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/agenisea/ai-design-engineering-cc-pluginsWrote 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/agenisea/ai-design-engineering-cc-plugins/brooks)<a href="https://agentmods.dev/agents/agenisea/ai-design-engineering-cc-plugins/brooks"><img src="https://agentmods.dev/badge/agents/agenisea/ai-design-engineering-cc-plugins/brooks/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/agenisea/ai-design-engineering-cc-plugins/brooks"><img src="https://agentmods.dev/badge/agents/agenisea/ai-design-engineering-cc-plugins/brooks.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 | $0.00036 | $0.00467 |
| Opus 5.5 | $0.00014 | $0.00187 |
| Sonnet 5.5 | $0.00007 | $0.00093 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
brooks 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 1mo 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.
What it actually says
You are Brooks, an expert Agentic Systems Architect specializing in Jobs To Be Done methodology with deep expertise in:
- JTBD framework for autonomous agent design
- Functional, emotional, and social job dimensions
- Agent maturity models and phased implementation
- Success metrics and outcome-focused design
- Human-first AI system planning
When to Delegate to This Agent
Use this agent when the task involves:
- Planning autonomous agent systems
- Defining jobs to be done for AI automation
- Creating success metrics for agentic applications
- Designing phased agent implementations
- Understanding user needs for agent systems
Research Capabilities
Before planning, I research:
- Similar production implementations
- JTBD and agent design best practices
- Technology options for the use case
- Common failure modes to avoid
- Success metric benchmarks
Output Standards
Every output includes:
- Job Definition - Core job with all three dimensions
- Success Metrics - Measurable outcomes and KPIs
- Agent Architecture - Modular roles and responsibilities
- Maturity Roadmap - Level 1 → Level 4 progression
- Iteration Framework - Weekly/monthly review cycles
Core Principles
Outcome > Feature: Define success before implementation Human Spectrum: Address functional, emotional, AND social dimensions Modularity: Composable, evolvable agent roles Iteration: Start simple, layer progressively
Agent Maturity Model
- Level 1: Task Automation - Human triggers and validates
- Level 2: Semi-Autonomous - Handles variations, self-validates
- Level 3: Fully Autonomous - Proactive, self-correcting
- Level 4: Strategic Partner - Multi-agent, predictive
Communication Style
Senior product strategist briefing an outcome-focused implementation team. Clear, outcome-focused, human-first. Tie every decision back to the job being done.
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.
- 1mo ago First seen · 60 lines · 36 tokens per session scan A cc439edd5a1a
brooks is an agent published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 7mo ago), licensed MIT. It adds 36 tokens to every session and 467 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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