harness/harness-schema
May 2026 – May 2026
1 Month active
Languages Used
JSONYAML
Technical Skills
API IntegrationAPI developmentCI/CDJSON SchemaJSON schema validationschema design
Srikar Mannepalli
PROFILE
Srikar Mannepalli
Developed a native AI Evaluation Run step for CI/CD pipelines within the harness/harness-schema repository, focusing on automation and streamlined configuration. Leveraging skills in API integration, JSON Schema, and schema design, the work introduced robust configurations for running evaluations, managing concurrency, and integrating with external APIs. The step schema was updated to require an apiKey and remove OTLP-related options, reducing misconfigurations and aligning with template v1.2.0 for forward compatibility. By enhancing automation and observability, the solution accelerated AI evaluation processes, minimized manual intervention, and improved security and governance in CI/CD workflows using JSON and YAML for schema validation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
1,015
Activity Months1
Your Network
466 peopleShared Repositories
111
Work History
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 — Harness Schema: Delivered AI Evaluation Runs as a native step in CI/CD pipelines, with robust configurations for running evaluations, concurrency management, and integration with external APIs. Updated the step schema to require an apiKey and removed unnecessary OTLP configurations to streamline the evaluation process, aligning with template v1.2.0. This work accelerates automated AI evaluations, reduces manual intervention, and improves security and governance in CI/CD workflows.
2 Commits • 1 Features
May 1, 2026May 2026 — Harness Schema: Delivered AI Evaluation Runs as a native step in CI/CD pipelines, with robust configurations for running evaluations, concurrency management, and integration with external APIs. Updated the step schema to require an apiKey and removed unnecessary OTLP configurations to streamline the evaluation process, aligning with template v1.2.0. This work accelerates automated AI evaluations, reduces manual intervention, and improves security and governance in CI/CD workflows.
May 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
JSONYAML
Technical Skills
API IntegrationAPI developmentCI/CDJSON SchemaJSON schema validationschema design
Repositories Contributed To
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