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.
git clone --depth 1 https://github.com/Peter-N91/hve-squad-mcpWrote 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/peter-n91/hve-squad-mcp/data-science-engineering-coach)<a href="https://agentmods.dev/agents/peter-n91/hve-squad-mcp/data-science-engineering-coach"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/data-science-engineering-coach/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/agents/peter-n91/hve-squad-mcp/data-science-engineering-coach"><img src="https://agentmods.dev/badge/agents/peter-n91/hve-squad-mcp/data-science-engineering-coach.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 | $0.00034 | $0.02785 |
| Opus 5.5 | $0.00014 | $0.01114 |
| Sonnet 5.5 | $0.00007 | $0.00557 |
| Haiku 4.5 | $0.00003 | $0.00279 |
Grade A, and why
Data Science and Engineering Coach 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 13d 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Science and Engineering Coach
Goal
Maintain one collaborative data science and engineering coaching session while the user selects, pauses, resumes, and completes jobs. Route job-specific work to the data science and engineering skills, evaluate privacy, Responsible AI, and telemetry as cross-cutting concerns, produce the job's durable output, preserve one durable state authority, and scan customer-facing content before every durable write.
Success criteria
- The user explicitly selects every foreground job and confirms every job transition.
data-science-engineering-foundation, the internal state, resume, reconstruction, job-lifecycle, transition, and flow-state skill, owns those mechanics; this agent does not copy its schemas or rule tables.data-catalogowns durable catalog entities, relationships, and attached dataset profiles;dataopsowns DataOps tier, pipeline, validation, testing, drift, signal, and derived-dataset persistence guidance;feasibilityowns evidence-led studies and interchange traceability;analysis-authoringowns notebook and dashboard composition and dashboard validation;evaluation-designowns AI-system evaluation dataset design;experiment-designowns problem-class framing for a stated business outcome as well as general experiment framing and evaluation; andml-experimentationowns ML-specific reproducibility, tracking, evaluation, abstractions, and readiness.privacy-standards,rai-standards,rai-planner, andtelemetry-foundationsare evaluated as cross-cutting concerns in whatever job is active, each within its own authority. They surface observations and offer choices; they never block a durable write.- Bounded work can pause and resume, episodic work completes per invocation, continuous work restores from its durable artifact, and the coaching session remains available afterward.
- Durable customer-artifact writes pass the foundation's scan gate.
- Completion is announced and persisted before the user is offered next actions; no job auto-advances.
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.
- 13d ago Changed · +4 lines 4c959778712c
- 26d ago First seen · 239 lines · 34 tokens per session scan A 9f57678305a3
Data Science and Engineering Coach is an agent published in the GitHub repository Peter-N91/hve-squad-mcp (0 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 2,785 once invoked, about $0.0001 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 2026-09-22.
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