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 varunk130/ai-ux-skill-library --skill ai-journey-mappergit clone --depth 1 https://github.com/varunk130/ai-ux-skill-libraryWrote 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/varunk130/ai-ux-skill-library/ai-journey-mapper)<a href="https://agentmods.dev/skills/varunk130/ai-ux-skill-library/ai-journey-mapper"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-journey-mapper/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/varunk130/ai-ux-skill-library/ai-journey-mapper"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-journey-mapper.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 | $0.00059 | $0.02437 |
| Opus 5.5 | $0.00024 | $0.00975 |
| Sonnet 5.5 | $0.00012 | $0.00487 |
| Haiku 4.5 | $0.00006 | $0.00244 |
Grade A, and why
ai-journey-mapper 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Journey Mapper
Map the unique journeys users take when interacting with AI products - from first encounter through mastery. Unlike traditional journey mapping, AI journeys include trust arcs, capability discovery curves, mental model evolution, and the shifting balance of human-AI control. The PATHWAY framework captures what traditional journey maps miss.
Core Principle
Traditional journey maps track what users DO. AI journey maps must also track what users BELIEVE - because the gap between what users believe the AI can do and what it actually can do is where every AI UX problem lives.
The PATHWAY Framework
| Letter | Dimension | What to Map |
|---|---|---|
| P | Perception Evolution | How the user's mental model of the AI changes over time |
| A | Autonomy Gradient | How the balance of human vs. AI control shifts across the journey |
| T | Trust Arc | How trust rises, falls, and recovers through the experience |
| H | Help Moments | Where the user needs assistance understanding the AI (not just the product) |
| W | Wow Moments | Where the AI exceeds expectations and creates advocacy |
| A | Anxiety Points | Where the user feels uncertain, vulnerable, or out of control |
| Y | Yield Decisions | Where the user must decide: trust the AI, override it, or disengage |
AI Journey Map Structure
An AI journey map extends the traditional CJM with AI-specific rows:
Standard Rows (from traditional journey mapping)
| Row | Content |
|---|---|
| Phases | 4-6 stages of the AI adoption journey |
| Actions | What the user does at each phase |
| Touchpoints | Where interactions occur |
| Pain Points | Friction and frustration sources |
| Opportunities | Design improvement possibilities |
AI-Specific Rows (unique to this skill)
| Row | Content | Why It Matters |
|---|---|---|
| Mental Model | What the user believes the AI can do at this phase | Misaligned mental models cause 80% of AI UX failures |
| Trust Level | High / Medium / Low / Broken - with the event that caused the change | Trust is the #1 predictor of AI adoption and retention |
| Autonomy Balance | Who is in control: User-led → Collaborative → AI-led | The shift from "I use the AI" to "the AI works for me" is the key transition |
| Capability Awareness | Percentage of AI capabilities the user has discovered | Most users discover < 30% of capabilities in the first month |
| Verification Behavior | How much the user checks AI outputs | Decreasing verification = growing trust (or dangerous complacency) |
| Error Exposure | What AI failures the user has encountered | Each error type reshapes the mental model differently |
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 · 196 lines · 59 tokens per session scan A 978cbceda94a
ai-journey-mapper is a skill published in the GitHub repository varunk130/ai-ux-skill-library (3 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 2,437 once invoked, about $0.0002 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.
ai-journey-mapper compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ai-journey-mappervarunk130/ai-ux-skill-library | Skill | 3 | 1mo ago | MIT | A | 59 | Claude Code, Codex |
| component-family-consistencydembrandt/dembrandt-skills | Skill | 183 | 6d ago | MIT | A | 38 | Claude Code |
| extract-designdembrandt/dembrandt-skills | Skill | 183 | 6d ago | MIT | A | 43 | Claude Code |
| modular-scale-typographydembrandt/dembrandt-skills | Skill | 183 | 6d ago | MIT | A | 34 | Claude Code |
| data-display-and-selectiondembrandt/dembrandt-skills | Skill | 183 | 6d ago | MIT | A | 37 | Claude Code |
| design-dnazanwei/design-dna | Skill | 2.1k | 2mo ago | MIT | A | 192 | Claude Code, Codex |
| information-architecturedembrandt/dembrandt-skills | Skill | 183 | 6d ago | MIT | A | 33 | Claude Code |