How do I install the Ml skill?

Run npx skillmds@latest add telagod/ml in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ml?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

telagod (@telagod) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ml by telagod — Agent Skill

Name: Ml
Author: telagod

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.

telagod f4531a9 6 files · 21.9 KB Updated Sep 17, 2026

File contents

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.

---name: mldescription: 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, trapsRule 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 neighborsModeling and evaluation judgment. The serving infrastructure around a model is ordinarybackend (`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 yourown 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). Thefailure mode of this field is not bad models — it is unearned confidence in numbers.

Frequently asked questions

Ml

File contents

ML — approach, data, evals, LLM craft, traps

Route by moment

Scope and neighbors

The stance

Related skills

Frequently asked questions

How do I install the Ml skill?

What does the Ml skill do?

Is Ml safe to use?

Which AI agents work with Ml?

Is Ml free to use?

Who published Ml?