model
Algorithm/model development and fine-tuning skill. Use for tasks like dataset design/cleaning, supervised fine-tuning (SFT), preference optimization (DPO/RLHF concepts), LoRA/QLoRA, training configs, evaluation (offline/online), safety checks, deployment packaging, and cost/performance trade-offs.
npx skills add majiayu000/claude-skill-registry --skill model --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# model Use this skill for 算法/模型开发/模型微调：从数据到训练到评测再到上线。 ## Defaults / assumptions to confirm - Goal: improve quality, reduce cost/latency, add domain knowledge, safety alignment? - Base model and license constraints - Hardware: local GPU / multi-GPU / cloud - Target inference stack (vLLM, TGI, llama.cpp, etc.) ## Workflow 1) Define the objective and success metrics - Task definition and input/output format. - Primary metrics (task-specific) + guardrails (safety, latency, cost). - Failure analysis categories (hallucination, format errors, refusal, toxicity). 2) Data strategy (most important) - Collect/curate dataset; define labeling guidelines. - Remove duplicates, leakage, PII, and near-duplicates. - Balance by scenario; ensure coverage of edge cases. - Split train/val/test with strict leakage prevention. 3) Choose training approach - SFT for instruction following and domain formatting. - LoRA/QLoRA for efficient fine-tuning (default for most cases). - DPO/Preference tuning when “style/quality preference” is the target. - Avoid fine-tuning when RAG or prompting solves it cheaper. 4) Training setup - Pick tokenizer/model family compatibility. - Hyperparameters: LR, batch size, sequen
- Defaults / assumptions to confirm
- Workflow
- Outputs
What does the model skill do?
Algorithm/model development and fine-tuning skill. Use for tasks like dataset design/cleaning, supervised fine-tuning (SFT), preference optimization (DPO/RLHF concepts), LoRA/QLoRA, training configs, evaluation (offline/online), safety checks, deployment packaging, and cost/performance trade-offs.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.