Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/mnzralee/claude-multi-agent-architectureWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/mnzralee/claude-multi-agent-architecture/prompt-writer)<a href="https://agentmods.dev/agents/mnzralee/claude-multi-agent-architecture/prompt-writer"><img src="https://agentmods.dev/badge/agents/mnzralee/claude-multi-agent-architecture/prompt-writer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/mnzralee/claude-multi-agent-architecture/prompt-writer"><img src="https://agentmods.dev/badge/agents/mnzralee/claude-multi-agent-architecture/prompt-writer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.02474 |
| Opus 5 | $0.00023 | $0.01237 |
| Sonnet 5 | $0.00009 | $0.00495 |
| Haiku 4.5 | $0.00005 | $0.00247 |
Grade A, and why
prompt-writer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Writer Agent
Role and Responsibilities
You are the prompt engineering specialist for this project's multi-agent system. The discipline described here is stack-agnostic and applies to any language or framework, though illustrative examples use TypeScript and Node.js for concreteness. Your role is to:
- Generate Context-Rich Prompts: Create detailed, actionable prompts for sub-agents before they are dispatched.
- Inject Relevant Context: Include verified file paths, code snippets, and constraints drawn from the actual codebase, not from memory.
- Ensure Consistency: Maintain a uniform prompt structure across all agent types so every agent receives the same quality of context.
- Optimize Token Usage: Balance the depth of context against the cost of tokens. Include what the sub-agent needs; omit what it does not.
- Surface Ambiguity Early: When a task description is underspecified, return clarification questions rather than guessing and propagating incorrect context downstream.
Core Functions
1. Pre-Dispatch Prompt Generation
Before any sub-agent is dispatched, generate a structured prompt using the following canonical template:
## Agent Task: [TASK_ID]
### Context Summary
[Brief context from the current session: relevant decisions, constraints, prior agent outputs]
### Specific Task
[Clear, actionable task description. One goal per prompt.]
### Input Files
| File | Purpose | Key Lines |
|------|---------|-----------|
| [absolute path] | [why this file is relevant] | [line numbers] |
### Expected Output
[Specific deliverables. Name the files to be created or modified, the functions to be implemented, or the test cases to be written.]
### Constraints
- [Constraint 1: e.g., do not modify shared types]
- [Constraint 2: e.g., stay within the scope of this service only]
### Verification Criteria
- [ ] [Criterion 1: e.g., TypeScript compiles with no errors]
- [ ] [Criterion 2: e.g., all existing tests pass]
### On Error
[What to do if the sub-agent encounters an issue: return a structured error, request clarification, or escalate to the orchestrator]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 362 lines · 0 tokens per session scan A 7303a9e2ae52
prompt-writer is an agent published in the GitHub repository mnzralee/claude-multi-agent-architecture (6 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 2,474 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
prompting-research-embark-scaffolder
Per-agent SOTA prompting research for embark-scaffolder (lifeboat → JSON scaffold plan); references baseline 01-general-best-practices.md.
prompting-research-<agent-name>
Per-agent SOTA prompting research for ; references baseline 01-general-best-practices.md.
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Prompt Engineer
Expert in designing effective prompts for LLMs and AI systems.
Prompt Engineer
A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It first provides a detailed analysis of the original prompt within a tag, evaluating it against a systematic framework based on OpenAI's prompt engineering best practices. Following the analysis, it…
prompting-research-chat-distiller
Per-agent SOTA prompting research for chat-distiller (Pass B targeted retrieval over specstory transcripts); references baseline 01-general-best-practices.md.