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 nimadorostkar/Claude-Skills-collection --skill user-researchgit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/user-research)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/user-research"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/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/nimadorostkar/claude-skills-collection/user-research"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/user-research.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 | $0.00035 | $0.01501 |
| Opus 5 | $0.00017 | $0.00750 |
| Sonnet 5 | $0.00007 | $0.00300 |
| Haiku 4.5 | $0.00003 | $0.00150 |
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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Research
Purpose
Learn what users actually do, as opposed to what they say they would do. The distinction is everything: people are unreliable narrators of their own future behavior and reliable narrators of their own past.
When to Use
- Planning interviews or a usability test.
- Writing an interview guide.
- Synthesizing transcripts or session notes into findings.
- Deciding whether a feature idea is worth building.
Capabilities
- Study design and participant selection.
- Interview questions that elicit behavior rather than opinion.
- Usability-test design and moderation.
- Synthesis: from transcripts to themes to recommendations.
- Distinguishing what users said from what they did.
Inputs
- The question the research must answer.
- Access to actual users, not proxies.
- The decision this informs.
Outputs
- Findings grounded in observed behavior.
- Themes supported by multiple participants.
- Recommendations with a confidence level.
Workflow
- Ask what decision this informs — Research with no decision attached is a hobby. The decision determines who you talk to and what you ask.
- Ask about the past, not the future — "Tell me about the last time you needed to do X" produces evidence. "Would you use a feature that does X?" produces politeness. Nobody can predict their own behavior, and everybody says yes.
- Ask for the story, then dig — What happened, what did you do, what happened next, what did you do instead when it did not work. The workaround is the finding.
- In usability tests, give a task and shut up — "Buy a red one, size medium." Then do not speak. Every hint you give destroys the data you came for.
- Synthesize across participants, not within — One person's complaint is an anecdote. The same behavior in five of eight participants is a finding.
- Report behavior, and separate it from opinion — "Six of eight participants abandoned at the address form" is evidence. "Participants said the form was confusing" is a report of what they said.
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 · 144 lines · 35 tokens per session scan A 34b0d9d22bdd
user-research is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 24d ago), licensed MIT. It adds 35 tokens to every session and 1,501 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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