pdf-extraction-fallbacks-7d54a9

Multi-fallback PDF extraction with sequential approaches and early failure detection

Quality

61%

Does it follow best practices?

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Securityby

Low

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Quality

Content

70%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.

The content is highly actionable with a well-sequenced, validation-rich workflow, but it is verbose due to duplicated code across the step-by-step and complete-implementation sections, and it inlines everything rather than using separate reference files. Consolidating the redundant code blocks and moving the full implementation to a script would improve both conciseness and progressive disclosure.

Suggestions

Remove the duplication between Step 2's per-method snippets and the 'Complete Python Implementation' section — keep the full implementation once and reference it from the steps.

Move the complete Python implementation and/or integration example into a script under scripts/ (or a reference file) and link to it, reducing the inline bulk and improving progressive disclosure.

Trim concept explanations Claude already knows (e.g. 'PDF processing can hang on corrupted files', 'Some sites require headless browser rendering first') or fold them tersely into the failure-mode table.

Dimension	Reasoning	Score
Conciseness	The body is noticeably verbose: the three extraction methods are shown once as snippets in Step 2 and then repeated as full functions in the 'Complete Python Implementation', and several lines explain concepts Claude already knows (e.g. PDFs hanging on corrupted files).	2 / 5
Actionability	It provides fully executable, copy-paste-ready bash and Python code with concrete thresholds (1KB, 100 chars, 20 words) and specific commands covering the common download-and-extract cases.	5 / 5
Workflow Clarity	A clear three-step sequence (Download with validation -> Sequential fallbacks -> Content sanity check) is paired with explicit validation checkpoints, fallback feedback loops, and a failure-detection checklist.	5 / 5
Progressive Disclosure	No bundle files exist, so all ~250 lines live in one file with reasonable section headers, but bulk content (the full Python implementation, checklists) that could be split into reference files is inlined rather than separated.	3 / 5
	Total	15 / 20 Passed

Description

53%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.

The description conveys a clear, moderately specific purpose but omits any explicit 'when to use' trigger guidance and offers only thin natural-language keywords. Adding a 'Use when...' clause with common user phrases would lift completeness and trigger-term quality.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when downloading PDFs from government/agency sites, or when extraction may fail due to JavaScript, CORS, or encoding issues.'

Expand trigger terms to include natural phrasings users say: 'PDF files', 'PDFs', '.pdf', 'extract text from PDF', 'download documents'.

Reframe the architectural terms ('sequential approaches', 'early failure detection') into concrete actions ('extracts text from PDFs through layered fallbacks, detecting failed downloads early').

Dimension	Reasoning	Score
Specificity	Names the domain ('PDF extraction') and 1-2 notions ('multi-fallback', 'sequential approaches', 'early failure detection'), but these are architectural descriptors rather than concrete user-facing actions like extracting text or filling forms.	3 / 5
Completeness	It states a clear 'what' (multi-fallback PDF extraction) but has no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines.	3 / 5
Trigger Term Quality	The only natural keyword is 'PDF extraction'; common variations users say ('PDF files', 'PDFs', '.pdf', 'documents') are absent, matching the anchor for some relevant keywords but missing common synonyms.	3 / 5
Distinctiveness Conflict Risk	The multi-fallback / early-failure-detection framing carves a fairly specific niche with only minor overlap risk against generic PDF skills, though it lacks explicit trigger phrases that would push it to 5.	4 / 5
	Total	13 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: HKUDS/OpenSpace
Path: benchmarks/gdpval/skills/pdf-extraction-fallbacks-7d54a9/SKILL.md
Commit: 3827781

Reviewed: 10 days ago

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