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 exiao/pm-skills --skill synthetic-userstudiesgit clone --depth 1 https://github.com/exiao/pm-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/exiao/pm-skills/synthetic-userstudies)<a href="https://agentmods.dev/skills/exiao/pm-skills/synthetic-userstudies"><img src="https://agentmods.dev/badge/skills/exiao/pm-skills/synthetic-userstudies/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/exiao/pm-skills/synthetic-userstudies"><img src="https://agentmods.dev/badge/skills/exiao/pm-skills/synthetic-userstudies.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 | $0.00117 | $0.04054 |
| Opus 5 | $0.00059 | $0.02027 |
| Sonnet 5 | $0.00023 | $0.00811 |
| Haiku 4.5 | $0.00012 | $0.00405 |
Grade A, and why
synthetic-userstudies 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthetic UX Research
Run user research sessions natively. No backend calls. The agent plays the persona, generates characters, and runs interviews using the same prompts as userstudies.ai.
Session Flow
1. Setup Phase
Collect the 4 Ps. Ask for any that are missing:
| Field | Description |
|---|---|
| Persona | Short description of target user (e.g. "Primary care doctor, US, recently graduated") |
| Problem | What they're struggling with — in their words |
| Promise | Value prop in <7 words (e.g. "Single-serve coffee") |
| Product | Key features / what you're building |
If any field is blank or weak, offer to autofill it. See Autofill section below.
Once all 4 Ps are set, confirm them and note the research phase (default: Right Problems — problem discovery). Load principles.md for the phase framework. Then move to character generation.
2. Character Generation
Generate a Character from the Persona description. Follow the schema in schema.md. Output the character as a JSON block so it can be referenced later.
Example prompt to yourself: "Create the most realistic character possible for: [persona]"
3. Interview Mode
Once the character is generated, enter interview mode. You are now the character.
Load prompts.md and follow USER_RESEARCH_PARTICIPANT_PROMPT exactly.
Rules while in character:
- Respond as the character would via SMS: casual, personal, specific
- Use contractions, abbreviations, light emotion — vary length (few words to ~100)
- Share specific stories with sequencing, feelings, and decisions — what they did, not what they think
- If the topic isn't relevant to the character, say so in character
- Never break character unless the researcher explicitly steps out (see below)
After every response, append a --- separator and list 3 suggested follow-up questions the researcher could ask. Use the suggested_questions format from schema.md.
What ships with it
7 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.
- 12d ago First seen · 151 lines · 117 tokens per session scan A d00b8451638c
synthetic-userstudies is a skill published in the GitHub repository exiao/pm-skills (9 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 4,054 once invoked, about $0.0006 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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