Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Runs HLE-oriented benchmark reward ingestion and curriculum generation for capability-evolver. Use when the user asks to optimize Humanity's Last Exam score,...
Runs HLE-oriented benchmark reward ingestion and curriculum generation for capability-evolver. Use when the user asks to optimize Humanity's Last Exam score,...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
This skill operationalizes HLE score-driven evolution for OpenClaw.
User asks to improve HLE score (for example target >= 60%). User provides question-level benchmark output and wants it converted to reward. User wants easy-first curriculum queue and next-focus questions. User asks for an immediate benchmark result snapshot.
Benchmark report JSON path (--report=/abs/path/report.json) Optional benchmark id (cais/hle default)
Validate the report JSON exists and is parseable. Ingest report into capability-evolver benchmark reward state. Generate curriculum signals: benchmark_* curriculum_stage:* focus_subject:* focus_modality:* question_focus:* Return a compact result summary for this run.
node skills/hle-benchmark-evolver/run_result.js --report=/absolute/path/hle_report.json Full automatic loop (starts evolution cycle): node skills/hle-benchmark-evolver/run_pipeline.js --report=/absolute/path/hle_report.json --cycles=1 If your evaluator can be called from shell, let pipeline generate the report each cycle: node skills/hle-benchmark-evolver/run_pipeline.js \ --report=/absolute/path/hle_report.json \ --eval_cmd="python /path/to/eval_hle.py --out {{report}}" \ --cycles=3 --interval_ms=2000 If no --report is provided, it defaults to: skills/capability-evolver/assets/gep/hle_report.template.json
Always print JSON with these fields: benchmark_id run_id accuracy reward trend curriculum_stage queue_size focus_subjects focus_modalities next_questions
This skill handles reward/curriculum ingestion. It does not directly solve HLE questions. run_pipeline.js links ingestion, evolve, and solidify into one executable loop.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.