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 tuanductran/hr-skills --skill hr-employee-journey-mappinggit clone --depth 1 https://github.com/tuanductran/hr-skillsWrote 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/tuanductran/hr-skills/hr-employee-journey-mapping)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-employee-journey-mapping"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-employee-journey-mapping/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/tuanductran/hr-skills/hr-employee-journey-mapping"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-employee-journey-mapping.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 | $0.00071 | $0.00869 |
| Opus 5 | $0.00036 | $0.00434 |
| Sonnet 5 | $0.00014 | $0.00174 |
| Haiku 4.5 | $0.00007 | $0.00087 |
Grade A, and why
hr-employee-journey-mapping 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Employee journey mapping
Map the end-to-end employee journey — from attraction through alumni — to identify moments that matter, surface friction points, and redesign specific stages around what employees actually experience.
Supported tasks
- Building end-to-end employee journey maps across the full lifecycle
- Mapping detailed journeys for specific stages (onboarding, promotion, parental leave)
- Identifying "moments that matter" that disproportionately shape employee sentiment
- Surfacing friction points and pain points within a specific journey stage
- Combining qualitative (interviews, surveys) and quantitative (systems data) journey inputs
- Comparing intended journey design against actual lived employee experience
- Designing journey improvements and prioritizing which to fix first
- Building persona-specific journey maps (new grad, senior IC, people manager, returning parent)
- Facilitating journey mapping workshops with cross-functional stakeholders
- Translating journey map findings into concrete process or policy changes
- Measuring the impact of journey redesign on experience metrics
- Maintaining and updating journey maps as processes evolve
Key prompts
Building journey maps
- "Build an end-to-end employee journey map from attraction through alumni for [company/role type]."
- "Map a detailed journey for the [onboarding / promotion / parental leave / offboarding] stage, including touchpoints, emotions, and systems involved."
- "Design a persona-specific journey map for [new graduate / senior IC / returning parent] that reflects their distinct experience."
- "What data sources — surveys, exit interviews, HRIS timestamps — should feed into an accurate journey map for [stage]?"
Identifying moments and friction
- "Identify the 'moments that matter' in our employee journey — points that disproportionately shape how employees feel about the company."
- "Where are the biggest friction points in our [specific stage] journey based on this qualitative and quantitative data?"
- "Compare our intended [journey stage] design against what employees report actually experiencing — where's the gap?"
- "How do we distinguish a true moment that matters from a minor annoyance that doesn't significantly shape the employee experience?"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 65 lines · 71 tokens per session scan A 77f86ea95da3
hr-employee-journey-mapping is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed 2d ago), licensed MIT. It adds 71 tokens to every session and 869 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…