pet-skill
Use this skill whenever the user wants to process PET neuroimaging data including spatial normalization to T1w/MNI space, SUVR computation, reference region quantification, partial volume correction, or tracer-specific workflows (PiB amyloid, FDG metabolism, tau). Triggers include: 'PET', 'PET processing', 'SUVR', 'amyloid PET', 'FDG PET', 'tau PET', 'PiB', 'flortaucipir', 'reference region', 'partial volume correction', or any request involving PET neuroimaging data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill pet-skill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PET Skill (Modality Layer) ## Overview `pet-skill` is the NeuroClaw **modality-layer** interface skill responsible for all PET neuroimaging data processing tasks. It strictly follows the NeuroClaw hierarchical design principles: - This skill **only describes WHAT needs to be done** and **which tool skill to delegate to**. - It contains **no implementation code or concrete commands**. - All concrete execution is delegated to existing base/tool skills: `fsl-tool`, `freesurfer-tool`, `nibabel-skill`, and `claw-shell`. - Companion scripts in `scripts/` provide reference implementations for SUVR computation and reference region extraction. **Core workflow (never bypassed):** 1. Identify input PET data and tracer type (PiB, FDG, tau, or other). 2. Ensure T1w structural data is available (via `smri-skill` if not yet processed). 3. Generate a **numbered execution plan** clearly stating WHAT needs to be done and which tool skill will handle each step. 4. Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed"). 5. On confirmation, delegate every step to the appropriate skill via `claw-shell`.
- Overview
- Quick Reference (Common PET Tasks)
- Tracer-Specific Reference Regions
- Core Processing Pipeline
- Stage 1: T1w Preprocessing (via smri-skill)
- Stage 2: PET-to-T1w Coregistration (via fsl-tool)
- Stage 3: Reference Region Definition
- Stage 4: SUVR Computation (via scripts/computesuvr.py)
- Stage 5 (Optional): Spatial Normalization to MNI
- Scripts
- scripts/computesuvr.py
- Standard Output Layout
- Installation (Handled by dependency-planner)
- Important Notes & Limitations
python skills/pet-skill/scripts/compute_suvr.py \
What does the pet-skill skill do?
Use this skill whenever the user wants to process PET neuroimaging data including spatial normalization to T1w/MNI space, SUVR computation, reference region quantification, partial volume correction, or tracer-specific workflows (PiB amyloid, FDG metabolism, tau). Triggers include: 'PET', 'PET processing', 'SUVR', 'amyloid PET', 'FDG PET', 'tau PET', 'PiB', 'flortaucipir', 'reference region', 'partial volume correction', or any request involving PET neuroimaging data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill pet-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.