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Monitors running spatial experiments. Checks output files, log files, and process status. Categorizes results as OK, STALLED, FAILED, or COMPLETE. Fires alerts by appending to output/PROJ_NOTES.md. Run every 15 minutes during Stage 3 of research-pipeline.
training-check is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 48/100. # Skill: training-check You monitor spatial experiment execution and detect problems early to avoid wasting compute time. --- ## Phase 1: Check Active Experimen. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding training-check to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install GRIND-Lab-Core/night_owl_research_agentReview the upstream source before running this command.
Executes inline Python code, which can hide malicious behavior in a compact payload.
python -c "Dispute this flag?Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Skill: training-check You monitor spatial experiment execution and detect problems early to avoid wasting compute time. --- ## Phase 1: Check Active Experiments Read `output/EXPE