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 flonat/flonat-research --skill synthetic-datagit clone --depth 1 https://github.com/flonat/flonat-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/flonat/flonat-research/synthetic-data)<a href="https://agentmods.dev/skills/flonat/flonat-research/synthetic-data"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/synthetic-data/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/flonat/flonat-research/synthetic-data"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/synthetic-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.02285 |
| Opus 5.5 | $0.00019 | $0.00914 |
| Sonnet 5.5 | $0.00010 | $0.00457 |
| Haiku 4.5 | $0.00005 | $0.00229 |
Grade A, and why
synthetic-data 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthetic Data Generation
Generate structurally realistic synthetic datasets for pilot testing, power analysis, and method development.
Modes
| Mode | What it produces | Entry point |
|---|---|---|
| From design | Synthetic data matching an existing experiment design document | "Generate test data for my experiment" |
| From schema | Synthetic data from a user-described structure | "Create a dataset with these variables" |
| Calibrated | Synthetic data calibrated to published summary statistics | "Make fake data matching these descriptives" |
Default: From schema. If an experiment design document exists in docs/, auto-select From design. If user provides published statistics, auto-select Calibrated.
When to Use
- Testing analysis code before real data collection
- Power analysis via simulation (complements
experiment-designPower mode) - Method development and debugging estimation pipelines
- Generating pilot data for grant proposals or ethics applications
- Teaching demonstrations with realistic-looking data
When NOT to Use
- Designing the experiment itself -->
experiment-design - Running analysis on real data -->
data-analysis - Auditing identification strategy -->
causal-design
Workflow
Step 1: Detect Mode
Detect from context or ask:
| Signal | Mode |
|---|---|
docs/experiment-design.md exists |
From design |
| User describes variables, types, relationships | From schema |
| User provides means, SDs, correlations from a paper | Calibrated |
| Ambiguous | Ask |
Step 2: Interview for Data Structure
Gather the following (adapt questions to mode):
| Parameter | Question | Default |
|---|---|---|
| Variables | What variables do you need? | — |
| Types | Continuous, binary, ordinal, categorical? | Infer from name |
| Sample size | How many observations? | 500 |
| Treatment | Is there a treatment variable? How many arms? | — |
| Effect size | Expected treatment effect (Cohen's d, OR, etc.)? | 0.3 (small-medium) |
| Correlations | Which variables should be correlated? How strongly? | — |
| Clustering | Are observations nested (e.g., students in classrooms)? | No |
| Panel structure | Multiple time periods? How many? | Cross-section |
| Missing data | Should the data include realistic missingness? | No |
| Language | R or Python? | Detect from project or ask |
What ships with it
5 files 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 Changed 2226588aca93
- 1mo ago First seen · 248 lines · 48 tokens per session scan A 03e1730714d3
synthetic-data is a skill published in the GitHub repository flonat/flonat-research (147 stars, last pushed 28d ago), licensed MIT. It adds 48 tokens to every session and 2,285 once invoked, about $0.0002 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 2026-09-03.
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