Task Decomposition
Breaking complex user goals into subtasks that agents can handle.
Install
$ agentstack add skill-owl-listener-ai-design-skills-task-decomposition ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-owl-listener-ai-design-skills-task-decomposition) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Task Decomposition
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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Owl-Listener
- Source: Owl-Listener/ai-design-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.