dial9-toolkit

name	dial9-toolkit
description	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.

dial9 Analysis Toolkit

This skill provides the JavaScript modules for working with dial9 traces programmatically.

What traces capture

dial9 traces capture the internal behavior of a Tokio async runtime:

• Poll events: Every time a worker thread polls a task future (start/end timestamps, task ID, spawn location)
• Worker lifecycle: Park/unpark events with CPU time and kernel scheduling wait
• Queue depth: Periodic samples of the global injection queue
• Task lifecycle: Spawn and terminate events with spawn location
• Wake events: Which task woke which other task, and on which worker
• CPU samples: Periodic stack traces from perf/eBPF, attached to the poll they occurred in
• Scheduling samples: Stack traces captured when the kernel deschedules a worker thread (shows blocking calls)
• Clock sync: Monotonic-to-wall-clock anchors for correlating with external logs
• Span events: Enter/exit events from tracing spans (#[instrument]), showing what happened inside each poll with field values and nesting

Quick start

node scripts/analyze.js <trace.bin or directory>  # full diagnostic report
node scripts/analyze.js traces/ --sample 50       # quick overview of large directories
node scripts/analyze.js trace.bin --force          # ignore cached results

Modules

File	Purpose
scripts/analyze.js	CLI entry point and analyzeTraces() aggregation function
scripts/diagnose_setup.js	Setup diagnostic: detects missing frame pointers, wake events, debug symbols, sched events
scripts/trace_parser.js	Binary parser: parseTrace(path) yields ParsedTrace objects
scripts/trace_analysis.js	Analysis functions: buildWorkerSpans, attachCpuSamples, etc.
scripts/decode.js	Low-level binary format decoder

Default workflow

1. Always run analyzeTraces(path) first for trace diagnosis. This automatically runs setup diagnostics first.
2. If setup diagnostics report issues (missing frame pointers, missing wake events, missing debug symbols), help the user fix those before diving into performance analysis.
3. Base initial findings on the aggregate result: long polls, worker spans, scheduling delays, CPU/off-CPU groups, queue depth, task lifecycle counts, and span summaries.
4. Then use parseTrace() or lower-level helpers only to confirm assumptions, inspect raw events, or follow a specific task/wake/span chronology.

Setup diagnostic

The setup diagnostic (diagnose_setup.js) runs automatically as part of analyze.js and checks for common configuration issues:

Check	Severity	Symptom
missing-frame-pointers	critical	CPU stacks are 1–3 frames deep instead of 10+
missing-wake-events	warning	Tasks spawned but no wake events recorded
missing-debug-symbols	warning	Stack addresses unresolved or no source locations
no-scheduling-events	info	No off-CPU samples (sched profiling not enabled)

Run standalone:

node scripts/diagnose_setup.js <trace.bin or directory>

const { analyzeTraces } = require('./scripts/analyze.js');
const result = await analyzeTraces('/path/to/traces/');
// result.longPolls, result.workerSpans, result.schedDelayHist, result.cpuGroups, result.spanStats

analyzeTraces works on a single file or a directory. It returns a single aggregated result object with everything you need for diagnosis. See the dial9-trace-analysis skill for the full return schema.

After the aggregate pass, use the low-level parser to confirm assumptions or inspect raw details that the aggregate result points at, such as specific tasks, custom filters, wake chains, or per-file chronology:

const { parseTrace } = require('./scripts/trace_parser.js');
const trace = await parseTrace('/path/to/trace.bin');  // single file
// or iterate a directory:
for await (const trace of parseTrace('/path/to/traces/')) { ... }