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 agentmods add skills/lgrappag/workflows-agents/analytics-funnel-attributionnpx 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.svg" alt="Measured on agentmods" 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 | $0.00009 | $0.00613 |
| Sonnet 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 5d 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.
- 5d 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 4mo ago), licensed MIT. It adds 17 tokens to every session and 1,227 once invoked, about $0.0001 per session on Opus 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.
Other skills, from other repositories
skill-creator
Create or improve Zhin Agent skills (SKILL.md). Use when asked to add a skill, write SKILL.md, document a repeatable agent workflow, or refine skill frontmatter/keywords. Triggers: 创建技能, 写 SKILL, skill-creator, 加 skill.
mass-line
触发:当你需要收集多方意见、把零散反馈整合成可执行方案,或把方案带回真实使用者/执行者验证时调用;常见信号包括 stakeholder input、user feedback、意见汇总、对齐与验证。 English: Trigger when input must be gathered from many people, synthesized into a clearer plan, and returned to the affected users or executors for validation. Use this skill for a collect-synthesize-validate loop.
protracted-strategy
触发:当目标长期、任务复杂、资源暂时处于劣势,或短期无法速胜但又不能放弃时调用;常见信号包括 long-term effort、phased plan、endurance、战略耐心、需要分阶段推进。 English: Trigger when the work is long-horizon, difficult, and unlikely to be won quickly. Use this skill to divide the effort into stages, keep strategic confidence, and accumulate small wins into overall victory.
practice-cognition
触发:当你提出了方案、假设或判断,需要通过实践验证、试错迭代或复盘升级认知时调用;常见信号包括 experiment、prototype、validate、iterate、feedback loop。 English: Trigger when an idea, hypothesis, or plan must be tested in practice and improved through iteration. Use this skill to move from action to understanding and back to action in a spiral learning loop.
to-issues
Decompose a PRD and/or SPEC into implementable, vertically-sliced Issues with real blocking edges, then create them in your chosen platform (GitHub or Local). Use after /prd (and optionally /prd-to-spec) to turn requirements into agent-ready tickets. Triggers on: create issues, to-issues, 创建issue, 拆解issue, 生成卡片, 创建卡片…
iflytek-hyper-tts
Use when user asks to synthesize speech, convert text to audio, or read text aloud. 讯飞超拟人语音合成 - 支持文本转语音、语音合成(发音人/语速/语调/音量/输出格式)。大模型语音合成技能。语音合成, 文字转语音, 超拟人, TTS.