data-governance
Establishes ownership, definitions, quality, access, and lineage for the organization's data. Use this when metrics disagree between teams, when nobody knows which dataset is authoritative, when setting up data ownership or access policy, when data quality is unreliable, or before opening a dataset to a wider audience.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/data-governance && cp -r /tmp/data-governance/plugins/data-analytics/skills/data-governance ~/.claude/skills/data-governanceSKILL.md
# Data governance Governance has a reputation for bureaucracy because it is usually implemented as approval queues. Done properly it is the opposite: it makes data usable without asking anyone. ## Start with definitions, not policy The highest-value governance artifact is a metric dictionary. For each business metric: - The **plain-language definition** — what it counts, and what it deliberately excludes. - The **computation**, unambiguously: source table, filters, time grain, timezone. - The **owner** — a person who decides when it is disputed. - **Known caveats** — when it is misleading, and what changed historically. Most metric disputes dissolve once both parties read the same definition and discover they were measuring different things. Almost none require a policy. Watch the ones that look obvious. "Active customer," "revenue," and "signup" each have half a dozen defensible definitions, and the ambiguity surfaces at the worst moment. ## Ownership Every dataset has a named owner accountable for its quality and access — a person, not a team. Unowned datasets decay, and nobody notices until a decision is made on stale data. The owner should sit with the business meaning, not with the pipeline. The team that generates the data understands what it means; the platform team understands how it moves. ## Quality, measured rather than asserted Test data like code, continuously, and alert on failures: - **Freshness** — did it arrive when expected? - **Volume** — is the row count within its normal range? A silent drop to zero is the classic failure. - **Uniqueness and nullity** on key fields. - **Referential integrity** across joins. - **Distribution** — has the shape shifted in a way nothing explains? The point is finding breakage before a decision is made on it. A pipeline that fails loudly is better than one that silently produces yesterday's numbers. ## Access Default to open for internal, non-personal data. Restrictive-by-default drives the shadow spreadsheet layer, which is genuinely less safe than a governed warehouse. Personal, financial, and regulated data are the exception: least privilege, purpose stated, reviewed periodically, with Legal & Risk involved on anything with a lawful-basis question. ## Lineage Know where a number came from and what feeds it. Without lineage, you cannot answer the two questions that matter during an incident: what broke upstream, and what downstream is now wrong. ## Never - Let two systems each claim to be the source of truth for the same fact. - Fix a data-quality issue in a dashboard. Fix it upstream or it recurs in every other consumer. - Retire a dataset because it looks unused — you cannot see every consumer. Deprecate, announce, then remove.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.