Repository audit
README checks, broken links, git state, manifests, configs, test files, entrypoints, and generated report bundles.
Guy Giordano Jr | Agent QA Automation
GG Eff Agent Cataloger
A Python CLI for auditing agent repositories, validating documentation, inferring tools and data sources, scoring readiness, and producing portfolio-ready QA catalogs.
Skill Signal
Portfolio operations for agent repositories.
GG Eff Agent Cataloger scans agent repos, checks README completeness, finds broken local links, infers tool and data-source usage, compares findings against an allowlist, and writes review artifacts.
The strong engineering signal is repeatability: each agent review follows the same QA catalogue standard, so gaps, fixes, smoke tests, and readiness classifications can be compared across a portfolio.
10required README sections tracked for operational completeness
0-100repository score based on documentation, implementation, sync, and verification findings
2 modesremote GitHub scanning and local folder review
System Work
Agent review, documentation repair, and catalogue reporting in one CLI.
Tool inference
Signals from code, agent manifests, config files, and documentation are compared against allowed tools and data-source patterns.
QA artifacts
Smoke test results, runtime gap summaries, catalogue summaries, TODOs, and per-repo reports.
Code Evidence
Concrete files for technical review.
src/gg_eff_agent_cataloger/cli.py
Command-line entrypoint and mode selection.src/gg_eff_agent_cataloger/runner.py
Scan orchestration and report bundle creation.src/gg_eff_agent_cataloger/readme_analysis.py
README section validation and local link checks.src/gg_eff_agent_cataloger/tool_inference.py
Tool and data-source inference from repo contents.src/gg_eff_agent_cataloger/scoring.py
Readiness score calculation.docs/standards/QA_CATALOGUING_STANDARD.md
Reusable standard for comparable QA outputs.
Positioning
Agent engineering needs operational proof.
This project is a practical answer to messy AI-agent portfolios: audit the repositories, identify runtime and documentation gaps, standardize reports, and preserve evidence in a format that engineers and recruiters can review quickly.