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 hollandkevint/data-product-operator --skill stakeholder-alignmentgit clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/hollandkevint/data-product-operator/stakeholder-alignment)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/stakeholder-alignment"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/stakeholder-alignment/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/hollandkevint/data-product-operator/stakeholder-alignment"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/stakeholder-alignment.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 | $0.00072 | $0.00675 |
| Opus 5 | $0.00036 | $0.00338 |
| Sonnet 5 | $0.00014 | $0.00135 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
stakeholder-alignment 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core Model: Extended Squad
Data products are built by a core squad with extended stakeholder input:
Core Squad (day-to-day, co-equal authority):
- Product Manager: strategy and prioritization
- Tech Lead: architecture and feasibility
- Design Lead: UX and usability
- Data Lead: data quality, ethics, and engineering
Extended Squad (contribute expertise, not approval gates):
- Legal/Compliance: regulatory input early in shaping, not as a late blocker
- Medical Affairs / Domain SMEs: validate domain accuracy
- Commercial/Sales: pricing, GTM, customer feedback
- Finance: budget and ROI modeling
- Data Governance: policy and access control
CRITICAL: Bring extended stakeholders in during shaping (before commitment), not during delivery (as surprise reviewers). Early input prevents late vetoes.
Translation Patterns
When translating technical data work for business audiences:
Replace jargon with outcomes. "We normalized the schema and added SCD Type 2" becomes "Historical changes are now tracked, so you can see how patient records evolved over time."
Lead with the decision it enables. Not "we built a pipeline" but "you can now see which patients are at risk of readmission within 24 hours of discharge."
Quantify impact in business terms. Not "query performance improved 10x" but "the report that took 45 minutes now takes under 5 seconds, saving your team 3 hours per week."
Name the tradeoff, not just the recommendation. "We can ship in 3 weeks with 90% accuracy, or 6 weeks with 99%. The 90% version catches the same high-risk patients but may flag 10% more false positives."
Shaping Vague Requests
When a stakeholder says "I need a dashboard for X":
- Ask what decision the dashboard enables (not what data it shows)
- Ask who will use it and how often
- Ask what they do today without it (the workaround reveals the real need)
- Ask what "good enough" looks like (perfect is the enemy of shipped)
NEVER build what was asked for without understanding why it was asked for. The request is a symptom. The decision they need to make is the diagnosis.
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 · 64 lines · 72 tokens per session scan A 9dbf2b556216
stakeholder-alignment is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 675 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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