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.
npx skills add ashermahonin/agentic-skills --skill data-ml-pipelinegit clone --depth 1 https://github.com/ashermahonin/agentic-skillsWrote 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.
[](https://agentmods.dev/skills/ashermahonin/agentic-skills/data-ml-pipeline)<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.
<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>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 | $0.00037 | $0.00465 |
| Sonnet 5 | $0.00015 | $0.00186 |
| Haiku 4.5 | $0.00007 | $0.00093 |
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 9d 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.
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 fromplatform-detector, security posture fromsecurity-secretsandsecurity-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-llmfor injection and disclosure risks.
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.
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.
- 9d ago First seen · 62 lines · 75 tokens per session scan A 82ef3d7032de
data-ml-pipeline is a skill published in the GitHub repository ashermahonin/agentic-skills (10 stars, last pushed 16d ago), licensed MIT. It adds 75 tokens to every session and 930 once invoked, about $0.0004 per session on Opus 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.
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