agent-task-splitter

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Use when a goal needs multiple bounded agent roles, parallel or dependent task packets, explicit scope partitions, and a provider-neutral DAG. Not for research-domain routing that owns .research, .paper, Zotero, Obsidian, or NotebookLM workflows; use research-hub-multi-ai for those.

AI & Automation 26 stars 6 forks Updated 6 days ago MIT

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Skill Content

# agent-task-splitter Turn one approved goal into a provider-neutral role DAG and bounded task packets. This skill plans; it does not spawn agents. Read references/task_splitter_heuristics.md when role selection or DAG shape is not obvious. Read ../../docs/public-harness-contract.md for schemas and artifact policy. ## Roles - primary-agent: owns scope, architecture, and human communication. - delegated-executor: performs bounded implementation or mechanical work. - reviewer: independently tests and judges a stable candidate. - synthesizer: structures completed inputs without reopening discovery. The host chooses the adapter for each role. Do not put provider or model names in the public role field. ## Use this skill when - Two or more independent task packets can run in parallel. - Implementation and independent review must be separate. - A fan-out/fan-in or diamond DAG materially shortens the critical path. - Several results require explicit reconciliation and acceptance. Do not split exploratory debugging before the cause is known. Do not split a small coherent change merely to create agent activity. ## Inputs - Goal and authorized scope. - Success criteria. - Files, systems, and external actions in/out of scope. - Available policy_ref and checkpoint_ref when any child may be spawned. - Existing recorded human decisions and evidence refs. If scope or acceptance is ambiguous, ask one focused question. Do not invent permission for external writes. ## Workflow 1. ...

Details

Author
WenyuChiou
Repository
WenyuChiou/agent-collab-skills
Created
4 months ago
Last Updated
6 days ago
Language
Python
License
MIT

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