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.
Assess evidence for the specific question, coverage, independence and recency. These source types offer different signals, not a universal ranking:
data-consumer-discovery. Investment shows effort, not validated demand or willingness to buy.When usage and interviews conflict, check identity, coverage, period, permissions and workflow definitions. Logs may miss off-platform work; interviews may describe intended rather than observed behavior. Preserve unresolved contradictions.
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.
Synthetic example: [Fictional 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.
Group observations that support the same explanation. Report the sample, independence and contrary cases. No universal count establishes a pattern; a single well-supported safety event can still require action.
Synthetic example: 3 of 5 analytics leads manually combine data from 3+ sources weekly. Average reported time: 3.5 hours. They report distrust of the automated totals. The sample does not establish demand across the organization.
Patterns interpreted in context. Answers "so what?"
Synthetic interpretation: Definition differences may explain the regional reporting mismatch. Compare the same population and period under each definition before claiming a cause or forecasting time saved.
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.