gaelic-ghost 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-skillsfor provider, GPU, endpoint, cost, and teardown decisions. - Use
python-skillsfor Python packaging and environment repair. - Use
apple-dev-skillsfor Swift/Xcode application integration after the runtime has been chosen. - Use
agent-engineering-skillsfor evaluating agent skills, prompts, or plugin packages rather than model checkpoints. - Use
cybersecurity-skillswhen 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.