“execution”的搜索结果

Load, validate, and use DACP bundles as structured execution context. Detects .bundle/ companion directories, validates integrity (manifest schema, fidelity, provenance), loads typed data, and builds ExecutionContext for receiving agents. Scripts are NEVER auto-executed. Use when processing incoming DACP bundles, interpreting .bundle/ directories alongside .msg files, validating bundle integrity, or building execution context from bundle payloads.

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Executes SAM Stage 5 — opens an attempt on a single ARTIFACT:TASK, dispatches it to a fresh stateless agent session, runs quality gates, and records an ARTIFACT:EXECUTION with implementation results and verification output on the task. Use when Stage 4 Task Decomposition is complete and tasks are ready for execution, when re-executing a task after Stage 6 returns NEEDS_WORK, or when dispatching a task to a language-appropriate specialist agent via the development harness pipeline.

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Grooms a GitHub milestone for parallel execution — batch-grooms ungroomed items, assesses scope gaps, analyzes cross-item dependencies via Impact Radius overlap, builds conflict groups, assigns items to execution waves, and persists the dispatch plan via dispatch_create_plan MCP tool. Calls dispatch_wave_start per wave to register state. Use when preparing a milestone for /work-milestone execution. Pass the milestone number as the first argument. Requires milestone items assigned via /group-items-to-milestone.

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Structures IPO execution from organizational meeting through pricing with workstream coordination and timeline management. Use when managing IPOs, coordinating offering processes, or tracking ECM execution milestones.

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Measures implementation shortfall with paper portfolio comparison, delay cost attribution, and execution quality assessment. Use when measuring implementation shortfall, analyzing execution quality, or attributing trading costs.

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Plan and freeze workflow execution by creating an Execution Contract, setting PLAN_FROZEN/CURRENT_ROLE, and updating PROJECT_STATE decisions and Top 3. Use when starting workflow delivery, re-planning after unfreeze, or when scope/constraints change before execution.

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Cross-tool decision matrix for field-update automation in Salesforce — Before-Save Flow vs After-Save Flow vs Apex Trigger vs the deprecated Workflow Rule + Field Update. Covers the recursion / re-entrancy rules, governor cost per pattern (Before-Save flow is governor-free for the same record), the order-of-execution slot each tool occupies, and the Workflow-Rule-to-Flow migration playbook for field-update actions. NOT for the full record-save order of execution — use apex/order-of-execution-deep-dive. NOT for building the record-triggered Flow once the tool is chosen — use flow/record-triggered-flow-patterns. Trigger keywords: WorkflowFieldUpdate, reevaluateOnChange, onCreateOrTriggeringUpdate, doesRequireRecordChangedToMeetCriteria, failedMigrationToolVersion, Migrate to Flow, workflow-meta.xml, field update deprecated, double writer, second trigger pass.

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Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination. Activate on 'execute dag', 'parallel execution', 'concurrent tasks', 'run workflow', 'spawn agents'. NOT for scheduling (use dag-task-scheduler) or building DAGs (use dag-graph-builder).

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Parses complex problems into DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies, identifies parallelization opportunities, and creates optimal execution plans. Activate on 'build dag', 'create workflow graph', 'decompose task', 'execution graph', 'task graph'. NOT for simple linear tasks or when an existing DAG structure is provided.

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Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).

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Task claiming and ownership tracking for parallel agent execution. Agents claim tasks from a shared queue with file ownership enforcement. Use during parallel execution phases when multiple agents work simultaneously.

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

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TanStack Pacer best practices for execution control in React — debouncing, throttling, rate limiting, queuing, and batching. Use when implementing search inputs, scroll handlers, API rate limits, task queues, bulk operations, or any scenario requiring controlled execution timing with reactive state.

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Use when working with Aws Codepipeline — aWS CodePipeline CI/CD pipeline management and execution analysis. Covers pipeline inventory, stage and action status, execution history, pipeline triggers, artifact stores, and action type configurations. Use when inspecting pipeline health, debugging failed stages, reviewing execution history, or auditing pipeline configurations.

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Use when working with Aws Data Pipeline — aWS Data Pipeline workflow management and execution analysis. Covers pipeline inventory, pipeline definitions, execution status, object status, task runner health, and pipeline scheduling. Use when inspecting data pipeline health, debugging failed executions, reviewing pipeline definitions, or auditing pipeline schedules.

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Review Jupyter notebooks for side effects, dependencies, and safe execution strategy before running or editing them.

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Securing AWS Lambda execution roles by implementing least-privilege IAM policies, applying permission boundaries, restricting resource-based policies, using IAM Access Analyzer to validate permissions, and enforcing role scoping through SCPs.

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

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Decompose multi-step work into a revisable subgoal plan with stable step IDs, checkpointing, and plan-ahead execution. Load when a task needs explicit planning before action, subgoal decomposition, plan checkpointing, or ReCAP-style plan-ahead execution. Also triggers on "structured plan", "plan with steps", "subgoal graph", "revise the plan", "checkpoint plan", or multi-step tasks where naive one-shot execution would be fragile. Skips trivial single-step tasks. Pairs with dynamic-routing on failure. Distinct from process-decomposer (registry/triage) and problem-to-plan (doc deliverables).

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Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination. Activate on 'execute dag', 'parallel execution', 'concurrent tasks', 'run workflow', 'spawn agents'. NOT for scheduling (use dag-task-scheduler) or building DAGs (use dag-graph-builder).

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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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