muapi-ai-clipping

Turn a long video into N viral-ready short clips with a single managed API call. Wraps muapi.ai's `/ai-clipping` endpoint, which handles transcription, highlight ranking through a virality framework (hook / emotional peak / opinion bomb / revelation / conflict / quotable / story peak / practical value), overlap dedupe, and vertical face-tracking auto-crop server-side. No local Whisper, no local LLM, no GPU.

Quality

72%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./.opencode/skills/muapi-ai-clipping/SKILL.md

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with a clear, validated workflow, but it is a long monolithic file that could offload reference material to bundle files and trim API-internal explanations.

Suggestions

Trim the 'What Happens Server-Side' virality-framework taxonomy to a one-line summary, since the API handles it and the agent does not act on those internals.

Split the full output schema and server-side pipeline detail into a reference file under references/ and link to it, so SKILL.md stays an overview.

Add the missing scripts/run-ai-clipping.sh bundle file (or fix the inconsistent path library/edit/ai-clipping/scripts/...) so referenced paths resolve.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with actionable tables and commands, but the eight-item 'What Happens Server-Side' virality taxonomy is explanatory detail the API handles internally and could be tightened; not a 3 because not every token earns its place.	2 / 3
Actionability	Provides copy-paste-ready executable bash commands, a concrete input table, named CLI flags, and a full output schema — matching 'fully executable code/commands; copy-paste ready'.	3 / 3
Workflow Clarity	Sequences Collect Inputs → Verify Prerequisites → Run, with prerequisite validation in Step 2, error-recovery guidance in 'Failure Modes', and a 'Done Criteria' checklist providing explicit checkpoints and feedback loops.	3 / 3
Progressive Disclosure	Well-sectioned but monolithic: the skill is ~185 lines (above the 50-line simple-skill exception), no bundle/reference files exist, and the referenced run-ai-clipping.sh script does not resolve to a real file, with reference-grade content (server-side taxonomy, full output schema) kept inline.	2 / 3
	Total	10 / 12 Passed

Description

67%

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 specific and occupies a clear niche, but it lacks an explicit 'when to use' trigger clause and leans on API/ML jargon over the natural terms users would say.

Suggestions

Add a 'Use when...' clause with natural trigger terms (e.g., auto-clip a podcast/interview/vlog into TikTok, Reels, or YouTube Shorts) so it answers 'when' explicitly.

Replace jargon like 'virality framework' and '/ai-clipping endpoint' with user-facing terms (shorts, reels, highlight clips) to improve trigger-term coverage.

Keep the concrete action list but tighten the parenthetical taxonomy so the description stays concise.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'transcription, highlight ranking...', 'overlap dedupe', and 'vertical face-tracking auto-crop' — matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Clearly answers 'what' but there is no 'Use when...' clause or equivalent trigger guidance, so per the rubric 'when' is only implied and completeness is capped at 2.	2 / 3
Trigger Term Quality	Has some natural terms ('long video', 'short clips', 'viral') but is missing common variations a user would actually say (TikTok, Reels, Shorts, podcast clips), leaning on jargon like 'virality framework' and '/ai-clipping endpoint'.	2 / 3
Distinctiveness Conflict Risk	Occupies a clear, narrow niche (muapi.ai video-to-viral-short-clips) that is unlikely to conflict with or trigger other skills.	3 / 3
	Total	10 / 12 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: SamurAIGPT/Generative-Media-Skills
Path: .opencode/skills/muapi-ai-clipping/SKILL.md
Commit: 71242e2

Reviewed: about 21 hours ago

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