anna-geller/prefect-aws-lambda

48.2

Weak · 21 September 2026

150

lines of production code

Python

primary language

4

measurements over time

What this system is

Features

Add ML inference entry point for AWS Lambda

A new Python module (ml.py) and its corresponding Dockerfile have been added to the ml directory, establishing a Lambda-compatible entry point. The module imports Prefect to orchestrate a simple dataflow task that logs a greeting and platform information, exposing a handler function for AWS Lambda invocation.

ml · high confidence

Add S3 event flow for generating and uploading time-series data

A new S3 event flow has been introduced to generate synthetic time-series data and upload it as Parquet files to an S3 bucket. The flow uses a configurable threshold (stored in a Prefect JSON block named 'max-value') to determine the range of random values in the generated dataset. The implementation includes a Dockerfile for containerization, a block definition for configuration, and the main flow logic using AWS Wrangler to write data to S3.

_s3\_event\flow · high confidence

Initial deployment of ETL and healthcheck Lambda functions

Added new AWS Lambda functions for the ETL pipeline and a healthcheck service. The ETL function fetches cryptocurrency prices from CryptoCompare, transforms the data using Pandas and PyArrow, and loads it into an S3-based data lake using Prefect flows. The healthcheck function logs platform and Prefect version details to verify the runtime environment. Both are containerized with Python 3.9 and Prefect dependencies.

etl, healthcheck · high confidence

New S3 reactive flow for data validation

A new AWS Lambda function has been added to process S3 events and validate data. The function reads Parquet files from S3, checks if the maximum value in the 'value' column exceeds 42, and sends a Slack notification if the threshold is breached.

_s3\_reactive\flow · high confidence

Written by watchdog.canine.dev from the codebase's own history, inside the signed delivery this page is composed from.

How this codebase got here

This is the PUBLIC form of this artifact. Findings are listed in full, but the details of SECURITY findings — which rule fired, in which file, on which line, and how to fix it — are deliberately withheld, and any secret-scanner results are excluded entirely. Where detail is absent here it was REMOVED FOR PUBLICATION; it is not missing from the analysis. The complete artifact is available from the repository owner.

Score

CAI 50 → 48 (-1.6)
Rubric changed (rubric-2026.08.18 → rubric-2026.09.15) — scores are not directly comparable.

Lenses

Code Health 100 → 100 (+0.0)
Architecture 69 → 69 (+0.0)
Maturity 61 → 61 (+0.0)
Readiness 36 → 36 (+0.0)
Security 54 → 48 (-6.3)

Resolved (12)

Dependency hygiene not measured — no supported dependency manifest was read
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
No exposed public API
early-stage repository — too little history to judge knowledge freshness
git history depth insufficient
git history depth insufficient
single-maintainer — knowledge-concentration (bus factor) risk

New (31)

Documentation: no installation or build instructions (README.md)
Documentation: no usage examples (README.md)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
High: security finding (details withheld)
Medium IaC: WD-DOCKER-0003 (etl/Dockerfile)
Medium IaC: WD-DOCKER-0003 (healthcheck/Dockerfile)
Medium IaC: WD-DOCKER-0003 (ml/Dockerfile)
Medium IaC: WD-DOCKER-0003 (s3_event_flow/Dockerfile)
…and 11 more

Written by watchdog.canine.dev from the codebase's own history, inside the signed delivery this page is composed from.

Survey your own repository

anna-geller/prefect-aws-lambda was measured the same way every project in this corpus was: the same rubric, at a pinned commit, with the result published in full. Point a surveyor at a repository you know and see whether you agree with it.

About this page

The score is its most recent published measurement, taken on 21 September 2026 at a pinned commit. It is not a live figure and does not change until the project is measured again.
Measured at commit c8850c8530cd790d047b2ba97dcda4bcb2bc881e — the exact code this score is about.
Scored under rubric-2026.09.15 — the same rubric and the same method as every other entry in this index.
Measured by watchdog.canine.dev using codehealth-analyzer preprod-28e75b8e3254.