Sigma domains

Skill navidgh66/sigma/skills/sigma-domains

Auto-surface the right sigma domain context-engine when implementing or verifying a data-science / AI-engineering task. Routes to hand-authored implementer guidance, verifier checks, and a logic-evaluator for the matching domain. Use when a task touches: classical ML (scikit-learn, feature engineering, cross-validation, model selection), deep learning (PyTorch / TensorFlow, training loops, architectures, distributed training), NLP (tokenization, transformers, classification, NER, semantic search, summarization), reinforcement learning (policy gradient, actor-critic, value-based, reward shaping, RLHF, environments), data analysis (EDA, A/B testing, statistical testing), data engineering (Airflow DAGs, dbt models, Spark jobs, pipelines), AI agent engineering (agent harness design, tool definitions, orchestration, evals), MLOps (experiment tracking, model registry, monitoring, production readiness), or LLM engineering (prompt engineering, RAG, fine-tuning, LLM evals). Trigger when about to write or review code in any of these areas, or when a sigma task is annotated with a domain.From its SKILL.md

Install

npx -y skills add navidgh66/sigma --skill sigma-domains

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.9 KB, as published. Nobody here has run it

sigma-domains

Load the domain context-engine that fits the task before implementing or verifying. Each domain ships three kinds of guidance, kept as the single source of truth under context-engines/<domain>/:

implementers/*.md — how to build it correctly (the MAKER guidance).
verifiers/*.md (minus the logic file) — PASS/WARN/FAIL checks (the CHECKER).
verifiers/logic-evaluator.md — reasoning / plan-coherence grading (a third, distinct axis from code-quality verification).

This skill indexes those files — it does not copy them. Always read the actual file for current guidance.

Pick the domain

Domain	Use when the task is about
`classic-ml`	scikit-learn, feature engineering, cross-validation, hyperparameter tuning, model selection
`deep-learning`	PyTorch / TensorFlow models, training loops, architectures (CNN/RNN/Transformer), distributed training, determinism/devices
`nlp`	tokenization, transformer fine-tuning, classification, NER / sequence labeling, semantic search, generation / summarization
`rl`	policy gradient, actor-critic, value-based methods, reward shaping, RLHF, environment design, reward hacking
`data-analysis`	EDA, A/B testing, statistical testing / soundness
`data-engineering`	Airflow DAGs, dbt models, Spark jobs, pipeline soundness
`ai-agent-engineering`	agent harness design, tool definitions, orchestration, agent evals / soundness
`mlops`	experiment tracking, model registry, monitoring, production readiness
`llm-engineering`	prompt engineering, RAG, fine-tuning, LLM evals / soundness

If a sigma task line is annotated (domain) (e.g. - [ ] T1 (nlp): ...), use that domain directly.

Workflow

Match the task to a domain from the table above.
Implementing? Read context-engines/<domain>/implementers/ and open the file(s) matching the sub-task (filenames are specific, e.g. nlp/implementers/tokenization.md). Follow that guidance; make the smallest correct change.
Verifying? Read context-engines/<domain>/verifiers/ (the non-logic files) and apply the PASS/WARN/FAIL checks. The implementer and verifier must be different agents (maker ≠ checker).
Logic check? Read context-engines/<domain>/verifiers/logic-evaluator.md and grade plan↔implementation coherence and reasoning soundness — NOT style. This is a third distinct agent from both maker and checker.

Rules

context-engines/ is the source of truth — read the file, don't rely on this table for technical detail.
Keep maker / checker / logic-evaluator as distinct roles (sigma enforces the separation in cli/loop.py).
Load only the domain(s) a task actually needs (lean context).