agentsclimarketplace
Sign in

Stakeholder alignment

Skill hollandkevint/data-product-operator/skills/stakeholder-alignment

Translates between technical data teams and business stakeholders. Use when preparing stakeholder updates, translating technical data work for executives, shaping vague business requests into buildable specs, navigating competing priorities across data consumers, or when someone asks "how do I explain this to my VP?" or "the business team wants X but that's not how data works."From its SKILL.md

Install
npx -y skills add hollandkevint/data-product-operator --skill stakeholder-alignment

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.0 KB, 583 tokens by cl100k_base, as published. Nobody here has run it

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":

  1. Ask what decision the dashboard enables (not what data it shows)
  2. Ask who will use it and how often
  3. Ask what they do today without it (the workaround reveals the real need)
  4. 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.

Resolving Competing Priorities

Use a Betting Table (quarterly or per-cycle):

  • Each stakeholder pitches their top priority with business impact
  • The squad evaluates feasibility and effort for each
  • Leadership allocates fixed capacity to the highest-impact bets
  • Boundaries enable autonomy: what's fixed (strategic direction) vs. flexible (implementation approach)

AVOID the loudest-voice-wins pattern. Data team capacity is finite. Every yes is an implicit no to something else. Make the tradeoffs explicit.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most operations skills give in 583 tokens

Counted across 483 of the 484 authors here whose files we hold, read 2026-08-07

  • Collect monitoring data throughout the simulationin 14 of 483, across 6 files
  • Set the random seed for reproducibilityin 14 of 483, across 6 files
  • Validate simulations against analytical solutionsin 12 of 483, across 4 files
  • Clarify goals, constraints, and inputsin 11 of 483, across 2 files
  • Implement contract tests for integration pointsin 11 of 483, across 2 files
  • Implement strangler fig infrastructure with API gatewayin 11 of 483, across 2 files
  • Audit modernized components for security vulnerabilitiesin 11 of 483, across 2 files
  • Avoid Python blocking calls in processesin 10 of 483, across 3 files
  • Use resource context managers for automatic cleanupin 9 of 483, across 2 files
  • Maintain consistent time unitsin 9 of 483, across 2 files
  • Validate outcomes against success criteriain 8 of 483, across 1 file
  • Analyze the legacy codebase for technical debtin 8 of 483, across 1 file

Said here and by no other author read

  • Bring extended stakeholders in during shaping
  • Lead with the decision it enables
  • Quantify impact in business terms
  • Name the tradeoff, not just the recommendation
  • Ask what decision the request enables
  • Ask who will use it and how often

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.