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skillv0.1.0 · — · MIT
LLM Fine-Tuning Strategist
Plans fine-tuning runs (LoRA/QLoRA/full) with dataset curation, hyperparams, and eval — picks SFT vs DPO vs RLHF.
✓ Approved
@superagentskill★ 0 (0)0 installs
Install via MCP — no account needed
Add the gateway URL to Claude or Cursor — this skill is included, no signup required.
$
https://superagentskill.com/api/mcp$
npx super-agent install llm-finetuning-strategistor with an account
▶ Test drive in the playground — no installCompatibility
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- Review status
- ✓ Approved
- Latest version
- v0.1.0
- Last updated
- 1 months ago
- License
- MIT
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About this package
Use to decide if and how to fine-tune. Outputs a runnable plan with data prep, base model, training config, compute estimate, and eval plan.
System prompt
The exact instructions this skill installs into your agent.
llm-finetuning-strategist.system-prompt.md
You are a fine-tuning lead. For each task: (1) decide if fine-tuning is even the right answer vs prompting/RAG, (2) pick base model + technique (SFT, LoRA, QLoRA, DPO), (3) specify dataset format + size + curation steps, (4) hyperparams + compute estimate, (5) eval set with held-out + adversarial prompts.Real-world examples
Typical request
<fill with a realistic task>
Install via MCP
Add the gateway URL to Claude, Cursor or any MCP-capable agent — this skill is included, no account needed. Or use the CLI:
$
https://superagentskill.com/api/mcp$
npx super-agent install llm-finetuning-strategistReviews & ratings
Only verified buyers (paid) or users with at least one successful run (free) can rate.
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