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 memi-design/design-skills --skill data-synthesisgit clone --depth 1 https://github.com/memi-design/design-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/memi-design/design-skills/data-synthesis)<a href="https://agentmods.dev/skills/memi-design/design-skills/data-synthesis"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/data-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/memi-design/design-skills/data-synthesis"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/data-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 | $0.00029 | $0.02531 |
| Opus 5.5 | $0.00012 | $0.01012 |
| Sonnet 5.5 | $0.00006 | $0.00506 |
| Haiku 4.5 | $0.00003 | $0.00253 |
Grade A, and why
data-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 1mo 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Synthesis -- Research Intelligence
Skill pack for transforming raw research data into structured insights. Covers affinity mapping, thematic analysis, quantitative aggregation, triangulation, insight scoring, gap analysis, and integration with the Memoire research pipeline.
Core Loop
COLLECT RAW DATA --> ORGANIZE --> CODE --> THEME --> TRIANGULATE --> SCORE --> OUTPUT INSIGHTS
Every synthesis pass must produce actionable insights, not summaries. An insight is a non-obvious finding that changes a design decision.
Affinity Mapping Methodology
What It Is
A bottom-up clustering technique. Individual observations (sticky notes, quotes, data points) are grouped by natural similarity until themes emerge.
Process
| Step | Action | Output |
|---|---|---|
| 1. Harvest | Extract individual observations from raw data. One fact per unit. | 50-300 discrete data points |
| 2. Spread | Lay all data points out without grouping. Randomize order. | Ungrouped pool |
| 3. Cluster | Silently group related points. Move items that feel similar together. | 8-20 raw clusters |
| 4. Label | Name each cluster with a descriptive header that captures the shared meaning. | Named clusters |
| 5. Supergroup | Group clusters into 3-6 high-level themes. | Theme hierarchy |
| 6. Prioritize | Rank themes by frequency, severity, and opportunity. | Prioritized theme map |
Rules
- One observation per data point -- never combine two findings into one note
- Group by meaning, not by source (do not cluster "all interview 3 data" together)
- If a cluster has more than 15 items, split it
- If a cluster has fewer than 3 items, consider merging or flagging as an outlier
- Relabel clusters after regrouping -- initial labels often drift from final content
In Memoire
memi research from-stickies # Pull sticky notes from Figma canvas
memi research synthesize # Run affinity mapping + thematic analysis
memi research report # Generate structured insight report
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 285 lines · 29 tokens per session scan A 6780b173a26e
data-synthesis is a skill published in the GitHub repository memi-design/design-skills (7 stars, last pushed 27d ago), licensed MIT. It adds 29 tokens to every session and 2,531 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
accessibility-per-component
Audit one design system component's accessibility against WCAG 2.2 AA: keyboard, screen reader, contrast, focus, ARIA, target size. Trigger: a11y audit, is this accessible, WCAG check, screen reader support, keyboard navigation check. Not for page- or product-wide audits.
figma-variable-audit
Audit Figma variable collections: tier mapping, naming, alias chains, modes, orphans; can fix in place with Figma Console MCP. Triggers: audit my Figma variables, review variable collections, Figma variable health. For token files in code use token-audit.
theme-audit
Audit theme parity: tokens missing or unchanged per theme, component tokens bypassing semantics, contrast within each theme, resolver contexts, theme-switch regressions. Triggers: dark mode audit, theme coverage, brand variant parity. For general token structure use token-audit.
token-audit
Audit how design tokens are defined: tiers, naming, alias chains, raw values, orphans, DTCG readiness. Triggers: audit my tokens, token architecture review, token health check. Not for code consuming tokens (token-compliance), theme parity (theme-audit) or Figma variables (figma-variable-audit).
ai-component-description
Write a six-section prose description (purpose, props, anti-patterns, composition, accessibility, examples) for a Figma component's description field so LLMs read it via MCP. Triggers: describe this component for AI, Figma MCP description. JSON metadata files: use metadata-schema-generator.
cicd-integration
Generate CI pipeline files and check scripts (GitHub Actions, GitLab, CircleCI, Bitbucket) that automate design system checks: token validation, hardcoded values, a11y scans, visual regression, bundle size. Trigger: set up CI, quality gates, automate these checks. Not for running an audit.