Dispatch work items to polecat workers across recoverable bursts.
npx skills add https://github.com/nicsuzor/academicOps --skill burst-supervisor
Please help me install this Agent Skill. Skill: burst-supervisor Source: https://github.com/nicsuzor/academicOps/tree/main/aops-core/skills/burst-supervisor Command: npx skills add https://github.com/nicsuzor/academicOps --skill burst-supervisor
This Skill manages long-running, iterative workflows by dispatching work items to workers in batches, ensuring that progress is saved and can be resumed even after interruptions.
Initialize a new supervisor for a spec audit workflow by running: /burst-supervisor init spec-audit
High-intent search queries and answers about installing and using this skill.
Long-running iterative workflows are managed by dispatching work items to polecat workers in batches and maintaining recoverable state, ensuring progress is saved and resumable after interruptions. The supervisor evaluates output semantically to accept, revise, or fail tasks.
Automated document review and data extraction are handled by dispatching items to workers in manageable bursts, semantically evaluating their output against defined criteria, and routing results for acceptance, revision, or failure based on the evaluation.
Iterative workflow supervision is the process of dispatching batch processing tasks to workers and evaluating their output across multiple bursts. It is needed for long-running automation tasks requiring state management, automated evaluation, and state recovery after interruptions.
Yes, you can audit a large set of API specifications by dispatching each spec to a worker for review, evaluating the output against defined criteria, and then accepting, sending back for revision, or escalating fundamentally flawed tasks when retries are exhausted.
To start an iterative workflow supervisor for a spec audit, initialize a new supervisor instance using the init command with your workflow name, which sets up the environment for dispatching and evaluating work items in bursts.
When a worker's output fails semantic evaluation against defined criteria, the supervisor sends the task back for revision with specific feedback, or escalates it if the output is fundamentally flawed or retries are exhausted.
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