than integrated into them, and overlap between the Mindset and Anti-Patterns sections.

Suggestions

Fix the broken reference: 'Dependencies: [requirements.txt](requirements.txt)' points to a file that does not exist in the bundle — either add requirements.txt or remove the link and fold dependency listing into references/setup-and-troubleshooting.md.

Integrate validation checkpoints into the numbered Workflow steps (e.g., after step 3 add 'spot-check rows with --print before saving'; after step 7 add 'read the batch summary and re-run failed files'), so the checks are encountered in sequence rather than only in the Anti-Patterns section.

Deduplicate the Mindset and Anti-Patterns sections — scanned-PDF handling, batch non-atomicity, and output validation are each covered twice; keep one section for principles and point to the Anti-Patterns for the BAD/GOOD pairs.

DimensionReasoningScore

Conciseness

The body is efficient: copy-paste commands with inline comments, no explanations of concepts Claude already knows, and details deferred to a reference file. It fits the 4 anchor ('Efficient; minor instances of over-explanation that could be trimmed') — the Mindset section substantially restates the Anti-Patterns (scanned-PDF handling, batch non-atomicity, output validation each appear twice), which is more than the lean 5 anchor's 'every token earns its place' but well short of the 3 anchor's 'unnecessary explanation'.

4 / 5

Actionability

Every workflow step is a fully executable command with real flags ('./scripts/extractor.py paper.pdf --template enzyme -o results.md', '--format csv', '--ocr mathpix', './scripts/batch_extract.py ./literature/ ./output/ --template enzyme --format csv'), and the GOOD custom-prompt example is copy-paste ready with exact column format. This matches the 5 anchor ('Fully executable; copy-paste ready code or commands; specific examples cover the common cases') — preset templates, custom prompts, CSV, Mathpix OCR, batch, and terminal printing are all covered.

5 / 5

Workflow Clarity

The eight numbered steps form a clear install→configure→run sequence, and validation with feedback loops is explicitly present (spot-check 3 rows with --print, re-run failed batch files individually, switch to --ocr mathpix on empty extraction, check Mathpix credentials before invoking), so the batch-operation cap of 3 does not apply. It sits at the 4 anchor rather than 5 because those checkpoints live in the separate Anti-Patterns section instead of being sequenced into the workflow steps — a reader following only the Workflow section would miss the validation checkpoints, which is the 'minor validation gaps' of anchor 4.

4 / 5

Progressive Disclosure

The SKILL.md is a lean overview with well-signaled, one-level-deep references ('See [setup-and-troubleshooting.md](references/setup-and-troubleshooting.md)', a References section listing Setup/Script/Upstream) and scripts appropriately separated, matching the 4 anchor ('Good structure; most content is appropriately placed; references mostly clear; minor organization gaps'). It falls short of 5 because the References section lists 'Dependencies: [requirements.txt](requirements.txt)' which does not exist in the bundle — a broken navigation pointer.

4 / 5

Total

17

/

20

Passed

Description

96%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 that explicitly states what the skill does, when to use it, and enumerates natural trigger phrases with synonyms. The only weaknesses are a handful of generic PDF triggers that could overlap with general PDF-extraction skills and the absence of the '.pdf' extension as a trigger term.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions with comprehensive coverage: 'Extract structured data from scientific literature PDFs using AI-powered OCR and LLM analysis', 'parse tables, charts, or text from paper PDFs into Markdown or CSV', and names the supported templates ('enzyme kinetics, experimental results, and literature review templates'). It matches the anchor 'Lists multiple specific concrete actions; comprehensive coverage' rather than 4, whose 'minor gaps in coverage' does not apply since extraction targets, output formats, and templates are all enumerated.

5 / 5

Completeness

Both 'what' and 'when' are explicit and concrete: what — 'Extract structured data from scientific literature PDFs using AI-powered OCR and LLM analysis. Supports enzyme kinetics, experimental results, and literature review templates'; when — 'Use when researchers need to parse tables, charts, or text from paper PDFs into Markdown or CSV' followed by a concrete trigger-phrase list. This matches the 5 anchor ('Clearly and explicitly answers both what AND when with concrete trigger phrases') and exceeds the 4 anchor where 'when' is less specific.

5 / 5

Trigger Term Quality

The explicit 'Triggers:' list gives nine natural phrases users would say ('extract data from PDF', 'parse paper tables', 'enzyme kinetics extraction', 'batch PDF extraction', 'convert PDF to structured data', 'OCR scientific paper') plus 'PDFs' as a synonym, matching the anchor for comprehensive coverage including synonyms. It is well above the 4 anchor ('good keyword coverage; a few natural terms missing') — the only omission is the '.pdf' file extension, which is trivial given 'PDF'/'PDFs' appear repeatedly.

5 / 5

Distinctiveness Conflict Risk

The scientific-literature niche is clear and triggers like 'enzyme kinetics extraction' and 'OCR scientific paper' are distinct, but generic triggers such as 'extract data from PDF' and 'convert PDF to structured data' would also fire for a general PDF-extraction skill, giving minor overlap risk with closely related skills. It therefore fits the 4 anchor ('Mostly distinct; minor overlap risk') rather than the 5 anchor's 'minimal conflict risk'.

4 / 5

Total

19

/

20

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.

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

metadata_version

'metadata.version' is missing

Warning

relative_links

Relative link issues: 1 missing

Warning

Total

13

/

16

Passed

Repository

pantheon-org/tekhne

Path

skills/documentation/research/sci-data-extractor/SKILL.md

Reviewed

Table of Contents

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