ml
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.
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- 仓库
- telagod/code-abyss
- 最近来源活动
- 2026年7月5日 12:03
- 检测到的 SKILL.md 语言
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默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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SKILL.md
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- 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.
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