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Live queue monitoring dashboard (curses TUI) for real-time visibility into DELEGATE/HANDBACK protocol queue status, metrics, and task progress across incoming, processing, and done states.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Experiment orchestration framework with traffic allocation, statistical analysis, and early stopping detection. Use to test routing changes, model upgrades, and role assignments with Welch's t-test significance testing.
DEPRECATED — This skill is no longer maintained. Prometheus and Grafana infrastructure was never implemented. Use local JSON metrics analysis instead.
Scaffolds new SPEC-compliant agentic-engineers agents with a single call. Generates SKILL.md frontmatter, test scaffolds (TDD RED-phase), __init__.py, scripts/ layout, and DELEGATE/HANDBACK protocol templates. Use when creating any new automation agent, task handler, or operational tool. Validates role, model, effort, naming, and dependency graphs (circular dep detection) before writing files. Supports dry-run mode for planning without side effects.
Routine maintenance for Codex sessions: monitor queue state, close completed sub-agents, resume or escalate active work, and keep agent capacity available.
Automated cross-validation of protocol queue integrity. Scans all DELEGATEs/HANDBACKs, validates schema compliance, detects cycles, checks rate limits, generates compliance report. Enables self-referential protocol improvements.
Consolidates provider-specific AI costs into unified metrics across Anthropic, OpenAI, Google Gemini, GitHub Copilot, and Ollama. Enables apples-to-apples cost comparison and savings analysis.
| name | queue-monitor |
| description | Live queue monitoring dashboard (curses TUI) for real-time visibility into DELEGATE/HANDBACK protocol queue status, metrics, and task progress across incoming, processing, and done states. |
| license | Proprietary |
| compatibility | agentic-engineers framework v5.10+. Requires Python 3.11+ |
| metadata | {"author":"agentic-engineers","version":"1.0.0","category":"observability","role":"orchestrator","model":"claude-haiku-4.5","effort":"high","dependencies":[{"skill":"queue-isolation","optional":true},{"skill":"queue-query","optional":false}]} |
| entry_points | ["scripts/queue_monitor.py"] |
| tests | ["tests/test_queue_monitor.py"] |
| coverage_minimum | 85 |
Real-time curses-based TUI for monitoring the agentic-engineers queue. Displays live queue metrics, DELEGATE/HANDBACK task progress, and agent execution status across incoming, processing, and done states.
~/.agentic-engineers/<harness>/<session-id>/queue/
incoming/ processing/ done/ failed/
python scripts/queue_monitor.py --session-id wave1-2026-06-15 --harness claude
python scripts/queue_monitor.py # auto-detect
python scripts/queue_monitor.py --base-dir /custom/path --harness local # custom paths
Global flags: --session-id, --harness, --base-dir (testing).
QUEUE MONITOR — Status: 3 incoming, 1 processing, 5 done
┌─────────────────────┬──────────────────┬──────────────────────┐
│ INCOMING (3) │ PROCESSING (1) │ DONE (5) │
├─────────────────────┼──────────────────┼──────────────────────┤
│ [2026-06-15-001] │ [2026-06-15-002] │ [2026-06-15-005] ✓ │
│ harness-eval │ skills-audit │ duration: 45s │
│ quality-engineer │ model-engineer │ │
│ │ 15s (running) │ [2026-06-15-004] ✓ │
│ [2026-06-15-003] │ │ duration: 120s │
│ queue-monitor │ │ │
│ engineer │ │ [2026-06-15-003] ✗ │
│ │ │ failure: timeout │
│ [2026-06-15-006] │ │ │
│ spec-validation │ │ [2026-06-15-002] ✓ │
│ security-engineer │ │ duration: 180s │
│ │ │ │
│ │ │ [2026-06-15-001] ✓ │
│ │ │ duration: 300s │
└─────────────────────┴──────────────────┴──────────────────────┘
METRICS SUMMARY
Incoming: 3 │ Processing: 1 │ Done: 5 (success: 4, failure: 1)
Avg duration: 161s │ Success rate: 80% │ Last updated: 2026-06-15 17:45:32
Press 'q' to quit, 'r' to refresh, 's' for stats detail
from scripts.queue_monitor import QueueMonitor, QueueMonitorUI
monitor = QueueMonitor(session_id=None, harness=None, base_dir=None)
ui = QueueMonitorUI(monitor)
ui.run()
Methods:
| Method | Purpose |
|---|---|
poll() | Refresh queue state from filesystem. |
get_metrics() | Return aggregated metrics dict. |
get_task_details(state, task_id) | Return parsed DELEGATE/HANDBACK details. |
curses.SIGWINCHThis skill participates in the framework's continuous improvement cycle (see skill-improvement-feedback).
When you use queue-monitor during a task, include a skill_feedback entry in your HANDBACK to help improve it over time:
skill_feedback:
- skill_name: queue-monitor
effectiveness_score: 0.85 # required: 0.0–1.0
clarity_score: 0.90 # optional
coverage_gaps:
- "Specific scenario the skill did not address"
improvement_suggestions:
- "Concrete change that would have helped"
usage_context: "One sentence on how you used this skill"
Positive feedback is as valuable as critical feedback. Three or more feedback items for this skill automatically trigger an improvement task.