Results for “execution”

Debugs recorded DAG lineage through historical inspection, deterministic decision replay when prerequisites hold, and clearly labeled counterfactual or live re-execution. Activate on "debug DAG", "replay execution", "inspect node state", "compare runs", or "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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Redux state for agent execution and firing agent shortcuts. Use when editing features/agents/redux/, building agent UI, creating a conversation, touching assembleRequest or NDJSON stream state, wiring agent-state selectors, adding a per-conversation capability, or triggering a shortcut from a button, menu, or code (useShortcutTrigger, launchAgentExecution).

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Agent-to-Agent (A2A) executor implementation patterns for task handling, execution management, and agent coordination. Use when building A2A executors, implementing task handlers, creating agent execution flows, or when user mentions A2A protocol, task execution, agent executors, task handlers, or agent coordination.

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Detect suspicious PowerShell execution patterns including encoded commands, download cradles, AMSI bypass attempts, and constrained language mode evasion.

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Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.

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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.

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Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

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Complete OKR & Strategy Execution system — from company vision to weekly execution. Covers goal hierarchy, OKR writing methodology, scoring rubrics, alignment cascading, KPI dashboards, review cadences, team accountability, and quarterly planning rituals. Use when setting goals, running planning cycles, tracking OKRs, building KPI dashboards, running retrospectives, or aligning team work to strategy. Trigger on: "OKR", "objectives", "key results", "goal setting", "quarterly planning", "KPIs", "strategy execution", "annual planning", "team goals", "alignment", "review cadence", "what should we focus on", "prioritize", "goal tracking", "north star metric".

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Investigate Node.js bugs by pausing execution, examining variables, and stepping through code. Diagnose stalled asynchronous work and unreliable tests, or collect memory snapshots and CPU profiles to investigate growing memory use and slow execution.

• Investigate stalled asynchronous work
• Debug tests that fail inconsistently
• Examine startup timing and child processes

Guide Swift 6.2 migration and concurrency design using MainActor, which keeps related work on the main actor. Address data-race compiler errors, protocol conformance, and explicit background execution for heavy computations through @concurrent.

• Migrating existing apps to Swift 6.2
• Resolving data-race compiler errors
• Moving heavy computations into background execution

Break an RFC, a written design proposal, into work units with dependencies and acceptance tests for multiple agents. Organize research, implementation, testing, review, queued merges, and final system verification for features too large for one pass.

• Splitting a large design proposal into verifiable work units
• Assigning dependent tasks to multiple agents
• Managing review and queued merges of agent-built branches

Triage GitHub issues and pull requests, classify their next steps, and connect active work to Linear. Keeps public updates in GitHub and internal ownership and priorities in Linear, with a clear recommendation for the next action.

• Sorting a backlog of open issues and pull requests
• Deciding whether GitHub work needs a Linear task
• Linking active work to internal owners and priorities

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

• Investigating failures that appear only inside an agent application
• Tracing old conversation content leaking into new sessions
• Checking skipped or falsely claimed tool execution

Turn a codebase into an interactive graph of code relationships. Explore functions, call chains, related groups, and execution flows in a browser, with optional Cloudflare tunneling to share the explorer remotely.

• Explore the architecture of an unfamiliar codebase
• Trace function calls and execution flows
• Share an interactive code graph remotely

Turn recurring Claude Code tasks into reusable slash commands stored as Markdown files. The guidance covers arguments, file references, tool permissions, shell execution, and plugin integration so commands can run consistent, context-aware workflows.

• Turning recurring review instructions into a command
• Creating commands that accept file paths or issue numbers
• Sharing standard workflows across a project team

Review Caveman Cloud spending, latency, errors, and potential improvements using current reports and traces, or individual execution records. Produce findings tied to time windows and trace IDs, while keeping measured costs, estimated opportunities, and verified savings separate.

• Understanding which workflows drive LLM spending
• Investigating slow responses and errors
• Reviewing measured costs and verified savings separately

Review Caveman Cloud experiment states, evaluation results, and safety checks to recommend a supported next action. Identify missing or failing evidence and explain execution blocks; the current agent interface reads experiment data without changing its lifecycle state.

• Reviewing whether an experiment is ready to start
• Checking whether evidence supports experiment approval
• Assessing cancellation or rollback options

Guide a first task in Paperclip by clarifying goals, constraints, and completion criteria. Prepare a plan or one task proposal, then wait for approval before creating execution tasks or carrying out the agreed work.

• Clarifying goals for a first Paperclip task
• Developing a plan and proposed agent team
• Agreeing on the scope of one task before execution

Add or extend Paperclip end-to-end evaluations that exercise real browser workflows across the server, database, and runner. Define expected outcomes, check independent evidence, and connect results to the existing reporting pipeline for local or Daytona execution.

• Adding an evaluation for a complete user workflow
• Updating test data and outcome checks
• Including screenshots and artifacts in evaluation reports

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

Use the command line to send tasks to OmniRoute's A2A server, which supports communication between agents. Inspect execution history and logs, monitor or cancel tasks, and interactively test the JSON-RPC 2.0 communication protocol.

• Check server status and available skills
• Invoke a skill with JSON input
• Inspect task history and logs

Manage Omni Skills, plugins, and persistent memory from the command line. Find and install skills, run them with supplied input, inspect execution history, and maintain saved information alongside plugin installations.

• Finding and installing skills by category or tag
• Running skills and reviewing execution history
• Installing plugins and creating plugin scaffolds

Automate Emelia operations through Rube MCP, a connection for agent tools. The skill guides tool discovery, connection checks, and execution using current input requirements, with support for bulk operations and retrieving results across multiple pages.

• Discovering available Emelia operations
• Checking Emelia connections before execution
• Running bulk operations through available tools

Automate Emailable operations through Rube MCP, a connection to external tools. The skill guides tool discovery, connection checks, execution with current input formats, bulk operations, and retrieval of results across multiple pages.

• Find tools for an Emailable task
• Check the Emailable connection before execution
• Run bulk Emailable operations