Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Ingests question-level HLE benchmark results into the capability-evolver reward system and generates curriculum signals for evolution cycles. Converts per-question accuracy data into prioritized queues that focus subsequent training on easier questions first.
Closes the loop between benchmark evaluation and capability evolution by automating reward ingestion, curriculum prioritization, and solidification in a single pipeline command.
When to use it
- Processing an HLE report JSON into reward and curriculum signals
- Running a multi-cycle evolution pipeline with automatic re-evaluation
- Checking current benchmark accuracy and curriculum stage snapshot
- Targeting a specific HLE score threshold using easy-first question queues
- Identifying which subjects and modalities to focus on next
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: ingest my HLE report and generate curriculum signals
- 1Validate the report JSON exists at the provided path
- 2Ingest report into capability-evolver benchmark reward state
- 3Generate curriculum signals: benchmark_*, curriculum_stage:*, focus_subject:*, question_focus:*
- 4Print compact result summary as JSON
JSON with benchmark_id, accuracy, reward, trend, curriculum_stage, queue_size, focus_subjects, and next_questions