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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.

68

Quality

82%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

A highly actionable, concise reference with strong copy-paste examples, but it reads as a flat catalog rather than an overview pointing to deeper materials, and lacks explicit validation checkpoints in its generation workflow.

Suggestions

Add a brief numbered workflow (estimate_cost → generate → check result/status) with an explicit verification step so generated output is confirmed before use.

Move detailed per-model examples and parameter tables into a separate reference file (e.g. references/models.md) and surface them as one-level-deep links, keeping SKILL.md as a concise overview.

Provide a concrete end-to-end example that resolves the <uploaded_url>/<video_url> placeholders by chaining upload() output into generate(), so the image-to-video and editing flows are fully runnable.

DimensionReasoningScore

Conciseness

The body is lean, assumes Claude's competence, and uses code plus compact parameter tables rather than explaining concepts, with only minor repetition of generate() boilerplate across model sections.

4 / 5

Actionability

Each modality has copy-paste-ready code with concrete model_name strings and input parameters, supported by parameter tables and a documented upload step, covering the common generation cases.

5 / 5

Workflow Clarity

Usage is presented as a reference catalog with an implied estimate-cost-then-generate sequence and an upload-then-generate example, but there are no explicit validation checkpoints or feedback loops for verifying generation results.

3 / 5

Progressive Disclosure

Sections are well-organized with clear headers, but with no bundle files present the ~270-line body keeps all per-model examples and parameter tables inline rather than splitting detail into one-level-deep reference files.

3 / 5

Total

15

/

20

Passed

Description

92%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.

A strong, well-scoped description that names concrete capabilities and explicit trigger conditions in third person. It could be marginally improved by adding common synonyms and file extensions for broader trigger coverage.

DimensionReasoningScore

Specificity

Lists multiple concrete actions across modalities — "image, video, and audio" with text-to-image, text/image-to-video, text-to-speech, and video-to-audio — alongside named models, giving comprehensive coverage.

5 / 5

Completeness

Explicitly states what it does (unified media generation via fal.ai MCP covering named modalities) and when to use it ("Use when the user wants to generate images, videos, or audio with AI") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Natural terms like "generate images, videos, or audio", "text-to-image", and "text-to-speech" are present, but synonyms (e.g. "make a thumbnail") and file extensions are missing, so it falls short of comprehensive.

4 / 5

Distinctiveness Conflict Risk

Scoped to fal.ai MCP media generation with distinct, modality-specific triggers, giving it a clear niche with minimal overlap risk against unrelated skills.

5 / 5

Total

19

/

20

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
affaan-m/ECC
Reviewed

Table of Contents

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