What is data-ml-pipeline?

A guide for designing and reviewing data and machine-learning workflows, from collecting and transforming data to training, deploying, and monitoring models. It also covers systems that use embeddings, vector search, or RAG, a method that lets an AI answer from supplied documents.

Use it to define dataset rules, data ownership, privacy and retention requirements, data lineage, model evaluations, deployment checks, and monitoring plans. It applies to analytics systems, streaming data, training pipelines, model services, and document-search AI.

It helps prevent unreliable results caused by poor data quality, missing data history, untested models, or changes in live data. It also keeps the process repeatable and makes it clear when a model needs review or retraining.

data-ml-pipeline — Skill for Codex by ashermahonin/agentic-skills

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add ashermahonin/agentic-skills --skill data-ml-pipeline

Clone the repo

git clone --depth 1 https://github.com/ashermahonin/agentic-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/data-ml-pipeline/github.svg)](https://agentmods.dev/skills/ashermahonin/agentic-skills/data-ml-pipeline)

Your own site

<a href="https://agentmods.dev/skills/ashermahonin/agentic-skills/data-ml-pipeline"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/data-ml-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/ashermahonin/agentic-skills/data-ml-pipeline"><img src="https://agentmods.dev/badge/skills/ashermahonin/agentic-skills/data-ml-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 930 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00075	$0.00930
Opus 5.5	$0.00030	$0.00372
Sonnet 5.5	$0.00015	$0.00186
Haiku 4.5	$0.00007	$0.00093

Measured 2mo ago against content hash 82ef3d7032de, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-ml-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agentic/skills/data-ml-pipeline/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data / ML Pipeline

Purpose

Define how data is sourced, validated, transformed, versioned, evaluated, deployed, and monitored. Preserve provenance and reproducibility, and do not release a model without representative evaluation and a drift response.

Product context

Read references/data-ml-stages.md.
Pull architecture context from architecture-review, platform matrix from platform-detector, security posture from security-secrets and security-owasp-llm (if LLM-using).
Classify the work: analytical warehouse, lakehouse, streaming, feature store, training pipeline, model serving, RAG, fine-tune, eval harness, monitoring.
Use Context7 MCP for current docs of: data warehouses (BigQuery, Snowflake, Redshift, ClickHouse), lakehouse (Delta, Iceberg, Hudi), orchestrators (Airflow, Dagster, Prefect), streaming (Kafka, Flink, Materialize), feature stores (Feast, Tecton), ML platforms (MLflow, Weights & Biases, Vertex AI, SageMaker), vector stores (Pinecone, pgvector, Qdrant, Weaviate), and LLM providers.

Design and validation

Data contracts. For each dataset: schema, owner, source, freshness SLA, quality SLA, PII fields, retention, access.
Lineage. Map upstream → transformations → downstream consumers. No orphan datasets.
Quality gates. Per dataset: schema check, row-count anomaly, null-rate threshold, distribution drift detector, referential integrity.
Versioning. Datasets and models versioned with reproducible build. Lock training data + code + hyperparameters per artifact.
Evaluation harness. Per model: train/val/test split policy, evaluation metrics, baseline, fairness audit, robustness probes (adversarial, distribution shift, missing fields).
Deployment. Canary or shadow deploy; rollback path; monitoring on prediction distribution; A/B test plan if user-facing.
Drift monitoring. Input drift, output drift, performance drift; retraining triggers.
LLM-specific. Embedding model pinned; RAG corpus provenance and refresh cadence; eval set per persona; offline + online eval; coordinate with security-owasp-llm for injection and disclosure risks.

Read the full file on GitHub · 62 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 62 lines · 75 tokens per session scan A 82ef3d7032de

Subscribe to this mod's changes

data-ml-pipeline is a skill published in the GitHub repository ashermahonin/agentic-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 75 tokens to every session and 930 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metrics Data and AI RAG and vectors

data-ml-pipeline next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
data-ml-pipelineashermahonin/agentic-skills	Skill	10	2mo ago	MIT	A	75	Codex
embeddingsruvnet/ruflo	Skill	81k	yesterday	MIT	A	62	Claude Code
agent-platform-rag-engine-managementgoogle/skills	Skill	22k	yesterday	Apache-2.0	A	85	Claude Code, Codex
llm-app-patternsdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	54	Claude Code, Codex
9router-embeddingsdecolua/9router	Skill	32k	9d ago	MIT	A	66	Claude Code, Codex
embedding-strategiesforyourhealth111-pixel/Vibe-Skills	Skill	3.8k	3d ago	Apache-2.0	A	37	Claude Code, Codex
similarity-search-patternsforyourhealth111-pixel/Vibe-Skills	Skill	3.8k	3d ago	Apache-2.0	A	30	Claude Code, Codex