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 dashboard-from-researchgit 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/dashboard-from-research)<a href="https://agentmods.dev/skills/memi-design/design-skills/dashboard-from-research"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/dashboard-from-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/memi-design/design-skills/dashboard-from-research"><img src="https://agentmods.dev/badge/skills/memi-design/design-skills/dashboard-from-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 | $0.00022 | $0.01074 |
| Opus 5 | $0.00011 | $0.00537 |
| Sonnet 5 | $0.00004 | $0.00215 |
| Haiku 4.5 | $0.00002 | $0.00107 |
Grade A, and why
dashboard-from-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 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.
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dashboard from Research — Research Data to Interactive Dashboard
Transform research data (Excel, CSV, FigJam stickies, markdown) into structured insights and interactive dashboards with dataviz components. Chains research pipeline → specs → code generation → preview.
Freedom Level: High
Full autonomy over data interpretation, visualization choices, and dashboard layout. Must back every design decision with the research data.
When to Use
- User has research data (Excel, CSV, survey results, interview notes)
- FigJam board has stickies from workshops or brainstorming
- Need to create a dashboard that visualizes research findings
- Turning qualitative/quantitative data into actionable UI
Workflow
Step 1: Ingest Research Data
memi research from-file <path> → Excel/CSV parsing
memi research from-stickies → FigJam sticky notes
memi research synthesize → Combine all sources
Output: research/insights.json with structured findings.
Step 2: Analyze & Categorize
Classify insights into dashboard-friendly categories:
Quantitative → KPI cards, charts, trend lines
- Metrics: numeric values with labels
- Time series: data over time → line/area charts
- Comparisons: A vs B → bar charts
- Distributions: spread → histograms
Qualitative → Text summaries, tag clouds, quotes
- Themes: grouped findings → category cards
- Quotes: user verbatims → quote components
- Sentiment: positive/negative → sentiment indicators
Relational → Flow diagrams, matrices, maps
- User journeys: step sequences → flow components
- Relationships: connections → network graphs
- Hierarchies: nested structures → tree views
Step 3: Create Specs (Atomic Design)
For each visualization need, create the right spec type:
KPI metric → memi spec component MetricCard (molecule)
props: { title, value, change, trend, icon }
Trend chart → memi spec dataviz TrendChart
chartType: "area" | "line"
dataShape: { x: "date", y: "value", series: [...] }
Comparison → memi spec dataviz ComparisonChart
chartType: "bar"
dataShape: { category: "string", values: [...] }
The dashboard page → memi spec page ResearchDashboard
layout: "dashboard"
sections: [metrics-row, charts-row, insights-section, quotes]
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.
- 12d ago First seen · 122 lines · 22 tokens per session scan A fa8b73cb6c5e
dashboard-from-research is a skill published in the GitHub repository memi-design/design-skills (7 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,074 once invoked, about $0.0001 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 2026-08-31.
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