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 uthumany/uthy-legacy-os --skill user-researchgit clone --depth 1 https://github.com/uthumany/uthy-legacy-osWrote 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/uthumany/uthy-legacy-os/user-research)<a href="https://agentmods.dev/skills/uthumany/uthy-legacy-os/user-research"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/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/uthumany/uthy-legacy-os/user-research"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/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 | $0.00030 | $0.01038 |
| Opus 5 | $0.00015 | $0.00519 |
| Sonnet 5 | $0.00006 | $0.00208 |
| Haiku 4.5 | $0.00003 | $0.00104 |
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 11d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Research
Overview
User research bridges the gap between what we think users need and what they actually need. This skill covers the full research lifecycle — from planning and conducting studies to synthesizing findings and making them actionable. Works for both qualitative (interviews, diary studies, field visits) and quantitative (surveys, analytics, logs) approaches.
When to Use
- Starting a new initiative and need to understand user needs
- Making a product decision where the cost of being wrong is high
- Building a research repository for your team
- Validating or invalidating assumptions about user behavior
- Don't use for: trivial UI decisions (A/B test or gut check is faster), already-well-understood problems
Instructions
Phase 1: Plan the Research
- Define the research question — What decision will this research inform? Frame as: "We need to decide X. What do we need to learn to make that decision confidently?"
- Choose the method:
- Generative (uncover unknown needs): interviews, diary studies, field observation
- Evaluative (test a concept): usability tests, concept tests, A/B tests
- Descriptive (measure behavior): surveys, analytics, log analysis
- Recruit participants — Define screener criteria. Aim for 5-8 per segment for qualitative, 200+ for surveys
Phase 2: Conduct the Research
For qualitative:
- Follow the customer-interviews skill for unstructured interviews
- For usability tests: give tasks, don't guide. Measure success rate, time-on-task, and satisfaction
- Take detailed notes. Record (with permission). Use a second observer if possible
For quantitative:
- Design surveys to avoid bias (no leading questions, balanced scales, randomize order)
- Use analytics to measure actual behavior, not self-reported behavior
- Triangulate: survey findings should be validated against behavioral data
Phase 3: Synthesize Findings
- Affinity mapping — Group observations by theme on a virtual or physical wall
- Thematic analysis — Identify 5-7 core themes. Each theme should have 3+ supporting data points
- Create research artifacts:
- Insight statements: "We learned X, which means Y, so we should do Z"
- Journey maps showing pain points
- Opportunity areas ranked by evidence strength
- Rate confidence — How many sources support each finding? High (5+ sources), Medium (3-4), Low (1-2)
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.
- 11d ago First seen · 95 lines · 30 tokens per session scan A 766d8658dd08
user-research is a skill published in the GitHub repository uthumany/uthy-legacy-os (5 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,038 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 2026-08-31.
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