本文へ移動
cccskills
無料GitHub で公開

dag-execution-tracer

Designs or reviews provenance-bound DAG attempt traces, coverage gaps, cancellation, and unknown-effect reconciliation. NOT for claiming execution completeness, deployment monitoring, or root-cause proof.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md5.2 KB
  • references/source-and-hand-checks.md10.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

DAG Execution Tracer

A trace is an instrumentation record, not proof that every node ran, an external effect occurred, or a missing span implies success. Sampling and export loss must be recorded as coverage limits.

Method 1 — model run, node, and attempt separately

Create immutable run ID and graph revision; node ID; attempt ID; trace/span ID; parent or span links; lifecycle state (scheduled, started, yielded, retry, success, failed, cancel-requested, cancellation-confirmed, or unknown). Attach input/output digest plus schema, a redacted evidence pointer, actor/tool/model/config version, clock source, and sampling/export/drop counters. Use parent spans for nested work and links for fan-in, scatter/gather, and cross-trace causes. Attempt to end spans in finally for handled exits; this is best-effort and cannot record a terminal event after process loss. Preserve failed attempts and expose missing coverage.

Method 2 — gate timeout and cancellation recovery

On a timeout, capture the last known state and query the receipt or target for the exact operation identity. If effect state is unknown, hold a repeat unless either (a) an authoritative terminal-absence receipt is bound to the exact operation and a fence excludes a late first commit, with current retry authority, or (b) a validated idempotency/deduplication contract covers the same operation and context, with current retry authority. Compensation requires a confirmed effect, applicable postconditions, and its own authority; afterward, separately authorize any retry under a fence or same-operation deduplication rule. A cancellation request without acknowledgement remains unknown, not cancelled. Trace absence is a query gap, not success or a skipped node.

sequenceDiagram
  participant R as Run r7
  participant C as Node C
  participant A1 as Attempt 1
  participant X as Target or receipt
  participant A2 as Attempt 2
  R->>C: start with graph revision and node identity
  C->>A1: create distinct attempt ID
  A1-->>C: timeout witness
  C->>X: reconcile operation identity and target state
  alt effect confirmed already applied
    X-->>C: preserve receipt and do not replay
  else authoritative terminal absence fenced and retry authorized
    X-->>C: record reconciliation and authority
    C->>A2: new attempt ID and retry reason
    A2-->>C: output digest and terminal receipt
  else validated same-operation dedup and retry authority
    X-->>C: record key, context, and authority
    C->>A2: new attempt ID with same operation key
    A2-->>C: output digest and terminal receipt
  else unresolved or retry gate missing
    X-->>C: hold and do not retry
  end

Method 3 — query causality honestly

A wall-clock order across machines is not causal order. Use parent IDs, links, event sequencing, and clock uncertainty. Query an expected dependency path and report complete, partial, sampled, dropped, or unknown coverage. Measure capture overhead on representative workloads; retention and sampling are local policy, not universal byte, node-count, or percentage thresholds.

flowchart LR
  I[Run node attempt identity] --> S[Started span and clock source]
  S --> T[Tool child or linked fan-in span]
  T --> E{Terminal evidence present?}
  E -->|success or failure| P[Preserve digest and evidence pointer]
  E -->|cancel requested only| U[Unknown terminal state]
  E -->|sampled or dropped| G[Trace gap; query receipt]
  P --> Q[Trace query with coverage label]
  U --> Q
  G --> Q

Hand check

r7/C/attempt-1 times out at 12:04Z. Reconciliation remains unknown, so it is held. attempt-2 may start at 12:06Z only if an authoritative absence receipt binds to this operation and a fence excludes a late first commit, with current retry authority, or a validated idempotency/deduplication key covers the same operation and context with current retry authority. The aggregator links the accepted digest to attempt 2 and retains attempt 1. A sampled missing span does not prove C was skipped.

Sources and limits

OpenTelemetry Trace API supports context, spans and links; its overview covers scatter/gather links. CloudEvents v1.0.2 defines event identity using source plus ID. They define representation conventions, not execution receipts or completeness guarantees. See reference note.

Related skills

  • dag-failure-analyzer investigates incidents using trace and other evidence.
  • dag-performance-profiler measures performance; this skill supplies trace observations only.
  • dag-task-scheduler provides execution context, and dag-pattern-learner analyzes repeated patterns.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

curiositech のスキルをすべて見る

このスキルの問題を報告する