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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.

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• Reconstructing a program execution timeline
• Investigating potentially renamed executables
• Reviewing suspicious tools and execution frequency

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.

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• Checking whether a suspicious program ran
• Building a timeline of investigative tool findings
• Investigating compression and file transfer 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.

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• Compare backtests under different slippage assumptions
• Estimate how trading costs reduce annual returns
• Explore VWAP and TWAP order splitting

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.

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• Run shell commands on the connected A0 CLI host
• Execute Python or Node.js on the host
• Retrieve output or answer an interactive program

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.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

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.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

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.

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• Run scripts when automated execution tools keep failing
• Save multiline scripts without shell variable expansion
• Check generated files for size and type

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.

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• Recovering from sandbox execution failures
• Keeping generated files in the intended folder
• Checking output files after code execution

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.

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• Investigating a slow task
• Reviewing coordination between agents
• Locating errors in recorded execution

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.

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• Hunting for suspicious PowerShell execution
• Investigating endpoint and security log alerts
• Assessing the scope of a PowerShell-related incident

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.

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• Reconstructing file access and program execution timelines
• Investigating access to files that have been deleted
• Examining traces of removable media use

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.

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• Creating a new Apps Script project
• Retrieving and updating script source files
• Checking execution status

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.

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• Turning an approved decision into a quarterly plan
• Assigning owners and weekly milestones
• Setting progress reviews and stopping criteria

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.

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• Investigate failed or slow BigQuery jobs
• Locate bottlenecks in execution stages
• Diagnose sudden increases in query or capacity costs

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.

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• Check execution roles before deploying a SageMaker endpoint
• Find an existing role when no ARN is provided
• Investigate IAM AccessDenied errors

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.

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• Test a Beam pipeline locally
• Configure pipeline execution on Dataflow
• Review Flink or Spark 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.

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• Adding a product operation journey
• Updating expectations after behavior changes
• Reproducing a bench failure from CI

Continue Python tasks when sandbox execution fails by saving code as a script and running it through run_shell. The workflow covers dependencies, execution checks, and output verification for PDFs, image processing, data analysis, and file operations.

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• Continuing Python tasks after sandbox initialization failures
• Generating PDFs from a saved script
• Running image processing or data analysis through the shell