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grid-ctf-ops
// Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.
// Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies.
| name | grid-ctf-ops |
| description | Operational knowledge for the grid_ctf scenario including strategy playbook, lessons learned, and resource references. Use when generating, evaluating, coaching, or debugging grid_ctf strategies. |
Accumulated knowledge from autocontext strategy evolution.
Prescriptive rules derived from what worked and what failed:
knowledge/grid_ctf/analysis/ — per-generation analysis markdownknowledge/grid_ctf/tools/ — architect-created Python toolsknowledge/grid_ctf/coach_history.md — raw coach output across all generationsknowledge/grid_ctf/architect/changelog.md — infrastructure and tooling changesUse when a Hermes agent needs to evaluate agent behavior, run Autocontext scenarios, inspect Hermes curator state, export reusable knowledge, or prepare local MLX/CUDA training data through the autoctx CLI.
Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, inspect runtime artifacts and session branch lineage, or discover available scenarios. Provides LLM-based judging with rubric-driven scoring.