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 shawnpang/startup-founder-skills --skill user-research-synthesisgit clone --depth 1 https://github.com/shawnpang/startup-founder-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/shawnpang/startup-founder-skills/user-research-synthesis)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-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/shawnpang/startup-founder-skills/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-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 | $0.00032 | $0.01560 |
| Opus 5 | $0.00016 | $0.00780 |
| Sonnet 5 | $0.00006 | $0.00312 |
| Haiku 4.5 | $0.00003 | $0.00156 |
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 13d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- user-research-synthesis — 100% identical, 0 lines differ
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 Synthesis
When to Use
Activate when a founder or PM provides raw qualitative research data and needs it synthesized into structured insights. This includes customer interview transcripts, survey open-ended responses, support ticket logs, NPS verbatims, sales call notes, app store reviews, or community forum posts. Trigger phrases include "summarize these interviews," "what are customers telling us," "synthesize this feedback," or "help me analyze these customer conversations."
Context Required
- From startup-context: product stage, current customer segments, known hypotheses being tested, existing personas (if any).
- From the user: the raw data sources (transcripts, notes, recordings), research questions being investigated, participant background information, any specific hypotheses to validate or invalidate.
Workflow
- Read the complete transcript -- Before summarizing, read the entire transcript or data source end-to-end. Do not begin summarizing until you have processed all material. This prevents recency bias and ensures nothing is missed.
- Capture metadata -- Record interview date, participants, participant background, and context for the conversation.
- Identify current solutions -- Document what solutions the participant currently uses and their satisfaction level with each. This reveals the competitive landscape from the user's perspective.
- Extract problems and pain points -- Catalog every problem mentioned, using the participant's own language. Separate symptoms from root causes.
- Apply Jobs to Be Done framing -- For each major finding, frame it as a JTBD: "When [situation], I want to [motivation], so I can [expected outcome]." This shifts focus from features to outcomes.
- Flag unexpected discoveries -- Note any surprising insights, contradictions, or findings that challenge existing assumptions. These often hold the most strategic value.
- Define follow-up actions -- List specific next steps with ownership: who should do what based on these findings.
- Assess confidence levels -- Rate each insight as high/medium/low confidence based on data volume and consistency across sources.
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.
- 13d ago First seen · 95 lines · 32 tokens per session scan A 49719b232c4c
user-research-synthesis is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 1,560 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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