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pollinations-ai

Generate and save images using Pollinations.ai's free, open-source API. No signup required. Supports URL-based generation, custom parameters (width, height, model, seed), and automatic file saving. Perfect for quick prototypes, marketing assets, and creative workflows.

Install with Tessl CLI

npx tessl i github:supercent-io/skills-template --skill pollinations-ai
What are skills?

68

1.68x

Quality

53%

Does it follow best practices?

Impact

96%

1.68x

Average score across 3 eval scenarios

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agent-skills/pollinations-ai/SKILL.md
SKILL.md
Review
Evals

Discovery

42%

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 excels at listing specific technical capabilities and parameters but fails to provide explicit trigger guidance for when Claude should select this skill. It would benefit from a clear 'Use when...' clause and more natural user-facing keywords rather than technical API details.

Suggestions

Add a 'Use when...' clause specifying triggers like 'when user asks to generate images', 'create pictures', 'AI artwork', or 'needs free image generation'

Include more natural user terms: 'create image', 'make a picture', 'AI-generated art', 'text-to-image'

Clarify distinctiveness by specifying when to use this over other image generation tools (e.g., 'Use for free, no-auth image generation' vs paid alternatives)

DimensionReasoningScore

Specificity

Lists multiple concrete actions: 'Generate and save images', 'URL-based generation', 'custom parameters (width, height, model, seed)', and 'automatic file saving'. These are specific, actionable capabilities.

3 / 3

Completeness

Clearly describes WHAT it does but completely lacks a 'Use when...' clause or any explicit trigger guidance. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2, and this has no 'when' component at all.

1 / 3

Trigger Term Quality

Includes some relevant terms like 'images', 'Pollinations.ai', 'prototypes', 'marketing assets', but misses common user phrases like 'create image', 'AI image', 'generate picture', or 'image generation'. The API name may not be what users naturally say.

2 / 3

Distinctiveness Conflict Risk

Mentions the specific API (Pollinations.ai) which helps distinguish it, but 'generate images' is generic enough to potentially conflict with other image generation skills (DALL-E, Midjourney, etc.). The use cases listed are broad.

2 / 3

Total

8

/

12

Passed

Implementation

64%

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

This skill provides highly actionable, executable code for Pollinations.ai image generation with good examples covering multiple use cases. However, it's overly verbose with sections like multi-agent workflow and extensive metadata that inflate token usage without proportional benefit. The workflow lacks explicit validation steps for batch operations, which is important for catching generation failures early.

Suggestions

Add validation checkpoints to the batch generation workflow (e.g., verify file exists and has non-zero size after each generation before proceeding)

Move the multi-agent workflow section and detailed metadata to separate reference files to reduce main skill length by ~30%

Remove or condense the 'When to use this skill' section - Claude can infer appropriate use cases from the instructions

Integrate retry logic into the main generate_image function rather than only showing it in troubleshooting

DimensionReasoningScore

Conciseness

The skill contains useful information but is verbose in places - the 'When to use this skill' section, multi-agent workflow section, and metadata section add significant length without proportional value. The core instructions are reasonably efficient but could be tightened.

2 / 3

Actionability

Excellent executable code examples throughout - the Python function is complete and copy-paste ready, curl commands work directly, and batch generation examples are fully functional with clear parameter usage.

3 / 3

Workflow Clarity

Steps are clearly numbered and sequenced, but validation checkpoints are missing. For batch operations, there's no verification that images were generated correctly before proceeding, and the retry logic is only shown in troubleshooting rather than integrated into the main workflow.

2 / 3

Progressive Disclosure

Content is reasonably structured with clear sections, but the document is monolithic at ~300 lines. The multi-agent workflow and metadata sections could be separate files. References to related skills exist but the main content could benefit from splitting advanced topics.

2 / 3

Total

9

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

10

/

11

Passed

Reviewed

Table of Contents

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.