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Analyze Windows Prefetch files, which record application execution data, using Python. Reconstruct execution timelines, review run counts and accessed resources, and identify suspicious programs or signs that an executable has been renamed to disguise its identity.
Parse Windows Prefetch files, which store traces of program execution, to extract run counts, timestamps, and referenced files. Build execution timelines and compare findings with other forensic evidence when investigating suspicious software or tool usage.
Model trading costs and execution assumptions in backtests, including slippage, the gap between expected and simulated fill prices. Compare price impact, fees, execution delays, and order splitting to assess how assumptions affect strategy results.
Guide shell, Python, and Node.js execution on the computer running a connected A0 CLI. It explains session reuse, interactive input, output retrieval, and recovery steps when execution permissions, timeouts, or disconnections interrupt the work.
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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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。
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Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.
Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.
Recover from repeated code execution tool failures by writing a script through the shell and running it with an absolute path. Check output files and investigate syntax, permissions, disk space, or execution errors when needed.
Recover from failed code execution by simplifying code, switching to shell commands, or handing complex tasks to shell_agent. Explicit working directories and output checks help keep generated files in the expected location.
Stop stuck Ouroboros executions or background jobs by ID, or cancel all running and paused executions in the current project. It reports whether cancellation is complete or has been requested, with guidance for checking the final status.
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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Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.
Investigate suspicious PowerShell activity using endpoint monitoring data and security logs. Examine encoded commands, downloads, and attempts to evade inspection, then document evidence, affected systems, risk, and recommended response actions.
Examine Windows shortcuts and Jump Lists, records of recently used files, to reconstruct user activity. Extract paths, timestamps, and device details with LECmd and JLECmd, including traces that remain after target files are deleted.
Manage Google Apps Script projects from the command line. Create projects, retrieve or update source files, inspect execution status, and fetch metrics such as execution counts and active users to review script activity.
Turn an approved decision into a 90-day execution plan with weekly milestones, named accountable owners, review meetings, dependencies, and risks. The plan preserves the decision’s success measures and criteria for stopping the work.
Investigate failing or slow BigQuery jobs and unexpected cost increases. Compare execution details and resource usage with a normal baseline to isolate bottlenecks, identify likely causes, and determine practical remediation steps.
Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.
Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.
Create and verify gentle-ai journeys, which are scripted sequences of product actions. Check their declarations separately from actual execution, review outdated expectations after behavior changes, and record execution results for pull requests.