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Use when you have access to the source code of a webservice component (Python, configuration files, route definitions) and need to produce machine-readable API documentation (OpenAPI 3.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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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 | api-specification-extraction |
| description | Use when you have access to the source code of a webservice component (Python, configuration files, route definitions) and need to produce machine-readable API documentation (OpenAPI 3. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3763","edam_topics":["http://edamontology.org/topic_3361","http://edamontology.org/topic_0769"],"tools":["MAGMa","joblauncher"],"license_tier":"open"} |
| derived_from | [{"doi":"10.5702/massspectrometry.S0033","title":"magma"}] |
| evidence_spans | ["MAGMa is a abbreviation for 'Ms Annotation based on in silico Generated Metabolites'.","MAGMa is a abbreviation for 'Ms Annotation based on in silico Generated Metabolites'"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_magma","doi":"10.5702/massspectrometry.S0033","title":"magma"}],"dedup_kept_from":"coll_magma"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.5702/massspectrometry.S0033","all_source_dois":["10.5702/massspectrometry.S0033"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Extract HTTP endpoint definitions, request/response schemas, and authentication mechanisms from webservice source code and map them into a formal API specification (OpenAPI 3.0). This skill is essential when integrating or documenting webservice components whose interfaces are defined in code rather than published specifications.
You have access to the source code of a webservice component (Python, configuration files, route definitions) and need to produce machine-readable API documentation (OpenAPI 3.0 format) that captures all HTTP endpoints, methods, URL patterns, parameter names, payload schemas, and authentication details. This is triggered when the webservice lacks published API documentation but serves as a dependency for other components (e.g., joblauncher webservice in MAGMa is called by the web application to trigger job calculations).
Clone the webservice repository and locate source files defining HTTP routes, endpoints, and request/response handlers (e.g., Python route definitions and function signatures). Systematically examine each endpoint to extract: (1) HTTP method and URL pattern, (2) query, path, and body parameters with their names and types from function signatures and type hints, (3) request and response payload structures by analyzing serialization logic and type annotations, (4) authentication and authorization mechanisms (e.g., API keys, headers). Map these extracted elements into an OpenAPI 3.0 document structure with proper schemas, parameter definitions, and response models. Validate the generated specification against the source code for structural correctness, parameter completeness, and schema accuracy by cross-referencing endpoint implementations.
web application starts job calculations via the joblauncher webservice.: "The web application starts job calculations via the joblauncher webservice."