opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ls-network-probes": {
      "type": "local",
      "command": ["python", "-m", "ls.agent_shell.mcp_server"],
      "enabled": true,
      "environment": {
        "PYTHONPATH": "python;python/modules"
      }
    }
  },
  "command": {
    "ls-precision-report": {
      "description": "Run LS network precision contributor report and create a GitHub issue body",
      "template": "Run python scripts/prepare_network_precision_contributor_report.py --runner $ARGUMENTS --output reports/network_precision_contributor_report.md, then read the generated report and format it as a GitHub issue comment body using the contributor issue template at .github/ISSUE_TEMPLATE/network_precision_contributor_run.md. If $ARGUMENTS is empty, use a placeholder runner."
    },
    "ls-probe-roster": {
      "description": "Show which LS actors are ready or unavailable on this environment",
      "template": "Run python scripts/run_model_roster_depth_probe.py --json and display the roster table: which actors are ready, which are unavailable, and what they are missing."
    },
    "ls-probe-precision": {
      "description": "Run the LS network precision gain probe and show results",
      "template": "Run python scripts/run_network_precision_gain_demo.py --json and display the precision scores: measured_route_reward_gain, network_precision_gain_over_baseline, stack_added_gain_over_cooperation, and score_ratio_vs_baseline."
    },
    "ls-probe-trajectory": {
      "description": "Run the LS network trajectory probe and show velocity and observer delta",
      "template": "Run python scripts/run_network_trajectory_demo.py --cycles $ARGUMENTS --json and display the summary: observer_delta_final, observer_velocity_multiplier, trajectory_gain_over_baseline, drift_reduction, and resonance_gain. If $ARGUMENTS is empty, use 6 cycles."
    },
    "ls-probe-conductor-noise": {
      "description": "Run the LS conductor noise robustness probe",
      "template": "Run python scripts/run_conductor_noise_robustness_demo.py --cycles 6 --seeds 12 --json and display the decision plus each aggregate noise_level, pass_rate, avg_fresh_minus_stale, avg_fresh_gain, avg_stale_gain, and avg_inverted_penalty."
    },
    "ls-live-pilot": {
      "description": "Run the LS E6 live model pilot in safe sample mode",
      "template": "Run python scripts/run_live_model_pilot.py --json and display the decision, pilot_precision_proxy, actor, ready actors, route event id, and next_step. Do not pass --live unless the user explicitly asks to call a configured model route."
    }
  },
  "instructions": [
    "docs/IDE_TESTING_ENTRYPOINTS.md",
    "docs/NETWORK_PRECISION_CONTRIBUTOR_CALL.md",
    "docs/NETWORK_TRAJECTORY_LAYER.md",
    "docs/CONDUCTOR_NOISE_ROBUSTNESS.md",
    "docs/LIVE_MODEL_PILOT.md"
  ]
}