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media-production

Generate and edit images, vector artwork, photos, audio and video; inspect results and deliver finished artifacts.

Quality

74%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/agent/media-production/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

73%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.

The content is a densely written but well-sequenced workflow with strong, explicit validation checkpoints and error-recovery guidance — its main structural strength. The weaknesses are sentence-level density that hurts token efficiency and a lack of section headings to help navigation across the distinct tool clusters.

Suggestions

Split multi-clause sentences into shorter bullets (e.g. break step 6 into 'Checking', 'Evidence reuse', and 'Rendering' sub-bullets) to reduce density and improve token efficiency.

Add section headings such as 'Hosted generation', 'Local production', 'Motion graphics', 'Verification and delivery' to signpost the distinct tool clusters.

Include one concrete invocation example per major tool (e.g. a representative 'hyperframes.create' or 'media_production.image.generate' call with its parameter shape) to close the remaining actionability gaps.

DimensionReasoningScore

Conciseness

The body avoids explaining known concepts, but each step is a single dense run-on sentence stacking 5-8 policy clauses (e.g. step 6 packs lint/targeted/full check modes, evidence-reuse rules, and render policy into one paragraph), so it is mostly efficient but could be meaningfully tightened.

3 / 5

Actionability

It names concrete tools and parameters ('media_production.image.generate', 'hyperframes.check' with 'options.mode=lint', 'scope=full', 'complete=true', 'checks.ok=true', 'file_system.file.attach'), giving mostly executable guidance; per the rubric's instruction-only note the absence of code is not penalized, though a few steps stop at naming tools without invocation examples.

4 / 5

Workflow Clarity

The 9-step sequence is clearly ordered with explicit validation checkpoints and feedback loops: validate one representative asset before batch treatment, lint cheaply then targeted then full checks with concrete success criteria, repeat only checks whose source changed, and an explicit error-recovery path ('If playback review remains unavailable, deliver the available outputs and identify the exact unverified property').

5 / 5

Progressive Disclosure

No external references are needed for a skill this compact (no references/, scripts/, or assets/ exist), and the numbered list gives a flat but coherent organization; however, there are no section headings to signpost the distinct clusters (hosted generation, local production, motion graphics, verification), leaving minor organization gaps.

4 / 5

Total

16

/

20

Passed

Description

62%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.

The description is concise and covers the full media domain with reasonably specific actions and natural keywords, but it lacks any explicit 'when to use' trigger clause and is broad enough to risk overlap with more specialized media skills. Adding a 'Use when...' clause with concrete trigger phrases would lift both completeness and distinctiveness.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks to create, edit, or convert images, photos, audio, or video, or mentions specific media files or extensions.'

Include common synonyms and file extensions users actually say (music, logos, clips, .png, .mp4, .svg) to strengthen trigger term coverage.

Sharpen the action verbs toward concrete operations (e.g. 'remove backgrounds, transcribe or generate speech, convert formats, compose animations') to raise specificity from generic generate/edit/deliver.

DimensionReasoningScore

Specificity

The description names the media domain and several concrete actions ('Generate and edit images, vector artwork, photos, audio and video; inspect results and deliver finished artifacts'), but the verbs stay generic without finer-grained operations, matching 'several specific actions; minor gaps in coverage' rather than comprehensive coverage.

4 / 5

Completeness

It has a clear 'what' but no 'when' — there is no 'Use when...' clause or equivalent explicit trigger guidance, which the judging guidelines cap at 3.

3 / 5

Trigger Term Quality

It includes natural terms users would say ('images', 'photos', 'audio', 'video', 'vector artwork'), but misses common variations and file-extension triggers like 'music', 'logo', 'clip', or 'mp4', so keyword coverage is good but not comprehensive.

4 / 5

Distinctiveness Conflict Risk

The description spans images, photos, audio and video in one broad media scope, so it could still overlap with dedicated image-generation, speech/audio, or video skills; it is somewhat specific but not a clear niche.

3 / 5

Total

14

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

leon-ai/leon

Path

skills/agent/media-production/SKILL.md

Reviewed

Table of Contents

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