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Structure ACE-Step prompts for better song generation

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AI-generated · Automatically published by LinkLoot. A practical guide to writing captions, lyrics, and metadata that help ACE-Step produce coherent music. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it does The acestep-songwriting skill provides a structured approach to creating music with ACE-Step. It focuses on three key inputs: the caption (style/genre/emotion), the lyrics (with structural tags like [Verse] or [Chorus]), and specific parameters (BPM, key, duration). The guide emphasizes that specificity in captions yields better results than vague descriptions, and it offers rules for avoiding conflicting musical elements. Who should use it This resource is designed for users who want to generate songs using ACE-Step but struggle with prompt engineering. It’s particularly useful for those who need to balance creative control with model flexibility, ensuring that the generated audio aligns with their vision for tempo, mood, and instrumentation. Setup surface The skill operates as a knowledge base within the OpenClaw environment. It requires no external dependencies beyond the ACE-Step tool itself. Users interact with it by referencing its guidelines when constructing prompts for the acestep command-line interface, specifically utilizing flags like -c for captions and -l for lyrics. Runner test plan As this is a documentation-based skill, testing involves verifying that the provided examples and tag structures are correctly interpreted by the ACE-Step engine. Future checks would include static analysis of prompt templates against known successful outputs, dependency validation for the ACE-Step binary, and injection tests to ensure malformed tags do not crash the generator. No execution tests have been performed yet; these are proposed steps. Risk notes Users should be aware that while the guide recommends letting the language model auto-infer most metadata, manual overrides for BPM or key can lead to instability if outside recommended ranges (e.g., BPM 30–300). Additionally, overly complex lyric tags may conflict with caption instructions, potentially degrading output quality. The skill relies entirely on the underlying ACE-Step model's capabilities, which may vary in consistency. Source links Original skill documentation Skill listing ClawHub registry

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Tune ElevenLabs Music Models from Your Own Audio

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#elevenlabs #music-generation+5

AI-generated · Automatically published by LinkLoot. ElevenLabs added Music Finetunes API endpoints for creating, listing, checking, updating, and deleting custom music finetunes. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. ElevenLabs now exposes Music Finetunes through its API, which makes this useful for teams building repeatable music-generation workflows rather than one-off prompts. The July 20 changelog says the new API can create a finetune from uploaded audio, list accessible finetunes, retrieve training status, update metadata and visibility, and delete a finetune. Music generation SDK methods now expose finetuneid, so an app can route future compositions through a specific trained style or catalog. Use it when you need controlled brand music, recurring creator formats, or internal audio experiments where the same reference material should drive multiple generations. Check rights on the uploaded audio first, keep training sets documented, and treat visibility settings as part of the release checklist. How to evaluate it: Read the July 20 changelog to confirm the endpoints and SDK support. Check the Create Music Finetune reference for required fields such as name and primarygenre. Start with a small, rights-cleared audio set and verify training status before using finetuneid in production generation calls.

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Structure ACE-Step prompts for better song generation

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