name: ibl-analyze description: Use this skill for quantitative IBL analysis and scientific questions requiring metric semantics, QC, or statistical-unit guidance. metadata: version: "1.0.0"

IBL Analyze

Use this skill when

• The user asks an IBL scientific question requiring quantitative analysis.
• You need metric semantics, operator selection, QC, or statistical-unit guidance.

Required first step

For empirical analysis projects, apply the AGENTS.md scientific workflow. Before implementing or executing metric validation or downstream analysis, read references/interactive_scientific_analysis.md for its validation and feedback gates. For conceptual answers, consult the applicable scientific references without starting an analysis project.

References

• references/scientific_context_and_metric_semantics.md: semantic core for direct/operationalized/proxy metrics, row grain, event anchors, and stored-versus-recomputed decisions.
• references/scientific_caveats/README.md: caveat index for metric proposals, adversarial checks, and metric risk notes.
• references/operators.md: compact operator routing.
• references/brainbox_routing.md: Brainbox module routing before custom code.
• references/bwm_analysis_patterns.md: BWM analysis-pattern routing after BWM dataset schemas are inspected.
• references/reproducibility_qc.md: QC, statistics, exclusions, and interpretation checks.
• ../ibl-load/references/bwm_runtime_policy.md: BWM loading policy.
• ../ibl-load/references/brain_regions_qc.md: region mapping and QC.
• references/visual_latency.md: specialized latency recipe.

Default analysis policy

1. Define the scientific quantity, metric, row grain, event/window, QC scope, denominators, and independent statistical unit.
2. Classify each metric as direct, operationalized, or proxy.
3. Do not map natural-language terms directly onto convenient stored columns.
4. Use stored derived features only within their documented event/window/statistic/denominator definition.
5. Validate custom, ambiguous, proxy, event-aligned, trial-aligned, neural, behavioral, or state metrics with source-to-metric diagnostic plots before downstream analysis unless explicitly skipped.
6. Consult applicable caveats and choose at least one adversarial metric check when validating a metric.
7. Prefer local BWM tables and shards when their schema covers the question.
8. Use brainbox operators when they match the metric semantics; otherwise use question-specific NumPy/Pandas/SciPy code and state why.
9. Normalize region labels to one declared atlas mapping before grouping.
10. Use deterministic seeds for stochastic analysis.

Stored derived feature rule

When using a stored derived feature:

• load and report event name, window specification, statistic, sign convention, and denominator fields when available;
• state whether the feature measures peak, baseline, onset, mean, modulation, or another statistic;
• filter to responsive/analyzable units when the scientific question implies a response property rather than all units;
• do not overinterpret the feature beyond its documented definition.

BWM routing note

Read ../ibl-load/references/bwm_runtime_policy.md when selecting or loading BWM data, and references/bwm_analysis_patterns.md when designing or implementing BWM analyses. Conceptual BWM questions need only the applicable scientific references.

Anatomy routing note

For questions involving cortical depth, laminar position, atlas slices, flatmaps, or CCF coordinate lookups, read ../ibl-anatomy/references/atlas_navigation.md. Key entry points: xyz_to_depth for continuous cortical depth from CCF coords; FlatMap + plot_scalar_on_flatmap for dorsal-cortex flatmaps; plot_points_on_slice for probe/unit visualisation on atlas slices.