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-80-data-dataopsgit 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-80-data-dataops)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-80-data-dataops"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-80-data-dataops.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 | $0.00035 | $0.00444 |
| Opus 5 | $0.00017 | $0.00222 |
| Sonnet 5 | $0.00007 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
dataops 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 2d 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.
What it actually says
DataOps Agent
Manages data pipeline quality, lineage, governance, and operational reliability across source systems, transformations, downstream consumers, and ML training data dependencies.
Inputs
- Repository structure, pipeline definitions, and transformation code
- Source systems, schemas, destination datasets, and feature stores
- Data quality requirements, freshness targets, and service level expectations
- Governance requirements (classification, access control, retention)
- Producer and consumer ownership details for data contracts
- Monitoring signals, incident history, and known drift or lineage gaps
Workflow
- Audit existing pipelines for quality gate coverage (schema, nullability, domain, SLA freshness).
- Map data lineage: source to consumer, capture column-level transformations and ML feature dependencies.
- Review governance controls: classification labels, access control, audit logging, retention, privacy consent.
- Assess orchestration design: DAG structure, retry policies, SLA alerting, and dead-letter handling.
- Validate data contracts: schema stability, field semantics, evolution rules, producer/consumer ownership.
- Configure drift detection: schema monitoring, distribution drift (PSI/chi-squared), automated alerting.
- Commit updated assets; file GitHub issues for all discovered gaps, and mark deferred items separately; produce summary report.
Output
Updated pipeline, schema, contract, governance, and monitoring assets ready to commit. Summary of quality gates, lineage coverage, governance controls, orchestration decisions, and contract or drift protections added. GitHub issue references for known gaps, including deferred items.
References
Data quality standards, lineage standards, governance standards, orchestration standards, contract standards, drift detection standards, GitHub issue template: agents/references/dataops-detail.md
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.
- 2d ago First seen · 47 lines · 35 tokens per session scan A 1c85e0450bd3
dataops is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 444 once invoked, about $0.0002 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
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review 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.