gemini-document-processing

Guide for implementing Google Gemini API document processing - analyze PDFs with native vision to extract text, images, diagrams, charts, and tables. Use when processing documents, extracting structured data, summarizing PDFs, answering questions about document content, or converting documents to structured formats. (project)

Quality

82%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

72%

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

This is a well-structured skill with excellent actionability and progressive disclosure. The code examples are executable and comprehensive, covering CLI, SDK, and structured output patterns. Main weaknesses are some verbosity in introductory sections and missing validation/error handling workflows for API operations.

Suggestions

Remove or condense the 'When to Use This Skill' and 'Core Capabilities' sections - Claude can infer these from the description and examples

Add validation steps after API calls (e.g., check response.text is not empty, handle API errors with try/except patterns)

Include a brief error recovery pattern for common failures like rate limits or malformed PDFs

Dimension	Reasoning	Score
Conciseness	The skill includes some unnecessary sections like 'When to Use This Skill' that Claude can infer, and the 'Core Capabilities' section restates what the description already covers. However, the code examples are lean and the decision guide is efficient.	2 / 3
Actionability	Provides fully executable code examples with proper imports, complete Python SDK usage, CLI commands with flags, and Pydantic schema examples. All code is copy-paste ready with real library calls.	3 / 3
Workflow Clarity	The setup steps are clear and the decision guide provides good branching logic, but there are no validation checkpoints or error recovery steps. Missing feedback loops for API failures or invalid PDF handling.	2 / 3
Progressive Disclosure	Excellent structure with quick setup, common use cases, and clear references to detailed documentation (references/gemini-document-processing-report.md, quick-reference.md, code-examples.md). One level deep, well-signaled navigation.	3 / 3
	Total	10 / 12 Passed

Description

92%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong skill description that clearly articulates specific capabilities and includes explicit trigger guidance. The description effectively uses third person voice and covers multiple concrete actions. The main weakness is potential overlap with other document processing skills, though the Gemini API specificity provides some differentiation.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'analyze PDFs with native vision to extract text, images, diagrams, charts, and tables' - covers distinct extraction capabilities comprehensively.	3 / 3
Completeness	Clearly answers both what ('analyze PDFs with native vision to extract text, images, diagrams, charts, and tables') AND when ('Use when processing documents, extracting structured data, summarizing PDFs...').	3 / 3
Trigger Term Quality	Includes natural keywords users would say: 'processing documents', 'extracting structured data', 'summarizing PDFs', 'answering questions about document content', 'converting documents to structured formats', 'PDFs'.	3 / 3
Distinctiveness Conflict Risk	While it specifies Google Gemini API and native vision, terms like 'processing documents' and 'extracting structured data' could overlap with other PDF or document processing skills. The Gemini-specific aspect helps but generic document triggers reduce distinctiveness.	2 / 3
	Total	11 / 12 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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: einverne/dotfiles
Commit: b1b2fe0

Reviewed: 5 months ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

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.