meg-skill
Use this skill whenever the user wants to process MEG (magnetoencephalography) data including source localization, time-frequency analysis, connectivity analysis, sensor-level preprocessing, or MEG-specific feature extraction. Triggers include: 'MEG', 'MEG processing', 'MEG source localization', 'MEG connectivity', 'magnetoencephalography', 'beamformer', 'time-frequency', 'MEG preprocessing', or any request involving MEG data files (.fif, .con, .ds).
npx skills add BioTender-max/awesome-bio-agent-skills --skill meg-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.
# MEG Skill (Modality Layer) ## Overview `meg-skill` is the NeuroClaw **modality-layer** interface skill responsible for all MEG (magnetoencephalography) 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 MNE-Python (via `claw-shell`) and companion scripts. - Companion scripts in `scripts/` provide reference implementations for time-frequency analysis and source localization. **Core workflow (never bypassed):** 1. Identify input MEG data format (.fif Elekta/Neuromag, .ds CTF, .con KIT/Yokogawa). 2. Ensure T1w structural MRI is available for source localization (via `smri-skill` if not yet processed). 3. Generate a **numbered execution plan** clearly stating WHAT needs to be done. 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 via `claw-shell`. 6. After execution, save all outputs in a clean directory
- Overview
- Quick Reference (Common MEG Tasks)
- Supported MEG File Formats
- Core Processing Pipeline
- Stage 1: Data Loading & Validation
- Stage 2: Preprocessing
- Stage 3: Artifact Removal (ICA)
- Stage 4: Epoching & Averaging
- Stage 5: Time-Frequency Analysis (via scripts/timefrequency.py)
- Stage 6 (Optional): Source Localization
- Scripts
- scripts/timefrequency.py
- Standard Output Layout
- Installation (Handled by dependency-planner)
python skills/meg-skill/scripts/time_frequency.py \
What does the meg-skill skill do?
Use this skill whenever the user wants to process MEG (magnetoencephalography) data including source localization, time-frequency analysis, connectivity analysis, sensor-level preprocessing, or MEG-specific feature extraction. Triggers include: 'MEG', 'MEG processing', 'MEG source localization', 'MEG connectivity', 'magnetoencephalography', 'beamformer', 'time-frequency', 'MEG preprocessing', or any request involving MEG data files (.fif, .con, .ds).
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill meg-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.