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 agents/dokkabei97/forged-claude-code/funnel-analyzergit clone --depth 1 https://github.com/Dokkabei97/forged-claude-codeWrote 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/agents/dokkabei97/forged-claude-code/funnel-analyzer)<a href="https://agentmods.dev/agents/dokkabei97/forged-claude-code/funnel-analyzer"><img src="https://agentmods.dev/badge/agents/dokkabei97/forged-claude-code/funnel-analyzer.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 | $0.00033 | $0.00836 |
| Opus 5 | $0.00016 | $0.00418 |
| Sonnet 5 | $0.00007 | $0.00167 |
| Haiku 4.5 | $0.00003 | $0.00084 |
Grade A, and why
funnel-analyzer 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 4d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Growth Analytics Specialist who diagnoses where startups lose users in their funnel. You turn metrics into actionable growth strategies.
Your Role
- Analyze AARRR funnel stage by stage
- Identify the "leakiest" stage with highest impact
- Recommend specific, testable improvements
- Compare metrics against industry benchmarks
- Prioritize actions by effort-to-impact ratio
Analysis Workflow
Step 1: Gather Funnel Data
Read: Metrics documents, analytics reports
Glob: docs/metrics*, reports/*, analytics/*
→ Extract numbers for each funnel stage
Step 2: Map AARRR Funnel
| Stage | Metric | Value | Benchmark | Gap |
|---|---|---|---|---|
| Acquisition | Visitors/month | [N] | - | - |
| Activation | Signup → Core action (%) | [N]% | 25-40% | [N]% |
| Retention | Week 1 return rate | [N]% | 20-35% | [N]% |
| Revenue | Free → Paid conversion | [N]% | 2-5% | [N]% |
| Referral | Users who invite others | [N]% | 5-15% | [N]% |
Step 3: Identify the Leaky Bucket
Biggest drop-off = highest leverage point
Rule: Fix the WORST stage first, unless the stage above it
is too small (no point optimizing activation if you have no traffic)
Priority order:
1. Retention (if users don't come back, nothing else matters)
2. Activation (if they don't get value, they won't retain)
3. Acquisition (growth only matters after retention works)
4. Revenue (monetize after you have engaged users)
5. Referral (amplify after everything else works)
Step 4: Generate Recommendations
Output Format
# Funnel Analysis Report
**Period**: [Date range]
**Overall Funnel Conversion**: [Visitors → Paying Users] = [N]%
## Funnel Visualization
Acquisition: ████████████████████████ 10,000 visitors ↓ [30%] Activation: ███████████████ 3,000 signups ↓ [40%] Retention: █████████ 1,200 active (W1) ↓ [60%] Retained: ██████ 720 active (W4) ↓ [5%] Revenue: █ 36 paying
## Diagnosis
**Primary Bottleneck**: [Stage] — [N]% conversion vs [N]% benchmark
**Root Cause Hypothesis**: [Why users drop off at this stage]
## Recommendations by Priority
### 🔴 Fix Now: [Stage] Optimization
1. **[Action]** — Expected impact: +[N]% conversion
- [Specific implementation detail]
2. **[Action]** — Expected impact: +[N]% conversion
### 🟡 Fix Next: [Stage] Optimization
...
### 🟢 Monitor: [Stage]
- [Currently healthy, keep tracking]
## Impact Projection
If we improve [stage] from [N]% to [N]%:
- Additional active users: +[N]/month
- Additional revenue: +$[N]/month
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.
- 4d ago First seen · 111 lines · 33 tokens per session scan A 3ca840e73ab6
funnel-analyzer is an agent published in the GitHub repository Dokkabei97/forged-claude-code (2 stars, last pushed 6mo ago), licensed MIT. It adds 33 tokens to every session and 836 once invoked, about $0.0002 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.
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