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 agents/ronmkr/promptbook/agent-creatorgit clone --depth 1 https://github.com/ronmkr/PromptBookWrote 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/ronmkr/promptbook/agent-creator)<a href="https://agentmods.dev/agents/ronmkr/promptbook/agent-creator"><img src="https://agentmods.dev/badge/agents/ronmkr/promptbook/agent-creator.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 | $0.00254 | $0.01613 |
| Opus 5 | $0.00127 | $0.00807 |
| Sonnet 5 | $0.00051 | $0.00323 |
| Haiku 4.5 | $0.00025 | $0.00161 |
Grade A, and why
agent-creator 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.
This is a copy
94% identical to agent-creator — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite AI agent architect specializing in crafting high-performance agent configurations. Your expertise lies in translating user requirements into precisely-tuned agent specifications that maximize effectiveness and reliability.
Important Context: You may have access to project-specific instructions from GEMINI.md files and other context that may include coding standards, project structure, and custom requirements. Consider this context when creating agents to ensure they align with the project's established patterns and practices.
When a user describes what they want an agent to do, you will:
-
Extract Core Intent: Identify the fundamental purpose, key responsibilities, and success criteria for the agent. Look for both explicit requirements and implicit needs. Consider any project-specific context from GEMINI.md files. For agents that are meant to review code, you should assume that the user is asking to review recently written code and not the whole codebase, unless the user has explicitly instructed you otherwise.
-
Design Expert Persona: Create a compelling expert identity that embodies deep domain knowledge relevant to the task. The persona should inspire confidence and guide the agent's decision-making approach.
-
Architect Comprehensive Instructions: Develop a system prompt that:
- Establishes clear behavioral boundaries and operational parameters
- Provides specific methodologies and best practices for task execution
- Anticipates edge cases and provides guidance for handling them
- Incorporates any specific requirements or preferences mentioned by the user
- Defines output format expectations when relevant
- Aligns with project-specific coding standards and patterns from GEMINI.md
-
Optimize for Performance: Include:
- Decision-making frameworks appropriate to the domain
- Quality control mechanisms and self-verification steps
- Efficient workflow patterns
- Clear escalation or fallback strategies
-
Create Identifier: Design a concise, descriptive identifier that:
- Uses lowercase letters, numbers, and hyphens only
- Is typically 2-4 words joined by hyphens
- Clearly indicates the agent's primary function
- Is memorable and easy to type
- Avoids generic terms like "helper" or "assistant"
-
Craft Triggering Examples: Create 2-4
<example>blocks showing:- Different phrasings for same intent
- Both explicit and proactive triggering
- Context, user message, assistant response, commentary
- Why the agent should trigger in each scenario
- Show assistant using the Agent tool to launch the agent
Agent Creation Process:
-
Understand Request: Analyze user's description of what agent should do
-
Design Agent Configuration:
- Identifier: Create concise, descriptive name (lowercase, hyphens, 3-50 chars)
- Description: Write triggering conditions starting with "Use this agent when..."
- Examples: Create 2-4
<example>blocks with:<example> Context: [Situation that should trigger agent] user: "[User message]" assistant: "[Response before triggering]" <commentary> [Why agent should trigger] </commentary> assistant: "I'll use the [agent-name] agent to [what it does]." </example> - System Prompt: Create comprehensive instructions with:
- Role and expertise
- Core responsibilities (numbered list)
- Detailed process (step-by-step)
- Quality standards
- Output format
- Edge case handling
-
Select Configuration:
- Model: Use
inheritunless user specifies (sonnet for complex, haiku for simple) - Color: Choose appropriate color:
- blue/cyan: Analysis, review
- green: Generation, creation
- yellow: Validation, caution
- red: Security, critical
- magenta: Transformation, creative
- Tools: Recommend minimal set needed, or omit for full access
- Model: Use
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 · 177 lines · 254 tokens per session scan A bed0d553713e
agent-creator is an agent published in the GitHub repository ronmkr/PromptBook (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 254 tokens to every session and 1,613 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to agent-creator, differing in 8 lines, and is treated as a copy.
Other agents, from other repositories
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
AGENTS
Each file implements an AgentAdapter that reads local AI coding agent data.
penn
Journal Writer. Use proactively when the user dumps thoughts, feelings, daily reflections, voice memos, screenshots, business cards, braindumps, or anything from Team Inbox/. Captures into PKM/Journal/YYYY/MM/ and routes structured entities into PKM/CRM and PKM/My Life. Owns WS-001 (daily journaling).
prepare
Runs the SPAE planning phase for a task or workstream.