Repository
NeuroAIHub/BrainPilot agent skills, page 2
Skills in NeuroAIHub/BrainPilot, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 49 | Expert guidance for designing EEG paradigms optimized to isolate specific ERP components, with domain-validated timing, trial count, and control condition parameters | NeuroAIHub/ | 1k | — | ~5k | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 50 | Domain-validated decision logic for selecting neuropsychological test batteries matched to suspected cognitive deficit profiles | NeuroAIHub/ | 1k | — | ~4k | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 51 | Domain-validated decision logic for optogenetic stimulation parameter selection, including opsin choice, light delivery, pulse protocols, fiber placement, and control conditions | NeuroAIHub/ | 1k | — | ~4.1k | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 52 | Core scientific methodology principles: research planning, method justification, assumption checking, and human-in-the-loop decision making for cognitive science and neuroscience | NeuroAIHub/ | 1k | — | ~4.5k | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 53 | Specifies display parameters, set sizes, target-distractor similarity, and randomization constraints for visual search experiments | NeuroAIHub/ | 1k | — | ~4.7k | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 54 | Audit scientific implementation, exported-model equivalence, dependency completeness, manifests, packaging, and isolated inference. | NeuroAIHub/ | 1k | — | ~477 | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 55 | Audit numeric, artifact, log, citation, and cross-report claims against inspectable evidence. | NeuroAIHub/ | 1k | — | ~474 | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 56 | Research a bounded factual, documentation, API, or literature question from authoritative sources and save a self-contained Markdown report with claim-level citations. | NeuroAIHub/ | 1k | — | ~503 | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 57 | Domain-validated guidance for building hierarchical Bayesian cognitive models with Stan/PyMC: prior specification, model structure, MCMC diagnostics, and posterior predictive checks | NeuroAIHub/ | 1k | — | ~5.6k | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 58 | Audit scientific data semantics, sample and label alignment, leakage, group splits, preprocessing boundaries, and train-to-inference transforms. | NeuroAIHub/ | 1k | — | ~433 | Automated safety check: Pass | AGPL-3.0 | 5 days ago |
| 59 | Create or update the canonical Markdown inventory of task-relevant research data. | NeuroAIHub/ | 1k | — | ~579 | Automated safety check: Pass | AGPL-3.0 | 5 days ago |