Trust Report
Growth Marketing Strategist
v0.1.0 · skill · @superagent
Trust vector
Not enough adversarial evidence yet to publish a verified score. We never default an untested package to a comfortable number — the dimensions below show what evidence exists so far, gated by confidence.
Published score = quality × confidence. More adversarial runs, case coverage and real-world executions raise confidence — so a few lucky runs can't earn a high score.
Latency (30d)
Robustness
Model heatmap (30d)
No telemetry yet. Connected agents can call report_execution via MCP after using this skill.
Robustness findings
CVE-style report of adversarial failures discovered by the SkillForge red-team pipeline. Public by default — we publish what we find so you can trust what you ship.
Compatibility matrix
Independent cross-model probe — runs every published example on each major frontier model and lets a neutral judge score the outcome. Updated by the SkillForge compatibility sweep.
How this trust score is computed
Trust Score v2 is evidence-gated: published score = quality × confidence, where quality is a weighted blend of four dimensions (safety, competence, freshness, coverage). Pass and success rates use the Wilson lower confidence bound, so large samples beat a handful of lucky runs, and an untested package shows Unverified rather than a default number. Real-world signals come from agent executions reported via the MCP report_execution tool. The formula is pure and reproducible offline — see src/lib/trust/scoring.ts.