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 reatlat/fullstory-claude-plugin --skill lifecycle-analyzergit clone --depth 1 https://github.com/reatlat/fullstory-claude-pluginWrote 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/reatlat/fullstory-claude-plugin/lifecycle-analyzer)<a href="https://agentmods.dev/skills/reatlat/fullstory-claude-plugin/lifecycle-analyzer"><img src="https://agentmods.dev/badge/skills/reatlat/fullstory-claude-plugin/lifecycle-analyzer/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/reatlat/fullstory-claude-plugin/lifecycle-analyzer"><img src="https://agentmods.dev/badge/skills/reatlat/fullstory-claude-plugin/lifecycle-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.01662 |
| Opus 5 | $0.00019 | $0.00831 |
| Sonnet 5 | $0.00008 | $0.00332 |
| Haiku 4.5 | $0.00004 | $0.00166 |
Grade A, and why
lifecycle-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 9d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lifecycle Analyzer
Map your users across the lifecycle — new, engaged, at-risk, churned — and understand what moves them from one stage to the next.
When to Use
- "How many users are at risk of churning?"
- "What percentage of new users become active?"
- "Show me the lifecycle breakdown for Q3"
- "What's the most common path from new → churned?"
- "Which lifecycle stage has the biggest drop-off?"
- "Compare lifecycle between enterprise and free users"
Mental Model
Every user is in one of these lifecycle stages:
| Stage | Definition | Segment |
|---|---|---|
| New | First session in last 7 days | first_seen in last 7 days |
| Activating | 2-5 sessions, exploring features | 2-5 sessions, first_seen in last 30 days |
| Active | Regular usage, established patterns | 6+ sessions in last 30 days |
| At-Risk | Declining usage, previously active | was active (6+ sessions) last month, <3 sessions this month |
| Churned | No activity in 30+ days | last_seen before 30 days ago |
| Resurrected | Returned after churning | last_seen before 30 days ago, but active in last 7 days |
Workflow
Step 1: Build lifecycle segments
Build one segment per stage:
fullstory:build_segment("users with first_seen in last 7 days") → new_users
fullstory:build_segment("users with 2-5 sessions and first_seen in last 30 days") → activating
fullstory:build_segment("users with 6+ sessions in last 30 days") → active
fullstory:build_segment("users with 6+ sessions last month and <3 sessions this month") → at_risk
fullstory:build_segment("users with last_seen before 30 days ago") → churned
Step 2: Measure lifecycle distribution
For each segment, get the user count:
fullstory:build_metric(query="unique users", output_type="single_number")
→ compute for each segment
Present the distribution:
Lifecycle Distribution (Aug 2026)
New: 2,100 (14%) 🆕
Activating: 3,400 (23%) 🌱
Active: 5,800 (39%) ✅
At-Risk: 1,900 (13%) ⚠️
Churned: 1,700 (11%) 💤
Total: 14,900
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.
- 9d ago First seen · 158 lines · 39 tokens per session scan A f69a3042003e
lifecycle-analyzer is a skill published in the GitHub repository reatlat/fullstory-claude-plugin (62 stars, last pushed 27d ago), licensed MIT. It adds 39 tokens to every session and 1,662 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 2026-08-30.
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