assemble

Create a persistent learning team with roles, file ownership, and shared memory.

Updated Feb 20, 2026
One-click install
npx skills add https://github.com/supertyrelle/pelley --skill assemble-supertyrelle
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: assemble
Source: https://github.com/supertyrelle/pelley/tree/main/skills/assemble
Command: npx skills add https://github.com/supertyrelle/pelley --skill assemble-supertyrelle

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Assemble turns ad-hoc agent use into a persistent team structure so long-running projects keep their roles, ownership, and accumulated learnings across sessions.

Core Features & Use Cases

  • Role and Ownership Planning: Proposes a small team with clear responsibilities and file ownership patterns.
  • Persistent Learnings: Creates per-member learnings files so each agent can retain project knowledge over time.
  • Shared Team Memory: Initializes common decisions and retrospective history for the whole team.
  • Use Case: Start a new codebase, organize a growing product effort, or upgrade a one-off agent workflow into a structured multi-agent operating model.

Quick Start

Use the assemble skill to create a persistent learning team for my project description and propose the initial roles, ownership, and setup files.

Frequently Asked Questions about assemble

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

FAQPage Schema
How do I set up persistent memory for multi-agent workflows on long-running projects?

Persistent memory for multi-agent workflows is created by initializing shared team memory files and per-member learnings files, ensuring accumulated project knowledge and decisions are retained across sessions.

What is the best way to assign file ownership and roles in a multi-agent development team?

Assigning file ownership and roles in a multi-agent team requires generating a structured team manifest that proposes a small team with clear responsibilities and specific file ownership patterns.

How do I coordinate long-horizon software projects across multiple sessions with multiple agents?

Long-horizon software projects are coordinated across sessions by creating a persistent learning team with shared memory, backlog tracking, and seeded learnings files to maintain cross-session continuity.

Can I upgrade an ad-hoc agent workflow into a structured multi-agent operating model?

You can upgrade an ad-hoc agent workflow into a structured multi-agent operating model by applying a repeatable setup that initializes team manifests, shared decisions, and retrospective history.

When do I need a structured team manifest for software engineering agents?

A structured team manifest is needed when starting a new codebase or growing a product effort that requires clear roles, cross-session coordination, and persistent learnings for multiple agents.

Does setting up a persistent learning team require pre-existing backlog tracking files?

Setting up a persistent learning team does not require pre-existing backlog tracking files because the repeatable setup process initializes the backlog tracking alongside the team members and shared memory.