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 nexus-labs-automation/mobile-observability --skill user-journey-trackinggit clone --depth 1 https://github.com/nexus-labs-automation/mobile-observabilityWrote 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/nexus-labs-automation/mobile-observability/user-journey-tracking)<a href="https://agentmods.dev/skills/nexus-labs-automation/mobile-observability/user-journey-tracking"><img src="https://agentmods.dev/badge/skills/nexus-labs-automation/mobile-observability/user-journey-tracking/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/nexus-labs-automation/mobile-observability/user-journey-tracking"><img src="https://agentmods.dev/badge/skills/nexus-labs-automation/mobile-observability/user-journey-tracking.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 | $0.00593 |
| Opus 5 | $0.00019 | $0.00296 |
| Sonnet 5 | $0.00008 | $0.00119 |
| Haiku 4.5 | $0.00004 | $0.00059 |
Grade A, and why
user-journey-tracking 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 12d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Journey Tracking
Track not just WHAT users do, but WHETHER they accomplished their goal.
Core Principle
Every journey event should help answer: "Why did users fail to complete their intended task?"
Key Context to Attach
| Field | Example | Purpose |
|---|---|---|
job_name |
"checkout" | User's intended task |
job_step |
"payment" | Current step in journey |
job_progress |
"3/4" | How far they got |
outcome |
"success" / "friction" / "abandon" | Did they succeed? |
Friction Signals to Track
Detect user struggle before they contact support:
| Signal | Detection |
|---|---|
| Rage taps | 3+ taps same element in 1s |
| Retry exhaustion | 3+ retries of same action |
| Quick abandonment | Exit within 5s of error |
| Navigation loops | 3+ back navigations without progress |
Outcome Quality
Not just success/failure:
- Completed smoothly — no friction
- Completed with friction — retries, errors, slow
- Abandoned after friction — struggled, then quit
- Abandoned immediately — no engagement
"Completed with friction" is often the most actionable signal.
When to Use This Skill
- Onboarding flows
- Checkout/payment funnels
- Signup/registration
- Any multi-step process
- Feature adoption tracking
Implementation References
| Topic | Reference |
|---|---|
| Full methodology | references/user-focused-observability.md |
| Job-based patterns | references/jtbd.md |
| Friction detection code | references/user-journeys.md |
| Journey correlation | references/user-journeys.md |
Decision Tree
Before adding journey instrumentation:
- Does this help identify what the user was trying to do? → Add intent context
- Does this help determine if they succeeded? → Track outcomes
- Does this help explain why they failed? → Add friction signals
If no to all three → probably don't need it.
Related Skills
- See
skills/instrumentation-planningfor prioritization framework - Combine with
skills/interaction-latencyfor friction detection on key actions - Combine with
skills/navigation-latencyfor screen transition context
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.
- 12d ago First seen · 85 lines · 39 tokens per session scan A a94b55ff6d4d
user-journey-tracking is a skill published in the GitHub repository nexus-labs-automation/mobile-observability (116 stars, last pushed 19d ago), licensed MIT. It adds 39 tokens to every session and 593 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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