Install
$ agentstack add skill-lgrappag-workflows-agents-analytics-funnel-attribution ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
analytics-funnel-attribution
Overview
Track user acquisition, engagement, and retention funnels for game features—measuring skill adoption, workflow completion, and feature impact on business metrics. This skill enables data-driven iteration on framework quality and community engagement.
Key Capabilities
1. Funnel Tracking
- Event Stream: Track skill usage, workflow starts/completions, feature interactions
- Session Management: Identify user cohorts and session lifecycles
- Retention Curves: Measure D1, D7, D30 retention for features
- Convertion Tracking: Skill discovery → adoption → maintenance pipeline
- Churn Analysis: Identify users at risk and feature abandonment patterns
2. Attribution Modeling
- Multi-Touch Attribution: Credit skill exposure across multiple touchpoints
- First/Last-Click Models: Simple and complex attribution for causal inference
- Cohort Analysis: Compare feature adoption across user segments
- A/B Testing Framework: Controlled experiments on skill variants
- Incrementality Testing: Measure true impact vs. correlation
3. Analytics Infrastructure
- Event Validation: Schema validation, deduplication, late arrival handling
- Real-time Dashboards: Live funnel metrics with 20%)
- Cohort Insights: Generation of insights via statistical testing
- Custom Reports: Self-service dashboards for different stakeholder needs
- Export APIs: CSV/JSON export for external analysis
- Predictive Models: Churn prediction, lifetime value estimation
Implementation Pattern
// Pseudo-code: Event tracking and funnel analysis
namespace SkillAnalytics {
public class SkillEventTracker {
public void TrackSkillDiscovery(string skillId, string userId, string source) {
var evt = new Event {
EventType = "skill_discovered",
SkillId = skillId,
UserId = userId,
Source = source,
Timestamp = DateTime.UtcNow
};
EventStore.Log(evt);
}
public void TrackSkillAdoption(string skillId, string userId, string workflowId) {
EventStore.Log(new Event {
EventType = "skill_adopted",
SkillId = skillId,
UserId = userId,
WorkflowId = workflowId,
Timestamp = DateTime.UtcNow
});
}
public async Task ComputeFunnel(
string skillId,
TimeSpan period,
CancellationToken ct = default)
{
var discovered = await EventStore.Count(
eventType: "skill_discovered",
skillId: skillId,
period: period
);
var adopted = await EventStore.Count(
eventType: "skill_adopted",
skillId: skillId,
period: period
);
var completed = await EventStore.Count(
eventType: "workflow_completed",
skillId: skillId,
period: period
);
return new FunnelMetrics {
Discovered = discovered,
Adopted = adopted,
AdoptionRate = (double)adopted / discovered,
Completed = completed,
CompletionRate = (double)completed / adopted
};
}
}
}
Mandates
- Consent-First: Track only GDPR-compliant events with explicit user consent
- Privacy by Design: No PII in event stream, anonymized user IDs
- Accuracy Thresholds: Metrics must be 99%+ accurate for decision-making
- Latency Targets: Real-time dashboards update within 5 minutes
- Retention Policy: Raw events deleted after 90 days, aggregates kept for 2 years
Best Practices
- Event Schema First: Design events before implementation, validate strictly
- Track Behaviors, Not Identities: Focus on what users do, not who they are
- Correlation ≠ Causation: Use proper statistical testing for causal claims
- Sample Judiciously: Balance accuracy with cost through intelligent sampling
- Iterate on Metrics: Review and refine KPIs quarterly based on learnings
Key Metrics
| Metric | Target | Alert Threshold | |--------|--------|-----------------| | Skill Discovery → Adoption | >30% | 80% | 50% | 5min | | Data Freshness | 15min |
Resources
- [Event Schema Design Guide](docs/event-schema.md)
- [Attribution Model Comparison](docs/attribution-models.md)
- [GDPR Compliance Checklist](docs/gdpr-checklist.md)
- Example:
examples/funnel-dashboard-config.json
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: LgrappaG
- Source: LgrappaG/Workflows-Agents
- License: MIT
- Homepage: https://lgrappag.github.io/portfolio/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.