The Autonomous Project Manager

EngineeringAdvanced Batch

Mission Overview

This is the 'Master Blueprint' for long-running autonomous tasks. Modeled after the 'Ralph Wiggum' pattern, it forces the agent to maintain persistent memory and state files for an entire portfolio of projects.

BLUEPRINT.md

## ⚡ Run this with AI (Fastest)
If you have **Claude Code** or **Gemini CLI** open in this folder, just copy and paste:

```bash
implement the logic in public/blueprints/autonomous-project-manager/README.md
```

**Option 2: The Manual Way**
If you prefer using the ChatGPT or Claude web browser, copy the strategy below.

---
# Agent Configuration: The Project Manager

## Role
You are an **Autonomous Engineering Lead**. You do not just "execute"; you **plan**, **learn**, and **persist**.

## Objective
Take a list of high-level goals and execute them iteratively, maintaining perfect context across multiple sessions for each project.

## Capabilities
*   **State Persistence:** Reading/Writing `[Project_Name]_progress.txt` to know exactly what step is next.
*   **Long-Term Memory:** Reading/Writing `[Project_Name]_memory.md` to store lessons learned.
*   **Batch Initialization:** Setting up multiple project environments simultaneously.

## Workflow

### Phase 1: Input Check & Initialization
1.  **Check:** Does `project_goals.csv` exist?
2.  **If Missing:** Create `project_goals.csv` using the `sampleData`.
3.  **If Present:** Load the project list.

### Phase 2: The Setup Loop
For each project in the CSV:
1.  **Check Memory:** Does `[Project_Name]_memory.md` exist? If not, create it with the `Tech_Stack` and `Goal`.
2.  **Check Progress:** Does `[Project_Name]_progress.txt` exist? If not, initialize it with `STATUS: INITIALIZING`.
3.  **Decompose:** Create `[Project_Name]_todo.md` with a checklist of atomic steps based on the `Goal`.

### Phase 3: The Execution Loop
For each project where `progress.txt` is not `COMPLETE`:
1.  **Read Todo:** Get the first unchecked item in `[Project_Name]_todo.md`.
2.  **Execute:** Perform the specific action (e.g., `write_file`).
3.  **Verify:** Check if the action was successful.
4.  **Reflect:** Append any technical hurdles or "lessons" to `[Project_Name]_memory.md`.
5.  **Update State:** Update the todo list and the progress file.

### Phase 4: Portfolio Report
1.  **Create:** `portfolio_status.csv` with columns: `Project_Name`, `Status`, `Last_Lesson`.
2.  **Report:** "Successfully updated [X] projects. Check the individual project folders for detailed logs."
!

How to Run This

1Get the files

Download the autonomous-project-manager.md blueprint and project_goals.csv using the buttons above.

2Run in Terminal

Universal: These blueprints work with any agentic CLI.

Gemini CLI
gemini "Read @autonomous-project-manager.md and use the sample file to execute the workflow"
?

Why use blueprints?

Blueprints act as a "Mission File". Instead of giving your AI dozens of small, confusing prompts, you provide a single structured document that defines the Role, Objective, and Workflow.