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 FlowRegSuite/flowreg-agent-skills --skill flowreg-benchmark-reproducegit clone --depth 1 https://github.com/FlowRegSuite/flowreg-agent-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/flowregsuite/flowreg-agent-skills/flowreg-benchmark-reproduce)<a href="https://agentmods.dev/skills/flowregsuite/flowreg-agent-skills/flowreg-benchmark-reproduce"><img src="https://agentmods.dev/badge/skills/flowregsuite/flowreg-agent-skills/flowreg-benchmark-reproduce/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/flowregsuite/flowreg-agent-skills/flowreg-benchmark-reproduce"><img src="https://agentmods.dev/badge/skills/flowregsuite/flowreg-agent-skills/flowreg-benchmark-reproduce.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 | $0.00108 | $0.02969 |
| Opus 5 | $0.00054 | $0.01484 |
| Sonnet 5 | $0.00022 | $0.00594 |
| Haiku 4.5 | $0.00011 | $0.00297 |
Grade A, and why
flowreg-benchmark-reproduce scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- It reports neither the 95th-percentile error, the `curl` column, nor the sign guard. How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark reproduction and audit
1. Purpose
Select the right benchmark parameter profile, resolve every value to its source revision, and run
the benchmark using pyflowreg's own implementation — then state honestly whether the result is an
exact reproduction or an approximation.
The failure this skill exists to prevent is a plausible benchmark table whose parameters came from a paper's prose, a stale comment, or a different revision than the one claimed.
This skill selects parameters and records provenance. It does not ship its own benchmark
implementation: the displacement field, the noise conditions and the end-point-error metric belong
to pyflowreg and its benchmark repository.
2. Use this skill when
- A benchmark number must be regenerated or independently checked.
- Someone asks which parameters produced a published figure.
- A comparison against NoRMCorre needs its exact rigid or non-rigid settings.
- A reviewer asks whether a result is reproducible, and the answer must be defensible.
3. Do not use this skill when
- You are correcting real experimental data →
flowreg-plan, then the execution skill. - You are assessing a finished correction run →
flowreg-qc-and-audit. - You want new parameters tuned for your own data. Benchmark presets are pinned historical
artifacts, not recommendations;
flowreg-planowns parameter selection.
4. Required inputs
- The condition to reproduce: synthetic (
clean,noisy35db,noisy30db) with one or two channels, or a MATLAB reproduction script at 6.2 Hz or 30.9 Hz. - A writable run directory outside any source checkout.
- For the synthetic benchmark: an installed
pyflowreg(section 6, step 4). - For MATLAB profiles: a MATLAB installation. This skill never executes MATLAB; it extracts parameters statically and reports them.
5. Preflight checks
- Identify the condition before opening the catalog. Choosing a profile first and then deciding what it reproduces is how classes get mixed.
- Confirm the runtime:
flowreg-agent-skills doctor --jsonreports whetherpyflowregis importable and at which version. - Verify the dataset hash against the catalog. A mismatch downgrades the run to
approximateat best, and usually means a different file. - Confirm the coordinate and component conventions (section 7) before computing any metric.
- Decide up front what would make this run approximate. Discovering it afterwards is how approximations get published as exact.
What ships with it
6 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.
- 11d ago First seen · 232 lines · 108 tokens per session scan A 6de480f63b2d
flowreg-benchmark-reproduce is a skill published in the GitHub repository FlowRegSuite/flowreg-agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 108 tokens to every session and 2,969 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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