Task Decomposition
SkillMediaBreaking complex user goals into subtasks that agents can handle.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Task Decomposition skill
What this skill tells your AI
The instructions your AI receives, as published by owl-listener/ai-design-skills in skills/design-agent-orchestration/task-decomposition/SKILL.md and read by ahel’s review.
Users come with goals, not subtasks. Task decomposition is how a multi-agent system breaks a complex user goal into pieces that individual agents can handle — and then reassembles the results into something coherent.
Decomposition Strategies
- Sequential decomposition: Break the goal into ordered steps. Step 1 must complete before Step 2 starts.
- Parallel decomposition: Break the goal into independent parts that can be worked on simultaneously.
- Hierarchical decomposition: Break the goal into sub-goals, then break each sub-goal into tasks.
- Conditional decomposition: The next step depends on the result of the current step. Different results lead to different paths.
- Iterative decomposition: Start with a rough version, then refine through multiple passes.
Designing Decomposition Rules
For each type of user goal the system handles:
- What's the entry point? How does the system receive the goal?
- What are the subtasks? List all possible subtasks for this goal type.
- What are the dependencies? Which subtasks depend on others' outputs?
- What's the critical path? Which sequence of subtasks determines the minimum completion time?
- What can be parallelised? Which subtasks can run simultaneously?
- What's the reassembly logic? How do subtask results combine into the final output?
Granularity
How finely to decompose matters:
- Too coarse: Single agents get tasks that are too complex, leading to lower quality
- Too fine: Overhead from handoffs exceeds the benefit of specialisation
- Just right: Each subtask matches one agent's sweet spot in terms of scope and complexity
Handling Ambiguity
User goals are often ambiguous. The system needs to:
- Clarify before decomposing: Ask the user to specify when the goal is unclear
- Decompose tentatively: Start with a plan and adjust as information emerges
- Recompose when needed: If decomposition was wrong, restructure without starting over
Design Artefacts
- Task decomposition trees for each goal type
- Dependency maps showing subtask relationships
- Parallelisation specifications
- Reassembly logic definitions
- Ambiguity handling protocols
Signals
- GitHub stars
- 173
- Forks
- 33
- Last commit
- Jun 2026
Advanced
- Catalog kind
- skill
- Gateway key
task-decomposition-owl-listener- Source
- github.com/owl-listener/ai-design-skills