Choose model lab workflow

Skill gaelic-ghost/socket/skills/choose-model-lab-workflow

The Source for macOS Agent Workflows

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npx -y skills add gaelic-ghost/socket --skill choose-model-lab-workflow

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Route language-model training, data, evaluation, checkpoint, representation, steering, ablation, jailbreak, tool-calling, Apple-runtime, and benchmark requests. Use when the primary workflow or Socket owner is unclear.

SKILL.md

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Choose Model Lab Workflow

Outcome

Select one primary workflow, name any supporting workflows, and make the evidence boundary explicit before work begins.

Route The Request

Requested outcome	Primary skill
Define a hypothesis, controls, budget, and artifacts	`design-model-experiment`
Curate, transform, split, or document examples	`prepare-language-model-dataset`
Run SFT, LoRA, QLoRA, or a full parameter update	`fine-tune-language-model`
Measure capability, behavior, quality, or safety	`evaluate-language-model`
Decide which checkpoint is better and why	`compare-model-checkpoints`
Choose Core AI, Core ML, MLX, ExecuTorch, or Foundation Models	`choose-apple-model-runtime`
Locate or test internal representations	`research-model-representations`
Apply activation or weight-space behavior steering	`steer-language-model-behavior`
Remove or suppress a refusal direction	`ablate-refusal-representations`
Measure jailbreak or prompt-injection robustness	`evaluate-jailbreak-resilience`
Measure tool selection, arguments, execution, or recovery	`evaluate-tool-calling-model`
Compare latency, memory, energy, throughput, or artifact size	`benchmark-model-runtime`
Run preference optimization such as DPO/ORPO	Keep the experiment and eval here; use the supported TRL workflow through `fine-tune-language-model` until a stable standalone skill is earned
Pretrain or continue pretraining a foundation model	Do not collapse it into fine-tuning; define the distributed/corpus contract and treat `train-language-model` as a deferred skill candidate
Merge adapters or quantize/package an artifact	Use `compare-model-checkpoints` around the exact transformation; use the project-native tool and evaluate the deployable output
Evaluate an agent skill, plugin, or host harness rather than a model protocol	Hand off to `productivity-skills` and `agent-portability-skills`

Respect Ownership Boundaries

Use cloud-inference-skills for provider, GPU, endpoint, cost, and teardown decisions.
Use python-skills for Python packaging and environment repair.
Use apple-dev-skills for Swift/Xcode application integration after the runtime has been chosen.
Use productivity-skills for evaluating agent skills, prompts, or plugin packages rather than model checkpoints.
Use cybersecurity-skills when an authorized evaluation targets a deployed system instead of a model artifact.

Return A Routing Contract

State:

the primary skill;
supporting skills in execution order;
the controlled variable;
the artifact or metric that proves completion;
any paid compute, data-access, or deployment authorization required.

Do not silently turn research planning into a paid run, model publication, or production-system test.