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 MonumentalSystems/Atlas-Agent-Teams --skill user-researchgit clone --depth 1 https://github.com/MonumentalSystems/Atlas-Agent-TeamsWrote 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/monumentalsystems/atlas-agent-teams/user-research)<a href="https://agentmods.dev/skills/monumentalsystems/atlas-agent-teams/user-research"><img src="https://agentmods.dev/badge/skills/monumentalsystems/atlas-agent-teams/user-research/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/monumentalsystems/atlas-agent-teams/user-research"><img src="https://agentmods.dev/badge/skills/monumentalsystems/atlas-agent-teams/user-research.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 | $0.00021 | $0.01126 |
| Opus 5 | $0.00010 | $0.00563 |
| Sonnet 5 | $0.00004 | $0.00225 |
| Haiku 4.5 | $0.00002 | $0.00113 |
Grade A, and why
user-research 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Research
Research Methodologies
User Interviews
- One-on-One Interviews: Deep, qualitative conversations with individual users
- Semi-Structured: Use a guide but allow flexibility to explore unexpected topics
- Open-Ended Questions: Ask questions that encourage detailed responses
- Active Listening: Listen more than you speak, probe for deeper understanding
- Recording: Record interviews (with permission) for later analysis
- Interview Length: 30-60 minutes is optimal for maintaining engagement
Surveys
- Survey Design: Keep surveys short and focused (5-10 minutes max)
- Question Types: Use a mix of multiple choice, rating scales, and open-ended questions
- Avoid Bias: Use neutral language and avoid leading questions
- Pilot Testing: Test surveys with a small group before full distribution
- Distribution Channels: Email, in-app, social media, or dedicated survey platforms
- Response Rates: Expect 10-20% response rate for email surveys
Usability Testing
- Moderated Testing: Researcher guides participants through tasks
- Unmoderated Testing: Participants complete tasks independently
- Think-Aloud Protocol: Ask participants to verbalize their thoughts
- Task Design: Create realistic tasks that represent actual user goals
- Metrics: Track task completion rate, time on task, error rate, and satisfaction
- Sample Size: 5 users reveal 80% of usability issues
Card Sorting
- Open Card Sort: Users create their own categories
- Closed Card Sort: Users sort into predefined categories
- Hybrid Approach: Combine both methods for comprehensive insights
- Tools: Use online tools for remote card sorting sessions
- Analysis: Look for patterns and consensus in how users organize information
- Application: Inform information architecture and navigation design
Persona Creation
Persona Development
- Research-Based: Personas should be based on real research data
- Demographics: Age, gender, location, education, occupation
- Psychographics: Goals, motivations, frustrations, attitudes
- Behaviors: How they interact with products, technology preferences
- Quotes: Include real quotes from interviews to bring personas to life
- Scenarios: Describe typical use cases and contexts
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 · 109 lines · 21 tokens per session scan A d768e5aeb041
user-research is a skill published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,126 once invoked, about $0.0001 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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