kdense-zarr-python

Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

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Repository: eightmm/codex-science
Last source activity: July 10, 2026 at 15:27
Detected SKILL.md language: English
Stars: 2
Forks: 0

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npx skills add https://github.com/eightmm/codex-science --skill kdense-zarr-python

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SKILL.md

Source instructions · Read-only preview

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quantitative-research

Execute auditable quantitative research across preregistered study design, statistical inference, numerical verification, dimensional analysis, uncertainty propagation, counterexample search, and formal proof. Use when a scientific or mathematical question needs actual computation and machine-readable evidence rather than only explanation.

2026-08-04

statistical-decision-analysis

Compare finite actions under uncertainty with explicit states, loss, utility or risk-neutral payoff, priors, experiment likelihoods, and a declared decision criterion. Use for Bayes risk, expected utility or payoff, pure-action minimax, minimax regret, dominance, posterior decision policies, EVPI/EVSI, experiment-cost decisions, and prior-threshold sensitivity; do not use for ordinary statistical testing, causal identification, strategic games, or MDP/POMDP/RL planning.

2026-08-04

cx-statistical-decision-analysis

2026-08-04

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Kdense Zarr Python

Use this Codex adapter for the pinned upstream scientific skill.

Catalog status

Status: active

Source: kdense

Audit reasons: none

Upstream license: MIT license

Inventory: ../../inventory.json

Upstream instructions: ../../../vendor/scientific-agent-skills/skills/zarr-python/SKILL.md

Procedure

Resolve the plugin root as three directories above this skill directory.

Read the matching inventory record and verify its status, reasons, source path, and pinned revision.

Read the upstream instructions only after confirming this record is still active in the current inventory.

Treat upstream frontmatter as source metadata, not as Codex configuration. Follow the instruction body only where it is compatible with available Codex tools and the current user-approved plan.

Treat tools named for another agent runtime as capability requests. Map them to an available Codex tool only when the behavior is equivalent; otherwise report the unavailable capability.

Ask before credentials, package installation, new network hosts, remote compute, writes, paid services, destructive actions, or imported executable code.

Use $science-provenance for produced scientific outputs and $science-review before presenting scientific claims.

Progressive loading

The upstream SKILL.md is 779 lines. Inspect its headings first, then read only the sections relevant to the current request. Load linked references only when the selected workflow requires them; do not load the entire upstream tree by default.

Boundaries

This wrapper does not grant credentials, network access, package installation, or script execution.

Do not change an inactive record to active from inside a research task.

Preserve failures, uncertainty, source identifiers, and licensing constraints.

name	kdense-zarr-python
description	Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.