「execution」の検索結果

Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).

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Parse Windows Prefetch files (versions 17, 23, 26, 30) with tools like PECmd, WinPrefetchView, or python-prefetch to determine program execution history, including run counts, execution timestamps, and referenced files/DLLs. Use when building a timeline of program execution on a Windows system, confirming whether a suspicious binary ran, or correlating execution evidence with other forensic artifacts during an investigation.

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Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

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ISTQB/JSTQB のテスト実行（test execution）活動を支援するスキル。test-implement が具体化したテストを実行し、結果を記録し、失敗を欠陥候補として整理して test-execution-log.md にまとめる。自動テストならランナーを実行し結果を解釈、手動テストなら利用者の実施結果を聞き取って記録する。失敗は欠陥候補として整理し、外部 issue 化は提案に留める。「テストを実行して結果を記録して」「テスト実行ログを作って」「失敗を欠陥として整理して」「手動テストの結果を記録して」と依頼されたときに使う。ワークフローの test-targeted（修正範囲にテストランナーを絞り込んで実行するだけ）とは別物で、こちらは ISTQB プロセスとしての実行・記録・欠陥候補整理を成果物 test-execution-log.md に残す。単発のテスト作成依頼（単にユニットテストを書きたいだけ）は対象外。/test-execute <テスト対象名> で明示的に呼び出されたときのみ使用する。

World-class trade execution - minimize market impact, optimize order routing, reduce slippage. The difference between backtest and live is usually execution. Use when "execution, slippage, market impact, order routing, TWAP, VWAP, iceberg, fill rate, best execution, " mentioned.

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Manage DAG execution lifecycles including start, stop, pause, resume, and cleanup. Activate on 'execution lifecycle', 'stop execution', 'abort DAG', 'graceful shutdown', 'kill process'. NOT for cost estimation, DAG building, or skill selection.

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Manage DAG execution lifecycles including start, stop, pause, resume, and cleanup. Activate on 'execution lifecycle', 'stop execution', 'abort DAG', 'graceful shutdown', 'kill process'. NOT for cost estimation, DAG building, or skill selection.

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Trade execution modeling (backtest only) — slippage formulas (linear / square-root impact), VWAP/TWAP execution logic, market-impact cost estimation, and execution-assumption configuration.

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Parse Windows Prefetch (.pf) files with the windowsprefetch Python library to reconstruct application execution history, run counts, and accessed file/volume lists. Use when investigating renamed or masquerading binaries, verifying program execution timelines, or hunting for suspicious execution patterns in incident response.

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Builds resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like lambda durable functions, durable execution, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, reliable serverless applications, context.step, context.wait, context.invoke, context.runInChildContext, withDurableExecution, DurableContext, UnrecoverableInvocationError, durable-execution-sdk, qualified ARN invocation, and durable handler replay.

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Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.

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Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.

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Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.

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Structures best execution monitoring with venue analysis, systematic internalization assessment, and regulatory compliance documentation. Use when managing best execution, documenting execution decisions, or conducting venue analysis.

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Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.

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Consumer Goods Cloud setup: visit execution, retail execution, route planning, in-store task management, compliance checks, off-shelf detection, image recognition. NOT for territory planning and route optimization for sales reps - use integration/salesforce-maps-setup. NOT for B2B Commerce storefronts - use admin/b2b-commerce-store-setup.

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Wave-based parallel scheduling for DAG execution. Manages execution order, resource allocation, and parallelism constraints. Activate on 'schedule dag', 'execution waves', 'parallel scheduling', 'task queue', 'resource allocation'. NOT for building DAGs (use dag-graph-builder) or actual execution (use dag-parallel-executor).

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Builds, validates, schedules, and dynamically modifies DAG execution graphs. Decomposes problems into nodes with dependencies, performs topological sorting, detects cycles, resolves conflicts, and schedules wave-based parallel execution. Use when designing a DAG structure, validating dependencies, planning execution order, or modifying a DAG at runtime. Activate on "build DAG", "plan workflow", "DAG dependencies", "topological sort", "schedule execution", "modify DAG", "replan". NOT for executing DAGs (use dag-runtime), validating outputs (use dag-quality), or matching skills to nodes (use dag-skills-matcher).

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Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

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Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).

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。

Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).

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Hunt for suspicious PowerShell execution (T1059.001) such as encoded commands, download cradles, AMSI bypass, and constrained language mode evasion using EDR telemetry (CrowdStrike, Microsoft Defender for Endpoint), Sysmon, and SIEM queries (Splunk, Elastic). Use when proactively threat hunting, triaging EDR/SIEM alerts, or scoping an incident involving malicious PowerShell activity.

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Parses the Windows Amcache.hve registry hive with Eric Zimmerman's AmcacheParser and Timeline Explorer to extract evidence of program execution, application installation, and driver loading, including SHA-1 hash correlation with threat intel and timeline reconstruction. Use for Amcache forensics, program execution evidence gathering, or application compatibility cache investigations in DFIR work.

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。