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.
git 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/commands/flonat/flonat-research/synthetic-data)<a href="https://agentmods.dev/commands/flonat/flonat-research/synthetic-data"><img src="https://agentmods.dev/badge/commands/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/commands/flonat/flonat-research/synthetic-data"><img src="https://agentmods.dev/badge/commands/flonat/flonat-research/synthetic-data.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 | $0.00044 | $0.00095 |
| Opus 5 | $0.00022 | $0.00048 |
| Sonnet 5 | $0.00009 | $0.00019 |
| Haiku 4.5 | $0.00004 | $0.00010 |
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 6d 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.
What it actually says
Shared skill adapter
Read and follow ~/.claude/shared-skills/synthetic-data/SKILL.md. Resolve bundled resources relative to ~/.claude/shared-skills/synthetic-data/.
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.
- 6d ago First seen · 11 lines · 44 tokens per session scan A ca067cf11edc
synthetic-data is a command published in the GitHub repository flonat/flonat-research (132 stars, last pushed 15d ago), licensed MIT. It adds 44 tokens to every session and 95 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 2026-09-03.
Other commands, from other repositories
generate-widget
Génération Widget Flutter avec Tests.
ia-verify
Run pre-PR verification chain -- build, types, lint, tests, security scan, diff review.
eg-fix-bug
Fix a bug using the elephant/goldfish workflow — problem doc, goldfish diagnosis check, failing test, fix, review, validate.
SIMULATION_EXAMPLES
Inspired by "AI agents at their most under-leveraged point" by AI News & Strategy Daily | Nate B. Jones Source: https://www.youtube.com/watch?v=duA2AwL7keg.
qual-characterize-patterns
Develop dimensional analysis of patterns through systematic characterization.
checklist
Generate a custom checklist for the current feature based on user requirements.