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Use when you have built multiple Docker image variants (e.g., cli, dev, linux, windows) from a multi-stage Dockerfile and need to verify that their uncompressed and compressed storage footprints fall within documented acceptable ranges (e.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | artifact-size-measurement-and-range-verification |
| description | Use when you have built multiple Docker image variants (e.g., cli, dev, linux, windows) from a multi-stage Dockerfile and need to verify that their uncompressed and compressed storage footprints fall within documented acceptable ranges (e. |
| license | CC-BY-4.0 |
| metadata | {"edam_topics":[],"tools":["AirdPro V5","Docker (docker build, docker inspect, docker system df)","Dockerfile (multi-stage build with --target flag)"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1186/s12859-021-04490-0","title":"aird"}] |
| evidence_spans | ["AirdPro V5 is now available at 2023.7"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_aird","doi":"10.1186/s12859-021-04490-0","title":"aird"}],"dedup_kept_from":"coll_aird"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1186/s12859-021-04490-0","all_source_dois":["10.1186/s12859-021-04490-0"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Measure uncompressed and compressed sizes of built Docker image artifacts, compare against documented acceptable ranges, and generate a structured pass/fail verification report. This skill ensures Docker image variants meet storage and distribution requirements before deployment.
You have built multiple Docker image variants (e.g., cli, dev, linux, windows) from a multi-stage Dockerfile and need to verify that their uncompressed and compressed storage footprints fall within documented acceptable ranges (e.g., cli 6–7 GB, dev 9–11 GB, linux 8–10 GB, windows 4–5 GB) before release or deployment.
Execute multi-stage Docker builds for each image variant using the --target flag (e.g., docker build --target runtime-cli). Query uncompressed image size in bytes using docker inspect --format='{{.Size}}' for each built image. Retrieve compressed size using docker system df or Docker API calls to measure actual storage footprint on disk. Convert both byte measurements to gigabytes. Compare each measured value (uncompressed and compressed) against the documented range for that variant. Generate a structured report listing image name, measured size (GB), documented range (GB), and pass/fail status. Pass criterion: measured size must fall within or below the documented range for storage compliance.
docker inspect.docker inspect --format='{{.Size}}' are non-zero and in bytes.docker system df are retrieved for each variant and are smaller than or equal to uncompressed sizes.docker inspect reflects the extracted filesystem size; this may not account for layer-level decompression overhead or intermediate build artifacts.docker build --target runtime-cli, docker build --target runtime-dev, docker build --target runtime-linux, and docker build --target runtime-windows to produce all four image variants.: "Execute the multi-stage Docker build process via docker build --target runtime-cli, docker build --target runtime-dev, docker build --target runtime-linux, and `docker build --target"docker inspect --format='{{.Size}}' to retrieve the uncompressed image size in bytes.: "Query each built image using docker inspect --format='{{.Size}}' to retrieve the uncompressed image size in bytes."docker system df or equivalent Docker API calls to determine the actual storage footprint.: "Query the compressed size of each image stored on disk using docker system df or equivalent Docker API calls to determine the actual storage footprint."