Stakeholder Requirements Gathering
Runs a structured intake interview so a vague "can you look into X" becomes a signed-off scope before any data gets touched.
Overview
Stakeholder Requirements Gathering runs at the start of any non-trivial analysis request, especially a vague one, when multiple stakeholders have a stake in the outcome, or when the result will drive an important decision.
What it covers
- Runs an intake interview to surface the business decision, the audience, what "done" looks like, and the real constraints.
- Classifies the decision as strategic, operational, or tactical to calibrate the required rigor and format.
- Documents the business question, success criteria, scope inclusions and exclusions, data sources, and timeline.
- Resolves any remaining ambiguity with techniques like 5-whys or MoSCoW prioritization.
- Gets explicit sign-off on the requirements before converting them into an authoritative analysis brief.
Part of Data Analytics Skills
This is one of 31 skills in the Data Analytics Skills library, in the Stakeholder Communication group.
Installation
- Clone the repo once:
git clone https://github.com/nimrodfisher/data-analytics-skills.git - Copy just this skill:
cp -r data-analytics-skills/05-stakeholder-communication/stakeholder-requirements-gathering ~/.claude/skills/(or~/.agents/skills/for Codex) - Ask your agent something like "help me scope this vague request before I start pulling data" and it activates on its own.
See the Data Analytics Skills entry for the full library.
Related assets
Frontend Design
Generates distinctive, production-grade UI that avoids generic "AI slop," with bold typography, cohesive color, and precise motion, implemented as real code.
Graphify
Turns a codebase, plus its docs, PDFs, images, and video, into a queryable knowledge graph that a coding agent can traverse instead of grepping raw files, and never guesses silently: every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS so you know what was found versus inferred.
Caveman
Makes your coding agent answer in a stripped down, high signal style that keeps every technical fact and cuts filler, saving output tokens on chat style questions.