Anam
Official@anam-org
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Agent Skills by Anam
Showing 7 vetted skills indexed across 1 GitHub repositories.
anam-orgsybil
Execute documentation code samples during pytest runs.
anam-orgsyrupy
Validate Python test outputs against stored snapshots with pytest integration.
anam-orgclaude-improve-config
Analyze sessions to propose CLAUDE.md, hook, and skill improvements.
anam-orgnarwhals
Write dataframe-agnostic Python code that runs across multiple backends.
anam-orgdocs-page-frontmatter
Generate YAML front matter with title and description fields for documentation pages.
anam-orghypothesis
Generate test inputs and shrink failing examples for Python property tests.
anam-orgmetaxy
Guide Metaxy feature definitions, versioning, and metadata tooling in data pipelines.
Frequently Asked Questions About Anam
FAQPage SchemaWhat specific engineering tasks are enabled by these capabilities?▼
These capabilities enable snapshot-based output validation, property-based input generation for edge-case discovery, dataframe-agnostic code portability, and automated generation of documentation metadata. They facilitate rigorous verification of code samples and ensure consistent documentation standards across project repositories.
Which technical personas benefit most from these utilities?▼
Software engineers, quality assurance specialists, and technical writers benefit most. These utilities are designed for developers maintaining complex test suites, engineers building cross-backend data processing layers, and documentation maintainers seeking to standardize project metadata and ensure code sample accuracy.
What are the primary prerequisites for integrating these utilities?▼
Integration requires a standard testing environment, specifically pytest for the snapshot and property-based testing components. Users must also maintain a structured documentation directory and have existing data processing logic that can be refactored to utilize dataframe-agnostic abstractions.