Borrowing it
Nothing to install: this file belongs to NikitaDmitrieff/auto-co-meta. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NikitaDmitrieff/auto-co-meta/main/.claude/skills/user-research-synthesis/SKILL.mdgit clone --depth 1 https://github.com/NikitaDmitrieff/auto-co-metaWrote 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/nikitadmitrieff/auto-co-meta/user-research-synthesis)<a href="https://agentmods.dev/skills/nikitadmitrieff/auto-co-meta/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/nikitadmitrieff/auto-co-meta/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/nikitadmitrieff/auto-co-meta/user-research-synthesis"><img src="https://agentmods.dev/badge/skills/nikitadmitrieff/auto-co-meta/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 | $0.00046 | $0.02249 |
| Opus 5 | $0.00023 | $0.01125 |
| Sonnet 5 | $0.00009 | $0.00450 |
| Haiku 4.5 | $0.00005 | $0.00225 |
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 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- user-research-synthesis — 100% identical, 0 lines differ
- user-research-synthesis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Research Synthesis Skill
You are an expert at synthesizing user research — turning raw qualitative and quantitative data into structured insights that drive product decisions. You help product managers make sense of interviews, surveys, usability tests, support data, and behavioral analytics.
Research Synthesis Methodology
Thematic Analysis
The core method for synthesizing qualitative research:
- Familiarization: Read through all the data. Get a feel for the overall landscape before coding anything.
- Initial coding: Go through the data systematically. Tag each observation, quote, or data point with descriptive codes. Be generous with codes — it is easier to merge than to split later.
- Theme development: Group related codes into candidate themes. A theme captures something important about the data in relation to the research question.
- Theme review: Check themes against the data. Does each theme have sufficient evidence? Are themes distinct from each other? Do they tell a coherent story?
- Theme refinement: Define and name each theme clearly. Write a 1-2 sentence description of what each theme captures.
- Report: Write up the themes as findings with supporting evidence.
Affinity Mapping
A collaborative method for grouping observations:
- Capture observations: Write each distinct observation, quote, or data point as a separate note
- Cluster: Group related notes together based on similarity. Do not pre-define categories — let them emerge from the data.
- Label clusters: Give each cluster a descriptive name that captures the common thread
- Organize clusters: Arrange clusters into higher-level groups if patterns emerge
- Identify themes: The clusters and their relationships reveal the key themes
Tips for affinity mapping:
- One observation per note. Do not combine multiple insights.
- Move notes between clusters freely. The first grouping is rarely the best.
- If a cluster gets too large, it probably contains multiple themes. Split it.
- Outliers are interesting. Do not force every observation into a cluster.
- The process of grouping is as valuable as the output. It builds shared understanding.
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 · 197 lines · 46 tokens per session scan A 968a08f0bf67
user-research-synthesis is a skill published in the GitHub repository NikitaDmitrieff/auto-co-meta (43 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 2,249 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
browser-automation
Playwright-based browser automation patterns for autonomous web interaction.
dws
A command-line guide for DingTalk, a workplace platform with chat, documents, calendars, approvals, tasks, and other business tools.
package-author
A packaging guide for turning scripts, skills, or an MCP service into a standard plugin package. MCP is a way for an AI assistant to connect to external tools.
clip-hand-skill
Expert knowledge for AI video clipping — yt-dlp downloading, whisper transcription, SRT generation, and ffmpeg processing.
twitter-hand-skill
Expert knowledge for AI Twitter/X management — API v2 reference, content strategy, engagement playbook, safety, and performance tracking.
slack-tools
Slack workspace management and automation specialist.