runware
Generate images and videos via Runware API. Access to FLUX, Stable Diffusion, Kling AI, and other top models. Supports text-to-image, image-to-image, upscaling, text-to-video, and image-to-video. Use when generating images, creating videos from prompts or images, upscaling images, or doing AI image transformation.
94
Quality
93%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 an excellent skill description that hits all the key criteria. It provides specific capabilities, names concrete models and operations, includes natural trigger terms users would actually say, and has an explicit 'Use when...' clause. The mention of the specific API (Runware) and model names (FLUX, Stable Diffusion, Kling AI) makes it highly distinctive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'text-to-image, image-to-image, upscaling, text-to-video, and image-to-video' along with specific model names (FLUX, Stable Diffusion, Kling AI). Uses proper third person voice. | 3 / 3 |
Completeness | Clearly answers both what ('Generate images and videos via Runware API' with specific capabilities) AND when with explicit 'Use when...' clause covering multiple trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'generating images', 'creating videos', 'upscaling images', 'AI image transformation', plus model names users might reference. Covers both casual and technical terminology. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche with distinct triggers: specifically mentions Runware API, named models (FLUX, Stable Diffusion, Kling AI), and specific operations. Unlikely to conflict with other skills due to API and model specificity. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%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-crafted skill that efficiently documents the Runware API with executable examples and clear organization. The main weakness is the lack of error handling guidance and validation steps for operations that could fail (API errors, generation failures, polling timeouts). The model tables and notes section add practical value without bloat.
Suggestions
Add error handling guidance: what to do when API returns errors, rate limits, or generation fails
Include a validation step or example of checking output quality/existence after generation completes
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely lean and efficient. No unnecessary explanations of what APIs or models are. Every section provides direct, actionable information without padding. | 3 / 3 |
Actionability | Fully executable bash commands with clear options documented. Copy-paste ready examples for all major operations (text-to-image, img2img, upscale, video generation). | 3 / 3 |
Workflow Clarity | Commands are clear but lacks validation checkpoints. No guidance on verifying output quality, handling API errors, or what to do if generation fails. Video polling is mentioned but error recovery isn't addressed. | 2 / 3 |
Progressive Disclosure | Well-organized with clear sections. External links to models and pricing are appropriately placed. Content is appropriately sized for a single SKILL.md without needing additional files. | 3 / 3 |
Total | 11 / 12 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 — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- Demerzels-lab/elsamultiskillagent
- Commit
45f9fac
- Reviewed
Table of Contents
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