Ml Agent Skill
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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/telagod/code-abyss --skill mlThe instruction itself
4 sections, as written by the authorML — 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.
Other skills for the same job
different authors, same section of the catalogue
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
How to use it
Take telagod/ml from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.