/choose-model-lab-workflow

name: choose-model-lab-workflow
description: 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.

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 `agent-engineering-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 `agent-engineering-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:

1. the primary skill; 2. supporting skills in execution order; 3. the controlled variable; 4. the artifact or metric that proves completion; 5. 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.