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muapi-cinema-director

Direct high-fidelity cinematic video with AI — translates creative intent into technical cinematographic directives for Veo3, Kling, and Luma video models via muapi.ai

55

Quality

63%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./library/motion/cinema-director/SKILL.md

The canonical home for this skill is muapi-cinema-director in SamurAIGPT/Generative-Media-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

61%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 well-structured, mostly actionable skill body with a real executable script and clear intent-to-shot mapping tables. It loses points for re-explaining cinematography basics Claude already knows and for leaving the async polling workflow without concrete validation steps.

Suggestions

Remove or condense definitions of basic cinematography terms (Dolly, Truck, Crane, Anamorphic) that Claude already knows; keep only the skill's controlled-vocabulary intent labels.

Show the actual polling command for Step 3 (e.g. the core/platform/check-result.sh invocation with a loop and a success/failure branch) to add validation checkpoints.

Trim the 'Core Competencies' section and decorative emoji/separator lines to improve token efficiency.

DimensionReasoningScore

Conciseness

Mostly efficient with compact tables and lists, but it defines camera fundamentals Claude already knows (Dolly, Truck, Crane, Anamorphic, Rack Focus) and the 'Core Competencies' section is padded with generic descriptions that could be trimmed.

3 / 5

Actionability

Provides a concrete, executable bash invocation with flags referencing a real bundle script (scripts/generate-film.sh); minor gaps in that the async polling command is named but not shown.

4 / 5

Workflow Clarity

Three steps are clearly sequenced (define brief, invoke script, handle async response), but there are no validation checkpoints and the polling/error-handling loop in Step 3 is only described, not specified.

3 / 5

Progressive Disclosure

Well-organized into clearly labeled sections with a real one-level-deep script reference; minor organization noise from emoji headers and multiple horizontal rules rather than fully lean navigation.

4 / 5

Total

14

/

20

Passed

Description

65%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 specific, niche-scoped description with strong natural trigger terms and low conflict risk, but it lacks an explicit 'Use when...' clause and lists only one core action. Adding a usage trigger and one or two more concrete capabilities would lift completeness and specificity.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when generating AI video with Veo3, Kling, or Luma, or when the user wants cinematic shot direction.'

Enumerate one or two more concrete actions (e.g. 'selects framing, camera movement, and lighting from a director's intent table') to raise specificity toward 5.

Include a natural synonym like 'text-to-video' or 'AI video generation' to broaden trigger-term coverage.

DimensionReasoningScore

Specificity

Names the domain ('high-fidelity cinematic video') and one concrete action ('translates creative intent into technical cinematographic directives') plus specific model targets, but does not enumerate several distinct actions as the score-5 anchor requires.

3 / 5

Completeness

Provides a clear 'what' (directing/translating intent into cinematographic directives) but has no 'Use when...' clause or equivalent explicit trigger guidance, which the rubric caps at 3.

3 / 5

Trigger Term Quality

Includes natural model-name keywords users would say ('Veo3, Kling, and Luma video models'), 'cinematic video', and platform 'muapi.ai'; a few natural synonyms like 'AI video generation' or 'text-to-video' are absent.

4 / 5

Distinctiveness Conflict Risk

Targets a narrow niche (AI cinematic video directing for specific named models via a specific platform), yielding distinct triggers and minimal overlap with other skills.

5 / 5

Total

15

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
SamurAIGPT/Generative-Media-Skills
Reviewed

Table of Contents

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