stakeholder-alignment
Skill5 starsChanged 2 months agoMITView on GitHub
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."
5 other projects use a version of this.
Adopted by
Most recently taken up by
- Natan-Mohart/24-strategy-skills-for-claudetheir own version, first seen Aug 8, 2026
- RefoundAI/lenny-skillstheir own version, first seen Jul 15, 2026
- kili-d/pmm-kittheir own version, first seen Jul 15, 2026
- miovrag/claude-skillstheir own version, first seen Jun 12, 2026
- launchapp-dev/animus-clitheir own version, first seen Mar 15, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- RefoundAI/lenny-skills★ 1.3ksame name
- Natan-Mohart/24-strategy-skills-for-claude★ 41same name
- launchapp-dev/animus-cli★ 39same name
- kili-d/pmm-kit★ 2same name
- miovrag/claude-skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository24 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- analystSkill
- arbitrage-audit-dataSkill
- dashboards-to-decisionsSkill
- databricks-data-product-reviewSkill
- data-consumer-discoverySkill
- data-model-designSkill
- data-pipeline-qualitySkill
- data-product-operatorSkill
- data-product-thinkingSkill
- data-product-validationSkill
- data-quality-assessmentSkill
- data-storytellingSkill
- data-team-operating-modelSkill
- data-team-positioningSkill
- duckdb-data-profilingSkill
- ethical-risk-assessmentSkill
- grill-me-dataSkill
- healthcare-cohort-designSkill
- healthcare-data-domainSkill
- healthcare-data-readiness-debriefSkill
- healthcare-data-reconciliationSkill
- metrics-definitionSkill
- research-synthesis-dataSkill
The file
We split this skill into the rules it gives an agent
9 rules. 0 are common, 9 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
9
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 34
When translating technical data work for business audiences:
- Distinctive: line 46
When a stakeholder says "I need a dashboard for X":
- Distinctive: line 48
Ask what decision the dashboard enables (not what data it shows)
- Distinctive: line 49
Ask who will use it and how often
- Distinctive: line 50
Ask what they do today without it (the workaround reveals the real need)
- Distinctive: line 51
Ask what "good enough" looks like (perfect is the enemy of shipped)
- Distinctive: line 53
Never build what was asked for without understanding why it was asked for.
- Distinctive: line 57
Use a Betting Table (quarterly or per-cycle):
- Distinctive: line 63
AVOID the loudest-voice-wins pattern.
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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
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.
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.
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Your trail
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Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

