SKILLEMALL.ai
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AC mv-pipeline

End-to-end automated Music Video pipeline. Covers songwriting (lyrics/composition), Suno music generation (browser automation), lyrics alignment (stable-ts), video generation (Veo 3.1 via Vertex AI or Google Flow via browser), Remotion-based editing (subtitles, effects, telops), and YouTube upload. Use when creating a full MV from scratch, or running any individual stage of the pipeline.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 1 645 tokens Open the sourcegithub.com↗ analyzed 2 d ago

End-to-end automated Music Video pipeline.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureYouTubeMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 24 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1645 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +1No license
    • +2Single-language instructions
    • +3Description length 390: enough signal without eating the budget
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 24 items
    • +4Has examples (15 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 6 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.