climatepolicyradar/knowledge-graph
Aug 2025 – Oct 2025
2 Months active
Languages Used
PythonYAML
Technical Skills
AWSBackend DevelopmentCloud ComputingCode ClarityCode OrganizationConfiguration Management
Michael Okarimia
PROFILE
Michael Okarimia
Worked on the climatepolicyradar/knowledge-graph repository to centralize and streamline pipeline configuration management, consolidating multiple config classes into a unified model for use across aggregation, inference, and indexing flows. Refactored code for clarity and maintainability, aligning imports and standardizing configuration usage. Enhanced reliability by enforcing AWS CLI login in CI workflows and improving documentation for developer guidance. Enabled broader classifier deployment on Sabin documents while excluding placeholders to improve data quality. Introduced failure-handling scripts for Prefect runs, allowing rapid identification and reprocessing of failed documents. Utilized Python, AWS, and Prefect, with a focus on backend development and automation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
21Total
Bugs
0
Commits
21
Features
4
Lines of code
2,491
Activity Months2
Your Network
16 peopleSame Organization
@climatepolicyradar.org7
Shared Repositories
9
arminellovell-sourceMember
olaughterMember
Harrison PimMember
Jesse ClavenMember
juliesaigusaCPRMember
Kalyan DutiaMember
Kerry ParkerMember
MarkMember
cpr-tech-adminMember
Work History
October 2025
6 Commits • 3 Features
Oct 1, 2025October 2025: Delivered three core improvements in climatepolicyradar/knowledge-graph that boost reliability, coverage, and operability. Key features: 1) Enforce AWS CLI login for evaluate.py in CI and clean up docstrings; 2) Enable all classifiers on Sabin documents by removing dont_run_on entries and exclude Sabin Placeholder documents from inference; 3) Add failure-handling for KG Prefect runs with a helper to extract failed IDs and an enhanced audit helper for quick reprocessing via Prefect UI. Major bugs fixed: exclusion of Sabin Placeholder documents from inference; improved visibility and reprocessing workflow for KG failures. Impact: improved data quality, classifier coverage, and faster remediation with better developer and operator experience. Technologies demonstrated: Python, AWS CLI, Prefect, KG auditing tooling, and documentation standards.
6 Commits • 3 Features
Oct 1, 2025October 2025: Delivered three core improvements in climatepolicyradar/knowledge-graph that boost reliability, coverage, and operability. Key features: 1) Enforce AWS CLI login for evaluate.py in CI and clean up docstrings; 2) Enable all classifiers on Sabin documents by removing dont_run_on entries and exclude Sabin Placeholder documents from inference; 3) Add failure-handling for KG Prefect runs with a helper to extract failed IDs and an enhanced audit helper for quick reprocessing via Prefect UI. Major bugs fixed: exclusion of Sabin Placeholder documents from inference; improved visibility and reprocessing workflow for KG failures. Impact: improved data quality, classifier coverage, and faster remediation with better developer and operator experience. Technologies demonstrated: Python, AWS CLI, Prefect, KG auditing tooling, and documentation standards.
October 2025
August 2025
15 Commits • 1 Features
Aug 1, 2025August 2025: Implemented unified pipeline configuration management for knowledge-graph, consolidating pipeline configs into a single Config model exposed via flows.config and flows.pipeline_config and used across aggregation, inference, and indexing flows. Refactors renamed and relocated config classes for clarity, removed InferenceConfig in favor of pipeline_config.Config, and introduced a combined config for full_pipeline. Added targeted test/config fixes and improved observability with a JSON representation update for flows.Config.
August 2025
15 Commits • 1 Features
Aug 1, 2025August 2025: Implemented unified pipeline configuration management for knowledge-graph, consolidating pipeline configs into a single Config model exposed via flows.config and flows.pipeline_config and used across aggregation, inference, and indexing flows. Refactors renamed and relocated config classes for clarity, removed InferenceConfig in favor of pipeline_config.Config, and introduced a combined config for full_pipeline. Added targeted test/config fixes and improved observability with a JSON representation update for flows.Config.
Activity
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Quality Metrics
Correctness94.2%
Maintainability94.8%
Architecture93.4%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
AWSAWS CLIAutomationBackend DevelopmentCI/CDCloud ComputingCode ClarityCode OrganizationConfiguration ManagementData EngineeringData ProcessingDebuggingDevOpsDocumentationEnvironment Variables
Repositories Contributed To
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