data-product-validation — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited data-product-validation (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Score each dimension 1-5. Multiply all five for a composite score (max 3,125).
How often do consumers need this answer?
| Score | Frequency | Example |
|---|---|---|
| 5 | Daily or real-time | ICU readmission risk scores |
| 4 | Weekly | Regional performance reports |
| 3 | Monthly | Quarterly business reviews |
| 2 | Quarterly | Annual planning data |
| 1 | Rarely or once | Ad-hoc executive request |
What happens when consumers don't have this data?
| Score | Impact | Example |
|---|---|---|
| 5 | Critical decision blocked | Can't discharge patients without risk assessment |
| 4 | Significant delay or cost | Team spends 20+ hours/week on manual workaround |
| 3 | Moderate inconvenience | Report takes 3 hours instead of 5 minutes |
| 2 | Minor friction | Slightly slower process, acceptable workaround exists |
| 1 | Mild inconvenience | Nice to have, nobody changes behavior without it |
What are consumers doing today instead?
| Score | Effort | Example |
|---|---|---|
| 5 | Custom tooling built | Analyst maintains a 47-tab Excel model updated daily |
| 4 | Significant manual process | 3 people spend 2 days/week compiling reports |
| 3 | Partial automation | Script exists but breaks frequently, needs babysitting |
| 2 | Simple workaround | Quick Excel export, takes 30 minutes |
| 1 | Haven't tried | Nobody has attempted to solve this yet |
Can we actually build this with available data?
| Score | Feasibility | Example |
|---|---|---|
| 5 | Data exists, quality verified | Source audited, passes data-quality-assessment checks |
| 4 | Data exists, quality unknown | Source available but no quality baseline established |
| 3 | Data exists, known issues | Source has gaps or accuracy problems that need fixing first |
| 2 | Data partially exists | Need to combine 3+ sources, some missing |
| 1 | Data doesn't exist | Would need new data collection or acquisition |
Cross-reference data-quality-assessment for the 5-dimension scoring. A Data Feasibility score of 1 kills the idea regardless of other scores.
How locked-in are consumers once we ship?
| Score | Risk | Example |
|---|---|---|
| 5 | Easily re-modeled | Internal API, few consumers, versioning supported |
| 4 | Moderate coupling | Dashboard consumed by 5-10 users, change is disruptive but manageable |
| 3 | Significant coupling | 20+ downstream queries depend on current schema |
| 2 | High coupling | External consumers or contractual SLAs on schema shape |
| 1 | Breaking change impossible | Regulated output, schema changes require compliance review |
| Composite Score | Decision | Action |
|---|---|---|
| 250+ | Build | Shape a pitch, allocate a cycle |
| 100-249 | Investigate | Run a 1-week experiment to de-risk the lowest-scoring dimension |
| <100 | Kill | Document why and move on. Revisit only if new evidence surfaces |
CRITICAL: A Data Feasibility score of 1 kills the idea at any composite score. You can't build a data product without data.
When the scorecard says "Investigate," pick the cheapest experiment that addresses the weakest dimension:
Sample Query (1 day): Write the SQL. Run it against production data. Does the result answer the consumer's question? Tests Data Feasibility and reveals quality issues fast. If the query returns garbage, kill early.
Manual Pipeline (1 week): Build the transformation by hand for one consumer. Deliver a spreadsheet or flat file. Measure: did they use it? Did it change a decision? Tests Demand Frequency and Decision Impact with real behavior.
Schema Prototype (2-3 weeks): Build a minimal star schema with one fact table and 2-3 dimensions. Load a month of data. Let 2-3 consumers query it. Tests Schema Risk and reveals modeling assumptions before they calcify.
Not all data product decisions are equal:
Type 1 (irreversible): Schema design decisions. Data source commitments. Terminology mappings published to external consumers. SCD strategies once historical data accumulates. These deserve full validation and the scorecard process.
Type 2 (reversible): Adding a metric to an existing dashboard. New filters on an existing report. Feature additions to established products. These don't need a scorecard. Just build and measure.
Discovery budget should match decision type. Spend 2 weeks validating a Type 1. Spend 2 hours on a Type 2.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.