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incomplete suites\n- Plan variance runs and gap-fill campaigns\n- Recommend task categories to focus on\n- Generate next-step recommendations based on current state\n\n## Canonical Commands\n\n```bash\n# Overall readiness assessment\npython3 scripts\u002Finfra\u002Fcheck_infra.py\n\n# Current run status and coverage\npython3 scripts\u002Fanalysis\u002Faggregate_status.py --staging\n\n# Gap analysis (missing suites\u002Fconfigs)\ngrep -r \"missing\\|incomplete\" runs\u002Fstaging\u002F*\u002Fresult.json | head -20\n\n# Plan variance runs\npython3 scripts\u002Fanalysis\u002Fplan_variance_runs.py --target 50 --dry-run\n\n# Coverage gap analysis\npython3 scripts\u002Fanalysis\u002Fanalyze_run_coverage.py --runs-dir runs\u002Fstaging\n```\n\n## Planning Workflow\n\n1. **Assess current state** — status, completion rates, failures\n2. **Identify gaps** — missing suites, configurations, models\n3. **Categorize work** — high-priority, medium, low-priority next steps\n4. **Estimate effort** — time, resources, dependencies\n5. **Recommend** — specific run commands and configurations\n\n## Gap Analysis Dimensions\n\n- **Suite coverage** — which suites are incomplete?\n- **Configuration** — missing baseline, MCP, or paired runs?\n- **Model coverage** — which models need testing?\n- **Environment** — Daytona vs local Docker?\n- **Rerun strategy** — which failures should be retried?\n\n## Variance Planning\n\n- **Power analysis** — how many runs for statistical significance?\n- **Rebalancing** — equalize difficulty across runs\n- **DOE (Design of Experiments)** — optimize run selection\n- **Gap-fill** — targeted runs to complete coverage\n\n## Related Skills\n\n- `\u002Fstatus` — check current completion state\n- `\u002Frun` — execute next steps\n- `\u002Faudit` — validate readiness for next 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