Task Decomposition

SkillMedia

Breaking complex user goals into subtasks that agents can handle.

Available today. Use it from your connected AI after setup.

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