Skill: parallel-subtask-scheduling
1. Capability Definition & Real Case
- Professional Definition: Parallel subtask scheduling is the capability to detect which subtasks are independent enough to be executed concurrently, dispatch them without violating dependency constraints, and then rejoin their outputs into a coherent downstream plan. This includes the dual ability to avoid false parallelism when dependencies exist and to avoid unnecessary serialization when independence exists. For orchestration agents, it is the core efficiency lever that separates mere multi-step execution from real coordination.
- Dimension Hierarchy: Workflow Orchestration->Dependency and Schedule Management->parallel-subtask-scheduling
Real Case
Extract 1 to 3 concrete cases from the actual benchmark papers to demonstrate this capability, aiming for a maximum of 3 cases. CRITICAL HANDLING FOR TEMPLATES: If the paper provides concrete examples, extract them directly. However, if the paper ONLY provides abstract templates, you MUST NOT just output the template. Instead, you MUST instantiate the template into a highly specific, realistic, and logically coherent example according to the papers. Based on the papers, invent a specific entity, a specific environment, a concrete trajectory, and a final answer that perfectly perfectly reflects the benchmark's exact intent and difficulty.
Do NOT state that they are extracted from papers or benchmarks, and absolutely do NOT mention or reference the source papers.
[Case 1]
- Initial Environment: A question-answering environment exposes a Wikipedia-style search tool and a final aggregation channel. The user asks whether two people share the same nationality, and the answer can be reached only after independently retrieving information about both entities. Nothing in the task requires the two searches to happen in sequence.
- Real Question: Were Scott Derrickson and Ed Wood of the same nationality?
- Real Trajectory: The planner generates two independent search subtasks, dispatches them concurrently, waits for both search results, and only then runs the aggregation step that compares the returned nationalities and emits the final answer. It does not serialize the searches or repeatedly loop over the same entity once enough evidence is already available.
- Real Answer: Yes.
- Why this demonstrates the capability: This is a minimal but clean scheduling case because the orchestration challenge is to see the latent parallelism in the dependency graph. The answer is easy once both facts are available, but the benchmark differentiates agents that coordinate parallel work from agents that reason and act one step at a time. It therefore measures scheduling intelligence rather than raw factual knowledge.
[Case 2]
- Initial Environment: A function-calling environment contains search and math tools, and the user asks a factual-arithmetic question whose graph contains both parallel leaves and dependent aggregation nodes. The environment rewards agents that plan the entire graph rather than expanding it greedily one action at a time. The tool outputs are stable and machine-readable, so the key challenge is orchestration structure.
- Real Question: How much does Microsoft’s market capitalization need to increase to exceed Apple’s market capitalization?
- Real Trajectory: The planner emits two search tasks in parallel to retrieve the current values for Microsoft and Apple, then schedules the dependent math operation only after both leaves have completed, and finally returns the computed gap. If intermediate assumptions are wrong or missing, the planner can regenerate the downstream stage without rerunning every completed leaf task.
- Why this demonstrates the capability: This case tests the coordinator’s ability to combine parallel dispatch with dependency-respecting joins. The valuable behavior is not simply “use search twice,” but “recognize that both searches can run at the same time and that the math node must wait for both parents.” That is exactly the type of schedule construction an orchestration agent must perform over specialized workers.
Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:
Phase 1: Environment Exploration
Read the exploration guidelines to discover raw knowledge seeds:
references/EXPLORATION.md
Phase 2: Trajectory Selection
Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
references/SELECTION.md
Phase 3: Data Synthesis
Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
references/SYNTHESIS.md
1---2name: parallel-subtask-scheduling-23description: Skill: parallel-subtask-scheduling4---56# Skill: parallel-subtask-scheduling7## 1. Capability Definition & Real Case8* **Professional Definition**: Parallel subtask scheduling is the capability to detect which subtasks are independent enough to be executed concurrently, dispatch them without violating dependency constraints, and then rejoin their outputs into a coherent downstream plan. This includes the dual ability to avoid false parallelism when dependencies exist and to avoid unnecessary serialization when independence exists. For orchestration agents, it is the core efficiency lever that separates mere multi-step execution from real coordination.9* **Dimension Hierarchy**: Workflow Orchestration->Dependency and Schedule Management->parallel-subtask-scheduling10### Real Case11*Extract 1 to 3 concrete cases from the actual benchmark papers to demonstrate this capability, aiming for a maximum of 3 cases. CRITICAL HANDLING FOR TEMPLATES: If the paper provides concrete examples, extract them directly. However, if the paper ONLY provides abstract templates, you MUST NOT just output the template. Instead, you MUST instantiate the template into a highly specific, realistic, and logically coherent example according to the papers. Based on the papers, invent a specific entity, a specific environment, a concrete trajectory, and a final answer that perfectly perfectly reflects the benchmark's exact intent and difficulty.12Do NOT state that they are extracted from papers or benchmarks, and absolutely do NOT mention or reference the source papers.*1314**[Case 1]**15* **Initial Environment**: A question-answering environment exposes a Wikipedia-style search tool and a final aggregation channel. The user asks whether two people share the same nationality, and the answer can be reached only after independently retrieving information about both entities. Nothing in the task requires the two searches to happen in sequence.16* **Real Question**: Were Scott Derrickson and Ed Wood of the same nationality?17* **Real Trajectory**: The planner generates two independent search subtasks, dispatches them concurrently, waits for both search results, and only then runs the aggregation step that compares the returned nationalities and emits the final answer. It does not serialize the searches or repeatedly loop over the same entity once enough evidence is already available.18* **Real Answer**: Yes.19* **Why this demonstrates the capability**: This is a minimal but clean scheduling case because the orchestration challenge is to see the latent parallelism in the dependency graph. The answer is easy once both facts are available, but the benchmark differentiates agents that coordinate parallel work from agents that reason and act one step at a time. It therefore measures scheduling intelligence rather than raw factual knowledge.2021---22**[Case 2]**23* **Initial Environment**: A function-calling environment contains search and math tools, and the user asks a factual-arithmetic question whose graph contains both parallel leaves and dependent aggregation nodes. The environment rewards agents that plan the entire graph rather than expanding it greedily one action at a time. The tool outputs are stable and machine-readable, so the key challenge is orchestration structure.24* **Real Question**: How much does Microsoft’s market capitalization need to increase to exceed Apple’s market capitalization?25* **Real Trajectory**: The planner emits two search tasks in parallel to retrieve the current values for Microsoft and Apple, then schedules the dependent math operation only after both leaves have completed, and finally returns the computed gap. If intermediate assumptions are wrong or missing, the planner can regenerate the downstream stage without rerunning every completed leaf task.26* **Why this demonstrates the capability**: This case tests the coordinator’s ability to combine parallel dispatch with dependency-respecting joins. The valuable behavior is not simply “use search twice,” but “recognize that both searches can run at the same time and that the math node must wait for both parents.” That is exactly the type of schedule construction an orchestration agent must perform over specialized workers.2728## Pipeline Execution Instructions29To synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:30311. **Phase 1: Environment Exploration**32 Read the exploration guidelines to discover raw knowledge seeds:33 `references/EXPLORATION.md`34352. **Phase 2: Trajectory Selection**36 Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:37 `references/SELECTION.md`38393. **Phase 3: Data Synthesis**40 Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:41 `references/SYNTHESIS.md`
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