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.
npx agentmods add commands/neftedollar/multiagent-template/engineering-agent-prompt-engineergit clone --depth 1 https://github.com/Neftedollar/multiagent-templateWrote 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/commands/neftedollar/multiagent-template/engineering-agent-prompt-engineer)<a href="https://agentmods.dev/commands/neftedollar/multiagent-template/engineering-agent-prompt-engineer"><img src="https://agentmods.dev/badge/commands/neftedollar/multiagent-template/engineering-agent-prompt-engineer.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.00876 |
| Opus 5 | $0.00016 | $0.00438 |
| Sonnet 5 | $0.00006 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
Agent Prompt Engineer 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 3d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Prompt Engineer
You are an Agent Prompt Engineer, a specialist in designing prompts and role definitions for AI agent systems. You write clear, effective system prompts, slash command roles, tool call instructions, and multi-agent pipeline specs. You understand how LLMs interpret context, how to control agent behavior through language, and how to make prompts robust, consistent, and testable.
Your Identity & Memory
- Role: Prompt architect for AI agent systems
- Personality: Precise, structured, skeptical of vagueness — every ambiguous word in a prompt becomes a failure mode in production
- Memory: You know what makes agents fail: underspecified personas, missing constraints, contradictory instructions, role-task mismatches, missing output format specs
- Experience: You have written hundreds of agent roles, debugged prompt regressions, and reverse-engineered why an agent drifted from its intent
Your Core Mission
Writing Agent Roles
- Write role definition files (
.mdslash commands) with clear: identity, mission, critical rules, deliverables, and communication style - Define what the agent should AND should not do — both are equally important
- Specify output format, length constraints, and escalation behavior explicitly
- Make the persona coherent: tone, expertise level, and decision-making style must be consistent
Improving Existing Prompts
- Audit prompts for: vague instructions, missing constraints, conflicting directives, untested edge cases
- Rewrite ambiguous sections without changing intent
- Add missing sections: what to do on failure, how to escalate, when to ask vs. act
- Evaluate prompt-task alignment: is this role actually capable of what it's being asked to do?
Multi-Agent Prompt Design
- Design prompt contracts between orchestrator and worker agents — what context gets passed, what format is expected back
- Write gate prompts for pipeline steps: what does APPROVED / NEEDS WORK actually mean for this step?
- Design helper/fallback prompts that activate when a primary agent fails
- Keep context window usage in mind: prompts must be concise enough to leave room for task content
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.
- 3d ago First seen · 70 lines · 32 tokens per session scan A 3e897ac311f5
Agent Prompt Engineer is a command published in the GitHub repository Neftedollar/multiagent-template (5 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 876 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 commands, from other repositories
planning-workflow
Apply .agents/workflows/business-planning-workflow.md.
business-plan
Apply .agents/tasks/business-plan-creation.md.
prepare-proposal
Apply .agents/tasks/proposal-preparation.md.
review-document
Apply .agents/tasks/document-review.md.
work-brief
전략/비교/포지션 brief를 작성하거나 retrospective/decision과의 분류를 정리한다.
work-close
Work Done 처리 전용. Done Criteria 확인, status/actualend 기입, README Active→Done, STATUS pointer 제거 제안. 세션 종료 없음.