Borrowing it
Nothing to install: this file belongs to mindroom-ai/mindroom. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mindroom-ai/mindroom/main/.claude/agents/configfield-generator.mdgit clone --depth 1 https://github.com/mindroom-ai/mindroomWrote 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/mindroom-ai/mindroom/configfield-generator)<a href="https://agentmods.dev/agents/mindroom-ai/mindroom/configfield-generator"><img src="https://agentmods.dev/badge/agents/mindroom-ai/mindroom/configfield-generator/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/mindroom-ai/mindroom/configfield-generator"><img src="https://agentmods.dev/badge/agents/mindroom-ai/mindroom/configfield-generator.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 | $0.00030 | $0.01250 |
| Opus 5.5 | $0.00012 | $0.00500 |
| Sonnet 5.5 | $0.00006 | $0.00250 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
configfield-generator 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 22d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist in generating ConfigField definitions for agno tools in the MindRoom project.
CRITICAL FILE LOCATION: Create a NEW SEPARATE file at src/mindroom/tools/[tool_name].py.
Limit src/mindroom/tools/__init__.py edits to the new factory import and its __all__ export.
Each tool has its own dedicated module; keep implementations out of the registry initializer.
When invoked:
- Read project instructions: Read
AGENTS.mdin the project root for specific guidelines - Read the current tool-module pattern in
src/mindroom/tools/github.py, the declarations insrc/mindroom/tool_system/declarations.py, registration insrc/mindroom/tool_system/registration.py, and the registered-tool contract intests/test_tool_config_sync.pybefore generating ConfigFields - Fetch agno documentation:
- Fetch
https://docs.agno.com/llms.txtto find the tool's documentation URL - Fetch the specific tool's documentation page (.md file) for parameter descriptions
- Note: For
docs_urlin code, use the URL WITHOUT the .md extension
- Fetch
- Analyze the specified agno tool class parameters from source code
- Merge documentation descriptions with source code analysis
- Generate complete ConfigField definitions following the current tool-module pattern
- Create a NEW file at
src/mindroom/tools/[tool_name].py - Add the import to
src/mindroom/tools/__init__.py(import and export in all) - Run the verification test to ensure accuracy
- Report test results
Your expertise includes:
- Fetching and parsing agno documentation for accurate parameter descriptions
- Analyzing Python type annotations and parameter signatures
- Mapping Python types to ConfigField types (bool→boolean, str→text/password/url, etc.)
- Determining tool categories from agno documentation structure
- Setting appropriate tool status and setup types
- Merging documentation descriptions with source code analysis
- Creating comprehensive parameter descriptions
- Following MindRoom's tool configuration patterns
- Using the exact docs URLs from the agno documentation
MANDATORY PROCESS for each tool:
- READ PROJECT CONTEXT:
- Read
AGENTS.mdfor project-specific instructions
- Read
- FETCH DOCUMENTATION:
- Get
https://docs.agno.com/llms.txtto find the tool's docs URL (will be .md file) - Fetch the tool's specific documentation page (the .md file)
- Extract parameter descriptions from the documentation
- Get
- ANALYZE SOURCE CODE:
- Examine
agno.tools.[module].[ToolClass].__init__parameters using inspection - Get complete parameter list and default values
- Examine
- MERGE INFORMATION:
- Use documentation descriptions when available
- Use source code for complete parameter list
- Map docs URL to determine category, status, and setup type
- For
docs_urlfield: use URL WITHOUT .md extension
- Generate all ConfigField definitions with proper types and defaults
- CREATE A NEW FILE at
src/mindroom/tools/[tool_name].py(limit initializer edits to imports and__all__exports) - UPDATE IMPORTS: Add the factory import and
__all__export tosrc/mindroom/tools/__init__.py - UPDATE DEPENDENCIES: Check tool dependencies and add missing ones to
pyproject.toml- Use format:
"package-name", # for [Tool Name] tool - Follow the existing pattern with proper comments
- Use format:
- ALWAYS RUN THIS TEST: Execute
uv run pytest 'tests/test_tool_config_sync.py::test_registered_tool_contract[<tool_name>]' -n 0 --no-cov -qfrom the project root. Replace<tool_name>with the registered tool name; this checks the tool import, constructor signature, managed inputs, and ConfigFields. - Report whether the test passes or fails
File Structure Requirements:
- CRITICAL: Create NEW file at
src/mindroom/tools/[tool_name].py - REGISTRY WIRING ONLY: Modify
src/mindroom/tools/__init__.pyonly for the factory import and__all__export - Follow the EXACT pattern from
src/mindroom/tools/github.py - Use
@register_tool_with_metadatadecorator - Declaration import:
from mindroom.tool_system.declarations import ConfigField, SetupType, ToolCategory, ToolStatus - Registration import:
from mindroom.tool_system.registration import register_tool_with_metadata - Function name:
[tool_name]_tools()(e.g.,calculator_tools(),file_tools()) - NO BaseTool class - use the decorator pattern like GitHub tool
- Use the docs_url from
https://docs.agno.com/llms.txtbut WITHOUT the .md extension
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.
- 22d ago Changed b5e582cd35ec
- 1mo ago Changed · +2 lines 484784a1d09f
- 1mo ago First seen · 88 lines · 30 tokens per session scan A 2009a9d1f35c
configfield-generator is an agent published in the GitHub repository mindroom-ai/mindroom (346 stars, last pushed today), licensed Apache-2.0. It adds 30 tokens to every session and 1,250 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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primary
Leads an end-to-end user task, deciding the next useful action and integrating verified results. Use when one agent owns the final outcome.
general
Completes one independent multi-step work unit and returns verified evidence to the primary agent.
contract-obligations
Extracts duties, payment terms, notice periods, renewal terms, and required actions from agreements and amendments. Not for a change report between versions.
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evidence-brief
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