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Automated health checks for dial9 Tokio runtime traces. Detects long polls, task leaks, scheduling delays, blocking calls, queue buildup, worker imbalance, CPU contention, and span anomalies. Use when you want a quick automated assessment of trace health.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/dial9-rs/dial9 --skill dial9-red-flags명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
JavaScript analysis toolkit for parsing and analyzing dial9 Tokio runtime traces. Always start trace diagnosis with analyzeTraces() from analyze.js, then use parseTrace() and lower-level helpers only to confirm assumptions or drill into raw events.
Analyze dial9 Tokio runtime traces stored in S3 buckets. Use when a user provides an S3 bucket containing dial9 traces and wants to understand runtime behavior, diagnose performance issues, or explore what data is available.
Compile dial9 trace analysis insights into a polished HTML report folder with embedded flamegraphs, timeline strips, and viewer deep-links. Use when you have findings from trace analysis and need to deliver them as something a human can open in a browser.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | dial9-red-flags |
| description | Automated health checks for dial9 Tokio runtime traces. Detects long polls, task leaks, scheduling delays, blocking calls, queue buildup, worker imbalance, CPU contention, and span anomalies. Use when you want a quick automated assessment of trace health. |
Run scripts/red_flag_scan.js against any trace to surface common Tokio runtime problems.
node scripts/red_flag_scan.js <trace.bin or directory>
Each finding has a severity: critical, warning, or info.
A single .poll() call took too long. This blocks the worker from processing other tasks. The fixed >10ms warning / >50ms critical cutoffs here are a coarse default, not a universal truth — "long" is really relative to this runtime's own poll distribution. In a service whose p99 poll is 500µs, a 1ms poll is a severe tail outlier these cutoffs miss entirely; in a batch job whose p99 is 40ms, a 20ms poll is normal. Calibrate against pollDurationByLoc (p50/p99 per spawn location) before trusting an absolute threshold. Look at poll.cpuSamples and poll.schedSamples for stack traces. To root-cause why a flagged poll was long — especially an off-CPU one with no scheduling stacks — use the dial9-diagnose-long-poll skill (which thresholds on p99 by default), and dial9-zoom-window to inspect the surrounding instant.
Active task count grows without bound. Tasks are spawned but never complete. Check taskSpawnLocs for spawn locations of unterminated tasks.
Time between Waker::wake() and the task being polled exceeds 5ms. All workers are busy. Fix: shorter polls, more workers, or yield points.
Scheduling samples (source=1) reveal blocking system calls (file I/O, DNS resolution, mutex contention) on the async runtime. These should use spawn_blocking or a dedicated thread.
Global injection queue exceeds 100 (warning) or 1000 (critical). The runtime cannot keep up with incoming work.
Poll counts differ by more than 3x across workers. Work-stealing may not be distributing evenly, or one worker is stuck on long polls.
Workers are active but spending less than 50% of wall time on CPU. The kernel is descheduling them due to CPU contention.
Worker unpark takes more than 1ms of kernel scheduling wait. Indicates CPU contention at the OS level.
A single poll contains more than 20 span enter/exit pairs. Usually a tight loop without yielding.
A span whose duration exceeds 10x the P50 for its name. Flags individual slow operations.
Spans with enter but no exit. Small counts are normal at segment boundaries. Large counts may indicate task cancellation or a bug in span instrumentation.