research-synthesis-data — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited research-synthesis-data (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.
Not all evidence is equal. Rank sources in this order:
data-consumer-discovery workaround archaeology format. Time invested = validated demand.CRITICAL: When usage data contradicts interview data, usage data wins. People describe aspirational workflows. Logs show actual ones.
Build insights from the bottom up. Every level must trace to the one below it.
Raw observations tagged with source and date. One fact per nugget.
Example: [Interview: Sarah, Analytics Lead, 2024-01-15] Spends 4 hours every Monday rebuilding the regional performance report from 3 separate data exports.
Tag each nugget: source type (interview, log, ticket, observation), consumer segment (Explorer, Reporter, Decision-maker, Builder), and topic.
Three or more nuggets from independent sources pointing to the same conclusion. Less than three is anecdotal.
Example: 3 of 5 analytics leads manually combine data from 3+ sources weekly. Average time: 3.5 hours. All distrust the automated report because "the numbers don't match what I pull manually."
Patterns interpreted in context. Answers "so what?"
Example: Regional reporting is the highest-pain workaround across analytics. The root cause is inconsistent metric definitions across source systems, not missing data. Fixing the semantic layer would eliminate 15+ hours/week of manual reconciliation.
Insights translated into action. Each recommendation links to a specific skill or command:
data-product-validation scorecarddata-quality-assessment auditstakeholder-alignment prioritizationEvery discovery effort produces three artifacts:
1. Problem Brief — The top 3 problems ranked by evidence strength. For each: problem statement, evidence summary (nuggets and patterns), affected consumer segments, estimated impact. Feeds directly into /dpo:write-data-prd.
2. Data Landscape Assessment — Current state of relevant data sources. For each: what exists, quality baseline (cross-ref data-quality-assessment), gaps, access constraints. Feeds into data-product-validation Data Feasibility scoring.
3. Consumer Map — Who needs what, segmented by type (see data-consumer-discovery segments). Includes: frequency of need, current workaround, trust level, downstream dependents. Feeds into stakeholder-alignment prioritization.
When evidence conflicts:
Example: "Interview data suggests daily demand, but query logs show weekly access. Recommend monitoring actual usage for 2 weeks before committing to real-time refresh SLA."
NEVER present a clean narrative when the evidence is messy. Stakeholders deserve honest uncertainty over false confidence.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.