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 aroyburman-codes/pm-skills --skill user-research-synthesisgit clone --depth 1 https://github.com/aroyburman-codes/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/aroyburman-codes/pm-skills/user-research-synthesis)<a href="https://agentmods.dev/skills/aroyburman-codes/pm-skills/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/user-research-synthesis/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/aroyburman-codes/pm-skills/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/user-research-synthesis.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 | $0.00040 | $0.00816 |
| Opus 5 | $0.00020 | $0.00408 |
| Sonnet 5 | $0.00008 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
user-research-synthesis 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Research Synthesis Skill
Turn raw user research data (interviews, surveys, feedback, support tickets) into structured, actionable insights.
When to Use
- User has interview notes and needs to synthesize findings
- User has survey results to analyze
- User wants to identify patterns across user feedback
- User says
/user-research-synthesisfollowed by research data - Any time qualitative or quantitative user data needs structure
Framework: Research Synthesis (5 Steps)
Step 1: Organize Raw Data
- Source type: Interviews / Surveys / Support tickets / App reviews / Usage data
- Sample size: How many data points?
- User segments: Who was included? Any notable gaps?
- Timeframe: When was this data collected?
Step 2: Code & Theme
Identify recurring themes across the data:
| Theme | Frequency | Sentiment | Example Quote |
|---|---|---|---|
| [Theme 1] | X of Y participants | Positive/Negative/Mixed | "..." |
| [Theme 2] | X of Y participants | "..." |
Group themes into categories:
- Pain Points: What's frustrating or broken
- Unmet Needs: What users want but don't have
- Bright Spots: What's working well (don't break these)
- Surprises: Unexpected findings
Step 3: Prioritize Insights
For each insight, assess:
- Prevalence: How many users mentioned this? (1 = rare, 5 = universal)
- Severity: How painful is this? (1 = minor annoyance, 5 = deal-breaker)
- Actionability: Can we do something about this? (1 = hard, 5 = clear path)
Priority Score = Prevalence x Severity x Actionability
Step 4: Generate Recommendations
For the top 3-5 insights:
- Insight: Clear statement of what we learned
- Evidence: Supporting data points and quotes
- Implication: What this means for the product
- Recommendation: Specific next step (build, test, investigate further)
- Confidence: High / Medium / Low (based on data quality)
Step 5: Research Report
Executive Summary (2-3 sentences): What we studied, what we found, what we should do.
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 · 88 lines · 40 tokens per session scan A ded9d388c105
user-research-synthesis is a skill published in the GitHub repository aroyburman-codes/pm-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 40 tokens to every session and 816 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.
Other skills, from other repositories
prd-taskmaster
Zero-config goal-to-tasks engine (the Atlas engine). Takes any goal (software, pentest, business, learning), runs adaptive discovery via brainstorming, generates a validated spec, parses into TaskMaster tasks, and hands off to execution. Use when user says "PRD", "product requirements", "I want to build", invokes…
generate
Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholdersfound, grade thresholds), parses the PRD into tasks via task-master, runs TaskMaster's native complexity analysis…
discover
Phase 1 of the prd-taskmaster pipeline: brainstorm-driven discovery. Delegates to superpowers:brainstorming in Interactive Mode (one adaptive question at a time), or self-brainstorms in Autonomous Mode when no user is present. Intercepts before the brainstorming chain hands off to writing-plans — this skill owns the…
ai-shaped-readiness-advisor
Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.
agent-orchestration-advisor
Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring. Use when a complex PM task should run as parallel specialized agents instead of one linear process.
company-intel
Research a company, industry, or competitor set using web search and seven analytical lenses. Use when you need structured intel that feeds downstream PM skills.