---
name: dynamic-workflows
description: "Instructions for the upstream workflow: Orchestration patterns for efficient agents — classify and act, fan out and synthesize, adversarial verification, generate and filter, tournament — each addressing one of three failure modes: agent laziness (declares more than it does), self-preference (judging its own work) and goal drift (losing the objective in a long session). Use when designing or debugging a multi-agent workflow rather than a single call. Not for the retroactive-loop controller — use loop-optimizer; not for the parallel review of one diff — use simplify-code; not for delegating a task — use the agent delegation surface."
license: MIT
metadata:
  version: "1.0.0"
  domain: agent-skills
  canonical-name: dynamic-workflows
---
# Dynamic Workflows — patterns for efficient agents

## Package scope and prerequisites

This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.

## Purpose

A catalogue of composition patterns, each mapped to the failure mode it fixes.
The underlying idea is a harness per task: the shape of the orchestration should
match the task, not be fixed for everything.

The full upstream pattern catalog is in `PATTERNS.md`, which is not included here. Obtain it from the upstream project before claiming to apply its complete procedure; if absent, explain that limitation.

## When NOT to use

- You need the loop controller and its stop reasons — use loop_optimizer.
- You need one diff reviewed by several narrow reviewers — use simplify-code.