Company-neutral workflow kit for creating, reviewing, calibrating, and packaging Harbor / Terminal-Bench tasks with LLM agents.
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Updated
May 18, 2026 - Python
Company-neutral workflow kit for creating, reviewing, calibrating, and packaging Harbor / Terminal-Bench tasks with LLM agents.
Rebuild a Rust crate at a historical commit in a digest-pinned Docker image and verify a fail-to-pass flip
An offline benchmark task using Terraform, Python, and Graphviz to reconstruct corrupted machine learning model lineage metadata, query Hugging Face dataset split counts, and render color-coded lineage graphs.
Mine and rank candidate fail-to-pass tasks from real repository history: file classifier, transparent scorer, patch-fingerprint dedupe ledger, JSONL export.
An AI coding benchmark task evaluating automated rebase conflict resolution, MLflow tracking security, secret redaction, and Hugging Face model evaluation safety.
Review gates for pull requests that add or change benchmark tasks for AI coding agents: Python CLI and composite GitHub Action, with Docker runner, determinism reruns and a content-hash cache
An automated media verification & reconciliation engine resolving conflicting SQLite attestations, Ed25519 signed release manifests, and lost Git verifier policies via Git reflog recovery.
To associate your repository with the benchmark-tasks topic, visit your repo's landing page and select "manage topics."