tdg-personal/fal-ai-media
Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.
85
85%
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 clearly communicates its capabilities, lists specific models and modalities, and includes an explicit 'Use when' trigger clause. It balances conciseness with comprehensive detail, covering the what, when, and how (via fal.ai MCP) effectively. The named models serve double duty as both specificity markers and trigger terms for users who know them.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions with named models: text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). This is highly specific about what capabilities are available. | 3 / 3 |
Completeness | Clearly answers both 'what' (unified media generation covering text-to-image, text/image-to-video, text-to-speech, video-to-audio with specific models) and 'when' (explicit 'Use when the user wants to generate images, videos, or audio with AI'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'generate images', 'videos', 'audio', 'AI', plus specific model names (Kling, Veo 3, Seedance) that power users might reference. Covers 'text-to-image', 'text-to-video', 'text-to-speech' which are common user phrasings. Also mentions 'fal.ai' as a platform trigger. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific platform (fal.ai MCP), named models, and the unified multi-modal generation scope. The combination of image, video, and audio generation via a specific service makes it unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 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 is a solid, actionable reference skill with concrete examples for each model and good parameter documentation. Its main weaknesses are the lack of async workflow guidance (generate → poll status → retrieve result), inclusion of non-MCP tools (ElevenLabs, VideoDB) that dilute focus, and the monolithic structure that could benefit from splitting into category-specific files.
Suggestions
Add an explicit async workflow section showing: generate → use `status` to poll → use `result` to retrieve output, with error handling guidance for failed generations
Remove or move ElevenLabs and VideoDB sections to their own skills — they're outside the fal.ai MCP scope and add ~30 lines of off-topic content
Split detailed model examples and parameter tables into separate files (e.g., IMAGE_MODELS.md, VIDEO_MODELS.md, AUDIO_MODELS.md) and keep SKILL.md as a concise overview with quick-start examples
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient with good use of tables and code examples, but includes some unnecessary sections (ElevenLabs and VideoDB are outside the fal.ai MCP scope and add bloat), and the MCP tools list could be trimmed since Claude can discover these. The tips section is mostly things Claude already knows. | 2 / 3 |
Actionability | Provides concrete, copy-paste-ready MCP tool calls for every model, complete parameter tables with types and options, and specific app_ids. The examples cover all major use cases (text-to-image, image-to-video, TTS, video-to-audio) with realistic prompts and parameters. | 3 / 3 |
Workflow Clarity | Individual generation calls are clear, and the image editing section shows a two-step upload-then-generate workflow. However, there are no validation checkpoints — no guidance on checking results, handling failures, or using the `result`/`status` tools for async operations. The async workflow (generate → status → result) is implied by listing the tools but never demonstrated. | 2 / 3 |
Progressive Disclosure | Content is well-organized with clear section headers and horizontal rules separating major categories. However, at ~200 lines this is a large monolithic file — the detailed parameter tables and model-specific examples for each category (image/video/audio) could be split into separate reference files. The 'Related Skills' section at the end is a good touch. | 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.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
Reviewed
Table of Contents