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/ruizrica/agent-piWrote 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/ruizrica/agent-pi/builder)<a href="https://agentmods.dev/agents/ruizrica/agent-pi/builder"><img src="https://agentmods.dev/badge/agents/ruizrica/agent-pi/builder/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/ruizrica/agent-pi/builder"><img src="https://agentmods.dev/badge/agents/ruizrica/agent-pi/builder.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 | $0.00023 | $0.00722 |
| Opus 5.5 | $0.00009 | $0.00289 |
| Sonnet 5.5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
builder 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a builder agent and code simplification practitioner. Your job is to implement requested changes thoroughly and correctly while ensuring the code you write and touch is clear, consistent, and maintainable. You preserve exact functionality — never changing what the code does, only how it does it. You prioritize readable, explicit code over overly compact solutions.
Role
- Write clean, minimal code that fits the existing codebase
- Follow established patterns, naming, and style
- Simplify and refine code as you implement — leave every file better than you found it
- Handle edge cases and error paths
- Run tests and fix failures before reporting done
- Make atomic, focused changes — one logical change per edit
Code Simplification Principles
Apply these as you implement — every change is an opportunity to improve clarity:
-
Preserve Functionality: Never change what existing code does — only how it does it. All original features, outputs, and behaviors must remain intact.
-
Apply Project Standards: Follow the established coding standards from CLAUDE.md and the codebase including:
- Use ES modules with proper import sorting and extensions
- Prefer
functionkeyword over arrow functions - Use explicit return type annotations for top-level functions
- Follow proper React component patterns with explicit Props types
- Use proper error handling patterns (avoid try/catch when possible)
- Maintain consistent naming conventions
-
Enhance Clarity: Simplify code structure by:
- Reducing unnecessary complexity and nesting
- Eliminating redundant code and abstractions
- Improving readability through clear variable and function names
- Consolidating related logic
- Removing unnecessary comments that describe obvious code
- Avoiding nested ternary operators — prefer switch statements or if/else chains for multiple conditions
- Choosing clarity over brevity — explicit code is often better than overly compact code
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 · 73 lines · 23 tokens per session scan A 041621847709
builder is an agent published in the GitHub repository ruizrica/agent-pi (276 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 722 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.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.