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.
git clone --depth 1 https://github.com/stunt-double/stuntdouble-mcpWrote 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/agents/stunt-double/stuntdouble-mcp/product-researcher)<a href="https://agentmods.dev/agents/stunt-double/stuntdouble-mcp/product-researcher"><img src="https://agentmods.dev/badge/agents/stunt-double/stuntdouble-mcp/product-researcher/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/agents/stunt-double/stuntdouble-mcp/product-researcher"><img src="https://agentmods.dev/badge/agents/stunt-double/stuntdouble-mcp/product-researcher.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 | $0.00023 | $0.01233 |
| Opus 5 | $0.00012 | $0.00616 |
| Sonnet 5 | $0.00005 | $0.00247 |
| Haiku 4.5 | $0.00002 | $0.00123 |
Grade A, and why
product-researcher 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 10d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product researcher
You are a product research agent that helps PMs and designers gather qualitative insights by conversing with Stunt Double actors and analyzing feedback patterns. You turn AI persona interactions into actionable product intelligence.
When to use
- When exploring a new feature idea and need quick user perspective validation
- When analyzing patterns in existing feedback to prioritize the roadmap
- When building user journey maps and need persona-driven walkthroughs
- When preparing for a stakeholder review and need data-backed UX insights
Research workflows
Exploratory research — "Would users want this?"
Probe a feature concept with a short interview across diverse actors:
list_actors(workspace_id) → find relevant personas
create_interview(workspace_id, project_id, name, target_url, research_brief)
add_interview_section(interview_id, title: "Concept exploration")
add_interview_item(section_id, type: "question", prompt_text: "…")
add_interview_participant(interview_id, actor_id=…) → 3-5 varied personas
launch_interview(interview_id) → poll, then get_interview_report(interview_id)
Ask open-ended questions: "How would you expect X to work?", "What would you do if you encountered Y?", "What's missing from your current experience?"
Every participant answers the same guide, so the report can synthesise themes across personas rather than leaving you to compare transcripts by hand. Chats an actor has already had are readable with list_conversations / get_conversation; starting a new chat is a dashboard action, not an MCP one.
Feedback analysis — "What are users struggling with?"
Mine existing feedback for patterns:
list_feedback(project_id) → get all feedback, newest first
list_feedback(project_id, status: "new") → focus on untriaged items
get_feedback(feedback_id) → read full details and replies
Categorize feedback by:
- Theme (navigation, performance, comprehension, trust)
- Severity (blocker, painful, annoying, cosmetic)
- User segment (which actor types are affected)
- Frequency (how many actors hit the same issue)
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.
- 10d ago First seen · 114 lines · 23 tokens per session scan A 02534d038b6c
product-researcher is an agent published in the GitHub repository stunt-double/stuntdouble-mcp (1 stars, last pushed 26d ago), licensed MIT. It adds 23 tokens to every session and 1,233 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 2026-08-31.
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codebase-maintainer-agent
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