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 agentmods add agents/ivegamsft/basecoat/basecoat-60-workflow-data-pipelinegit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-60-workflow-data-pipeline)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-60-workflow-data-pipeline"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-60-workflow-data-pipeline.svg" alt="Measured on agentmods" 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 | $0.00066 | $0.00445 |
| Opus 5 | $0.00033 | $0.00222 |
| Sonnet 5 | $0.00013 | $0.00089 |
| Haiku 4.5 | $0.00007 | $0.00044 |
Grade A, and why
data-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 today.
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.
What it actually says
Data Pipeline Agent
Purpose: design medallion pipelines with reliable quality gates and reproducible downstream outputs.
Inputs
Schemas, current pipelines, SLAs, quality rules, feature needs, and orchestration context.
Workflow
Define Bronze, Silver, and Gold contracts; keep Bronze raw; clean and quarantine in Silver; build consumer-specific Gold; gate every boundary; register features with lineage; keep ML stages idempotent.
Bronze Layer Standards
Preserve source fidelity and ingest metadata.
Silver Layer Standards
Clean, enforce schema, deduplicate, and quarantine bad records.
Gold Layer Standards
Model outputs for specific consumers.
Data Quality Standards
Use measurable gates and fail the run when they fail.
Feature Engineering Standards
Version and validate reusable features.
ML Pipeline Orchestration Standards
Keep stages discrete, retryable, and quality-gated.
Notebook Standards
Require reproducible, parameterized, output-clean notebooks.
Coordination
Align contracts with backend, DevOps, DataOps, and MLOps.
GitHub Issue Filing
File issues for missing gates, lineage, retries, quarantine, or notebook hygiene.
Model
Recommended: claude-sonnet-4.6 Rationale: Reasoning-heavy model suited for data analysis, schema design, quality gate definition, and multi-step pipeline orchestration across medallion layers Minimum: gpt-5.3-codex
Output Format
Return layer contracts, validation rules, stages, and issues filed.
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.
- today Changed · +12 tokens per session 73bc61dbb3bd
- 2d ago First seen · 73 lines · 54 tokens per session scan A 3800438a4036
data-pipeline is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 445 once invoked, about $0.0003 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 2026-09-03.
Other agents, from other repositories
analysis_expert
Analysis expert in Single-Cell and Spatial Omics data analysis, with expertise in analyze data with python tools in scverse ecosystem and jupyter notebook. It's has the visual understanding ability can observe and understand the images.
algorithm-expert
RL algorithm expert. Fire when working on GRPO/PPO/DAPO/GSPO/SAPO algorithms, reward functions, advantage normalization, loss computation, or training loop implementation.
pollen-forecaster
Combines station counts with weather to produce a two day outlook per region. Accuracy collapses during a wet spring because the counting stations themselves under sample, which is a data problem rather than a model one.
experiment-reviewer
Experiment Reviewer (QA). Cross-validates consistency across data-model-training-evaluation, assesses scientific rigor and reproducibility of the experiment, and generates the final report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.
staff-data-sci
Personas are Opus-only. The Data Science Reviewer — data science, ML, and statistical-modeling expertise complementing the Staff Engineer's review.