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piflow-overlord

Manage pi fleet workflow nodes with continue, abort, rerun, and escalate decisions.

103|1|Updated Jun 9, 2026
Search Tags:#optimization#workflow management#control-plane#telemetry analysis#pi fleet
One-click install
npx skills add https://github.com/blueif16/PiFlow --skill piflow-overlord
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: piflow-overlord
Source: https://github.com/blueif16/PiFlow/tree/main/.claude/skills/piflow-overlord
Command: npx skills add https://github.com/blueif16/PiFlow --skill piflow-overlord

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pi, piflowctl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The piflow-overlord Skill unit provides control and oversight of the pi fleet, enabling precise management and optimization of workflow runs.

Core Features & Use Cases

  • Control-Plane Agent: Manages and decides the fate of pi fleet nodes during runs, optimizes, and fixes.
  • Telemetry Stream Analysis: Observes and analyzes canonical telemetry to make informed decisions.
  • Decision Making: Offers decisions like continue, abort, rerun, nudge, escalate, and land based on observed data.
  • Use Case: Supervise a live run, optimize pass, or fix loop, making critical decisions to ensure the successful execution of the workflow.

Quick Start

Load the piflow-overlord skill to control a live run and make decisions based on the observed data.

Frequently Asked Questions about piflow-overlord

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I monitor and control workflow nodes during a live pi fleet run?▼

To manage pi fleet workflows, load the control-plane skill which supervises live runs by analyzing telemetry and issuing decisions like continue, abort, rerun, nudge, escalate, or land to control node fate.

What is control-plane telemetry analysis for large-scale workflow orchestration?▼

Control-plane telemetry analysis for large-scale workflow orchestration is the process of observing canonical telemetry data from fleet nodes to make informed, real-time decisions about whether to continue, abort, rerun, or escalate workflow runs.

Do I need pi and piflowctl to run workflow optimization and oversight?▼

Yes, you need the pi runtime and piflowctl installed to execute and manage the control-plane functionality required for workflow optimization and fleet oversight.

When should I use automated decision-making for workflow management?▼

You should use automated decision-making for workflow management when running large-scale orchestration where real-time oversight is crucial, such as supervising live runs, optimization passes, or fix loops to ensure successful execution.

What's the best way to handle failing nodes during an optimization pass?▼

The best way to handle failing nodes during an optimization pass is to use a control-plane agent that analyzes telemetry and applies decisions like rerun, nudge, escalate, or land to fix and optimize the workflow in real time.

Can I abort or rerun specific nodes in a pi fleet workflow based on telemetry?▼

Yes, you can abort or rerun specific nodes in a pi fleet workflow by using the control-plane agent to analyze telemetry streams and issue targeted decisions like abort, rerun, nudge, or escalate based on the observed data.

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