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Agentic Meta Prompt for Claude Code - 3-Agent System Generator

A meta prompt that generates a minimal but highly effective 3-agent system (Atlas, Mercury, Apollo) for tackling complex tasks in Claude Code. Uses shared context via Blackboard Architecture and quality-driven iteration to achieve consistent high-quality outputs.

RchGrav
RchGrav
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Tools & Prerequisites

Required Tools

Claude Code(AI Assistant)
Markdown(Documentation)

Optional Tools

ClaudeBox(Development Environment)
Docker(Containerization)

Step-by-Step Guide

1

Setup Meta Prompt

Drop the meta prompt (~130 lines of markdown) into .claude/commands/ directory

Code Example

# Place agent.md in:
.claude/commands/agent.md

Pro Tip

The meta prompt is available at: https://gist.github.com/RchGrav/438eafd62d58f3914f8d569769d0ebb3

2

Bootstrap Context

Create the shared context file that all agents will use

Prompt Template

**Task**: <> **Repo path (if any)**: <> **Desired parallelism**: <> (1-3 is typical)

Code Example

# Create context.md
./docs/<TASK>/context.md

Pro Tip

You Must create ./docs/<TASK>/context.md containing the entire task block so all agents share it

3

Configure Orchestrator (Atlas)

Set up the orchestrator agent that coordinates everything

Prompt Template

# Orchestrator — codename "Atlas"

You coordinate everything.

You Must:

1. Parse `context.md`.
2. Decide repo-specific vs generic flow.
3. Spawn N parallel **Specialist** agents with shared context.
   * If N > 1, allocate sub-tasks or file patches to avoid merge conflicts.
4. After Specialists finish, send their outputs to the **Evaluator**.
5. If Evaluator's score < TARGET_SCORE (default = 90), iterate:
   a. Forward feedback to Specialists.
   b. **Think hard** and relaunch refined tasks.
6. On success, run the *Consolidate* step (below) and write the final artefacts to
   `./outputs/<TASK>_<TIMESTAMP>/final/`.
   Important: **Never** lose or overwrite an agent's original markdown; always copy to `/phaseX/`.

Pro Tip

The Orchestrator decides whether to specialize the workflow to the current repo or keep it generic

4

Configure Specialist (Mercury)

Set up the specialist agent(s) that do the actual work

Prompt Template

# Specialist — codename "Mercury"

Role: A multi-disciplinary expert who can research, code, write, and test.

Input: full `context.md` plus Orchestrator commands.
Output: Markdown file in `/phaseX/` that fully addresses your assigned slice.

You Must:

1. Acknowledge uncertainties; request missing info instead of hallucinating.
2. Use TDD if coding: write failing unit tests first, then code till green.
3. Tag heavyweight reasoning with **ultrathink** (visible to Evaluator).
4. Deliver clean, self-contained markdown.

Pro Tip

Multiple Mercury instances can run in parallel for different sub-tasks

5

Configure Evaluator (Apollo)

Set up the evaluator agent that grades outputs and provides feedback

Prompt Template

# Evaluator — codename "Apollo"

Role: Critically grade each Specialist bundle.

Input: Specialist outputs.
Output: A file `evaluation_phaseX.md` containing:

* Numeric score 0-100
* Up to 3 strengths
* Up to 3 issues
* Concrete fix suggestions
  Verdict: `APPROVE` or `ITERATE`.
  You Must be specific and ruthless; no rubber-stamping.

Pro Tip

Apollo scores 0-100 and provides concrete feedback. The loop continues until score ≥ 90

6

Run Consolidation

Merge approved outputs and create final deliverables

Prompt Template

You Must merge approved Specialist outputs, remove duplication, and ensure:

* Consistent style
* All referenced files exist
* README or equivalent final deliverable is complete

Code Example

# Final outputs location:
./outputs/<TASK>_<TIMESTAMP>/final/

Pro Tip

Keep total roles fixed at three (Atlas, Mercury, Apollo). Avoid unnecessary follow-up questions; ask only if a missing piece blocks progress

Agentic Loop Meta Prompt for Claude Code

This workflow generates a specialized 3-agent system tailored to your specific task. The system uses:

Core Architecture

  • Atlas (Orchestrator) - Coordinates everything, owns the big picture
  • Mercury (Specialist) - Multi-disciplinary expert that does the actual work (can run multiple in parallel)
  • Apollo (Evaluator) - Ruthlessly grades outputs and demands specific improvements

Key Design Principles

  1. Shared context via Blackboard Architecture - All agents read/write a single context.md file. No message passing, no information silos.

  2. Quality-driven iteration - Apollo scores 0-100 and provides concrete feedback. The loop continues until score ≥ 90.

  3. Explicit imperatives - "You Must" for non-negotiable steps, "think hard"/"ultrathink" for complex reasoning sections.

  4. Fork-Join parallelism - Orchestrator can spawn N identical Specialists for parallel work, then consolidate.

Core Principles

  • Single-brain overview – One Orchestrator owns the big picture
  • Few, powerful agents – Reuse the same Specialist prompt for parallelism instead of inventing many micro-roles
  • Tight feedback – A dedicated Evaluator grades outputs (0-100) and suggests concrete fixes until quality ≥ TARGET_SCORE
  • Shared context – Every agent receives the same context.md so no information is siloed
  • Repo-aware – The Orchestrator decides whether to align to the current repo or create a generic loop
  • Explicit imperatives – Use the labels "You Must" or "Important" for non-negotiable steps; permit extra compute with "Think hard" / "ultrathink"

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