| user-invocable | false |
| name | ml |
| description | Machine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working with training/eval data or labels; building or reviewing evals for models and LLM features; designing RAG, structured output, or agent pipelines; or diagnosing why a model/LLM feature underperforms. Method-selection ladder, data and leakage discipline, eval-as-spec rules, LLM-era craft, and a trap catalog. |
ML — approach, data, evals, LLM craft, traps
Rule content lives in the five files below; this SKILL.md only routes
(doctrine/04-maintenance.md governs edits to this bundle too).
Route by moment
| You are about to… | Read (in this folder) |
|---|
| Decide whether ML/an LLM is warranted, and which method rung to use | approach.md |
| Touch a dataset, labels, or splits; suspect a score is too good | data.md |
| Define success, build/judge an eval, or assess someone's metric claim | evals.md |
| Build with LLMs: prompts, RAG, structured output, agents, model choice | llm.md |
| Diagnose an underperforming model or LLM feature | data.md §1 first (read real failures), then llm.md §3 if RAG, traps.md to name the pattern |
| Review an ML project's health; name why a claim or pipeline smells wrong | traps.md |
A new ML feature usually runs approach.md (interrogate + pick the rung) →
evals.md §1 (eval BEFORE build) → data.md → then llm.md if the rung is LLM-shaped
→ skim traps.md §C before finalizing any launch or monitoring plan.
Scope and neighbors
Modeling and evaluation judgment. The serving infrastructure around a model is ordinary
backend (backend bundle: APIs, queues, operate.md); experiment execution discipline is
methods (investigate/verify); whether to delegate → doctrine.
The stance
The eval is the spec; anything unmeasured is folklore. Look at the data with your
own eyes (data.md §1), climb the method ladder from the cheapest rung (approach.md
§3), and treat every surprising score as leakage until disproven (data.md §2). The
failure mode of this field is not bad models — it is unearned confidence in numbers.
