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-register-2dgit 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-register-2d)<a href="https://agentmods.dev/skills/flowregsuite/flowreg-agent-skills/flowreg-register-2d"><img src="https://agentmods.dev/badge/skills/flowregsuite/flowreg-agent-skills/flowreg-register-2d/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-register-2d"><img src="https://agentmods.dev/badge/skills/flowregsuite/flowreg-agent-skills/flowreg-register-2d.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 | $0.00085 | $0.01853 |
| Opus 5 | $0.00043 | $0.00927 |
| Sonnet 5 | $0.00017 | $0.00371 |
| Haiku 4.5 | $0.00009 | $0.00185 |
Grade A, and why
flowreg-register-2d 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 9d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dense 2D motion correction
1. Purpose
Correct in-plane motion in one logical two-dimensional time series, and keep the dense displacement field as a scientific output rather than a disposable intermediate.
2. Use this skill when
- One recording (possibly split across files that are chunks of the same acquisition) shows x-y motion.
- The data are single- or multichannel, with or without a stable structural channel.
- The region of interest is narrow (a dendrite, a vessel) and needs careful pyramid handling.
- Large bulk drift suggests rigid pre-alignment before the variational stage.
3. Do not use this skill when
- Several recordings must end up in a shared coordinate system →
flowreg-register-session. - The imaging plane is drifting axially through a reference volume →
flowreg-correct-z-drift. - The moving data are volumes →
flowreg-register-3d. - The run has already finished and needs assessment →
flowreg-qc-and-audit.
4. Required inputs and metadata
- Input path or in-memory array, and its explicit axis order. Frames are
(T, H, W, C). - Channel count and what each channel is. Structural and functional channels behave differently.
- Pixel spacing and frame rate, for interpretation and for the manifest.
- A writable new run directory, outside the source data directory.
- Free disk at least the estimated output size, with margin.
There is no command-line interface for 2D correction. This capability is Python-API-only. Any command purporting to run 2D compensation from a shell is fabricated.
5. Preflight checks
- Read
references/runtime-capability.mdfor verified entry points and the exact output names. - Import the runtime and record its version into the manifest. Do not install anything.
- Confirm the axis order from metadata or from the user — never from rank.
- Confirm the output directory exists, is writable, and is not inside the source directory.
- If a GPU backend was requested, confirm the GPU exists and the runtime can use it. Do not fall back silently in either direction.
What ships with it
3 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.
- 9d ago First seen · 153 lines · 85 tokens per session scan A 60a24c5c6ea0
flowreg-register-2d is a skill published in the GitHub repository FlowRegSuite/flowreg-agent-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 1,853 once invoked, about $0.0004 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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