data-modeling
Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through. Use this to design or restructure a warehouse, model a new source, decide on grain or table structure, build a semantic or metric layer, or diagnose why queries are slow, wrong, or impossible to write.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/data-modeling && cp -r /tmp/data-modeling/plugins/data-analytics/skills/data-modeling ~/.claude/skills/data-modelingSKILL.md
# Data modeling ## Layers, and why the middle one matters Three layers, each with one job: 1. **Raw** — source data, append-only, otherwise unmodified. Do not apply *business* logic on ingest: you cannot recover what you discarded, and the logic will need to change retroactively. **Privacy and security transformations are the exception, and belong at ingest.** Credentials and secrets should never land in the warehouse at all. Personal data that is not needed should be dropped rather than stored and governed later, and identifiers you must keep but rarely need in the clear should be tokenized or encrypted on arrival. Retention and deletion apply from ingest, not from the marts. The distinction: strip what you must not hold, keep everything you are entitled to hold, and leave interpretation for later. 2. **Staging** — cleaned and conformed: consistent types, standardized names, deduplicated, no business logic yet. 3. **Marts** — business-facing models shaped for how questions are asked. The discipline that pays is keeping business logic out of layers 1 and 2. Logic embedded in ingestion cannot be changed retroactively, and it will need to change. ## Grain is the decision everything follows from State the grain of every table in one sentence: *one row per what*. "One row per order line per day" is a grain. "Order data" is not. Most modeling errors are grain errors, and they surface as fan-out — a join multiplying rows so every downstream sum is inflated. If a number is mysteriously too high, check the grain before checking the logic. ## Dimensional structure Facts for events and measurements; dimensions for the things being described. Keep facts narrow and long, dimensions wide and short. Conform dimensions across facts — one customer dimension, used everywhere. Separate customer tables per domain is how the same customer gets counted differently in two reports. **Handle history deliberately.** Overwriting a dimension attribute rewrites the past: last year's revenue silently re-attributes to this year's segment. Decide per attribute whether history matters, and where it does, keep versions with valid-from and valid-to. ## The semantic layer Define metrics once, above the marts, and have every consumer read through it. Without it, the same metric is reimplemented in each dashboard and they drift — not because anyone is careless, but because a filter differs. The semantic layer is where the metric dictionary becomes executable rather than documentary. ## Performance Model for the query pattern you actually have. Pre-aggregate what is queried constantly; leave the long tail to compute on demand. Partition and cluster on what people filter by — usually time, then a tenant or entity key. Most slow warehouse queries are full scans of a table that could have been partitioned by date. Denormalize deliberately, and write down why. Undocumented denormalization is indistinguishable from a modeling error six months later. ## Never - Build a mart directly on raw. The coupling means every source change breaks the business layer. - Mix grains in one table. - Let a dashboard contain business logic the warehouse does not. That logic is invisible and unversioned.
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.