Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sun2443/designer-skillsnpx agentmods add skills/sun2443/designer-skills/synthesize-researchWrote 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/sun2443/designer-skills/synthesize-research)<a href="https://agentmods.dev/skills/sun2443/designer-skills/synthesize-research"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/synthesize-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/sun2443/designer-skills/synthesize-research"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/synthesize-research.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 | $0.00063 | $0.03272 |
| Opus 5.5 | $0.00025 | $0.01309 |
| Sonnet 5.5 | $0.00013 | $0.00654 |
| Haiku 4.5 | $0.00006 | $0.00327 |
Grade A, and why
synthesize-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 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.
This is a copy
100% identical to synthesize-research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthesize Research
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Synthesize user research from multiple sources into structured insights and recommendations.
Usage
/synthesize-research $ARGUMENTS
Workflow
1. Gather Research Inputs
Accept research from any combination of:
- Pasted text: Interview notes, transcripts, survey responses, feedback
- Uploaded files: Research documents, spreadsheets, recordings summaries
- ~~knowledge base (if connected): Search for research documents, interview notes, survey results
- ~~user feedback (if connected): Pull recent support tickets, feature requests, bug reports
- ~~product analytics (if connected): Pull usage data, funnel metrics, behavioral data
- ~~meeting transcription (if connected): Pull interview recordings, meeting summaries, and discussion notes
Ask the user what they have:
- What type of research? (interviews, surveys, usability tests, analytics, support tickets, sales call notes)
- How many sources / participants?
- Is there a specific question or hypothesis they are investigating?
- What decisions will this research inform?
2. Process the Research
For each source, extract:
- Key observations: What did users say, do, or experience?
- Quotes: Verbatim quotes that illustrate important points
- Behaviors: What users actually did (vs what they said they do)
- Pain points: Frustrations, workarounds, and unmet needs
- Positive signals: What works well, moments of delight
- Context: User segment, use case, experience level
3. Identify Themes and Patterns
Apply thematic analysis — see Research Synthesis Methodology below for detailed guidance on thematic analysis, affinity mapping, and triangulation techniques.
Group observations into themes, count frequency across participants, and assess impact severity. Note contradictions and surprises.
Create a priority matrix:
- High frequency + High impact: Top priority findings
- Low frequency + High impact: Important for specific segments
- High frequency + Low impact: Quality-of-life improvements
- Low frequency + Low impact: Note but deprioritize
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 · 313 lines · 63 tokens per session scan A ae98c1110254
synthesize-research is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 3,272 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to synthesize-research, differing in 0 lines, and is treated as a copy.
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