Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LgrappaG/Workflows-Agents --skill analytics-funnel-attributiongit clone --depth 1 https://github.com/LgrappaG/Workflows-AgentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lgrappag/workflows-agents/analytics-funnel-attribution)<a href="https://agentmods.dev/skills/lgrappag/workflows-agents/analytics-funnel-attribution"><img src="https://agentmods.dev/badge/skills/lgrappag/workflows-agents/analytics-funnel-attribution/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lgrappag/workflows-agents/analytics-funnel-attribution"><img src="https://agentmods.dev/badge/skills/lgrappag/workflows-agents/analytics-funnel-attribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.01227 |
| Opus 5.5 | $0.00007 | $0.00491 |
| Sonnet 5.5 | $0.00003 | $0.00245 |
| Haiku 4.5 | $0.00002 | $0.00123 |
Grade A, and why
analytics-funnel-attribution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 <5min latency
- Batch Processing: Daily/weekly cohort analysis and retention calculations
- Data Privacy: GDPR compliance with anonymization and consent tracking
- Sampling: Efficient large-scale processing via intelligent sampling
4. Insights & Reporting
- Automated Alerts: Anomaly detection on key metrics (drop >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<FunnelMetrics> 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
};
}
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 146 lines · 17 tokens per session scan A 279fa922c784
analytics-funnel-attribution is a skill published in the GitHub repository LgrappaG/Workflows-Agents (2 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 1,227 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
analytics-funnel-attribution compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| analytics-funnel-attributionLgrappaG/Workflows-Agents | Skill | 2 | 6mo ago | MIT | A | 17 | Claude Code, Codex |
| skill-creatorzhinjs/zhin | Skill | 137 | today | MIT | A | 61 | Claude Code, Codex |
| mass-lineHughYau/qiushi-skill | Skill | 3.9k | 1mo ago | MIT | A | 195 | Claude Code |
| practice-cognitionHughYau/qiushi-skill | Skill | 3.9k | 1mo ago | MIT | A | 183 | Claude Code |
| protracted-strategyHughYau/qiushi-skill | Skill | 3.9k | 1mo ago | MIT | A | 211 | Claude Code |
| langfuselangfuse/skills | Skill | 334 | today | MIT | A | 121 | Claude Code |
| iflytek-contract-intelligence-reviewiflytek/iFly-Skills | Skill | 214 | 3d ago | Apache-2.0 | A | 116 | Claude Code, Codex |