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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.

66

Quality

80%

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SKILL.md
Quality
Evals
Security

Quality

Content

68%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, well-organized reference with concrete per-model examples and parameter tables, and a welcome drift warning. Its main weaknesses are the absence of an explicit end-to-end workflow (no ordering of estimate_cost → generate → result-polling and no error-recovery loop) and a verbose legal-style paragraph in Related Skills.

Suggestions

Add an explicit ordered workflow section, e.g.: 1) search/find current model metadata (per the drift warning), 2) estimate_cost for expensive video runs, 3) generate, 4) poll result for async jobs and re-check on failure — turning the scattered checkpoints into a sequenced process with feedback.

Trim the tasteforge-video paragraph to one sentence of scope (e.g., "tasteforge-video produces reference-only manifests; verify endpoint schema before executing") to cut boilerplate that doesn't change behavior.

Demonstrate async result retrieval with a short result()/status() example, since several models likely return async jobs and the tool exists only as a one-line bullet.

DimensionReasoningScore

Conciseness

The body is mostly lean tables and code examples with minimal prose padding, but the tasteforge-video paragraph ("Its endpoint candidates and request manifests are reference-only, not submitted jobs or saved Fal workflows. A TasteForge handoff does not authorize upload or generation...") is legalistic boilerplate that earns few tokens, fitting the 'minor instances of over-explanation' level of 4.

4 / 5

Actionability

Concrete generate() calls with real app_ids and values, parameter tables, an upload-then-generate flow, and an executable ElevenLabs snippet make the guidance mostly copy-paste ready, but async result retrieval is never demonstrated even though the result/status tools are listed — a minor gap placing it at 4 rather than fully-executable 5.

4 / 5

Workflow Clarity

The body is organized as a per-modality catalog rather than a sequenced workflow: the implied flow (search current metadata → estimate_cost → generate → poll result) is scattered across the drift warning, Cost Estimation, and MCP Tools sections with no explicit ordering or validation checkpoints, matching 'sequence present but checkpoints missing or implicit' at 3.

3 / 5

Progressive Disclosure

No bundle files exist and the single file is cleanly sectioned (When to Activate, MCP Requirement, per-modality sections, tables, Tips) making navigation easy; however, the ~200-line per-model catalog — content the drift warning itself flags as volatile — could be split into reference files, leaving minor organization gaps at 4 rather than a fully appropriate split.

4 / 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 description: concrete capabilities with named models, an explicit 'Use when' clause, and a clearly delimited niche. The only gap is that a few natural trigger variants (text to speech, TTS, thumbnail, music) that the body already lists are missing from the description itself.

DimensionReasoningScore

Specificity

The description enumerates four concrete capability pairs with model names — "text-to-image (Nano Banana)", "text/image-to-video (Seedance, Kling, Veo 3)", "text-to-speech (CSM-1B)", "video-to-audio (ThinkSound)" — giving comprehensive coverage of the image, video, and audio domain, matching the score-5 anchor rather than the minor-gaps level of 4.

5 / 5

Completeness

It explicitly answers both what ("Unified media generation via fal.ai MCP" with four concrete modes) and when ("Use when the user wants to generate images, videos, or audio with AI") with concrete trigger phrases, matching the score-5 anchor; the 'when' clause is explicit and specific, not the weaker version described at level 4.

5 / 5

Trigger Term Quality

"Use when the user wants to generate images, videos, or audio with AI" provides good natural-phrase coverage, but common variants the body itself lists ("text to speech", "make a thumbnail", music, sound effects) are absent from the description, fitting the 'a few natural terms missing' level of 4 rather than the comprehensive synonym coverage of 5.

4 / 5

Distinctiveness Conflict Risk

The niche is clear — fal.ai MCP media generation with named models — and the triggers are specific to that purpose, so overlap with adjacent skills (e.g., video editing) is minimal, matching the 'clear niche with distinct triggers' anchor.

5 / 5

Total

19

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
affaan-m/ECC
Reviewed

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