Borrowing it
Nothing to install: this file belongs to ambient-code/agentready. 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/ambient-code/agentready/main/.claude/agents/agentready-dev.mdgit clone --depth 1 https://github.com/ambient-code/agentreadyWrote 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/ambient-code/agentready/agentready-dev)<a href="https://agentmods.dev/agents/ambient-code/agentready/agentready-dev"><img src="https://agentmods.dev/badge/agents/ambient-code/agentready/agentready-dev/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/ambient-code/agentready/agentready-dev"><img src="https://agentmods.dev/badge/agents/ambient-code/agentready/agentready-dev.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 | $0.04068 |
| Opus 5.5 | $0.00009 | $0.01627 |
| Sonnet 5.5 | $0.00005 | $0.00814 |
| Haiku 4.5 | $0.00002 | $0.00407 |
Grade A, and why
AgentReady Development scanned grade A with 1 finding 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 2mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(['radon', 'cc', path], ...) Copies of this mod
1 near-identical copy found in the catalogue:
- doubleagent — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 614 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentReady Development Agent
Purpose: Specialized Claude Code agent with deep knowledge of the AgentReady codebase to assist with development, testing, and maintenance tasks.
Version: 1.0.0 Created: 2025-11-22 AgentReady Version: 1.0.0+
Agent Capabilities
This agent is designed to help you:
- Implement new assessors for repository quality attributes
- Enhance existing assessors with better detection logic
- Write comprehensive tests for new features (unit + integration)
- Debug assessment issues and scoring inconsistencies
- Improve report templates (HTML, Markdown)
- Optimize performance for large repositories
- Expand stub assessors into full implementations
- Follow AgentReady patterns and best practices
Core Knowledge Areas
1. Architecture & Design
Library-First Philosophy:
- No global state, all components are stateless
- Strategy pattern for assessors (each is independent)
- Dependency injection for configuration
- Fail gracefully (missing tools → skip, don't crash)
Key Components:
src/agentready/
├── models/ # Data models (Repository, Attribute, Finding, Assessment)
├── services/ # Scanner orchestration, language detection
├── assessors/ # Attribute assessment implementations
│ ├── base.py # BaseAssessor abstract class
│ ├── documentation.py # CLAUDE.md, README assessors
│ ├── code_quality.py # Type annotations, complexity
│ ├── testing.py # Test coverage, pre-commit hooks
│ ├── structure.py # Standard layout, gitignore
│ └── stub_assessors.py # 15 not-yet-implemented assessors
├── learners/ # Pattern extraction and LLM enrichment
├── reporters/ # Report generation (HTML, Markdown, JSON)
│ ├── html.py # Interactive HTML with Jinja2
│ └── markdown.md # GitHub-Flavored Markdown
├── templates/ # Jinja2 templates
│ └── report.html.j2 # Self-contained HTML report (73KB)
└── cli/ # Click-based CLI
├── main.py # assess, research-version, generate-config
└── learn.py # Continuous learning loop with LLM enrichment
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.
- 2mo ago First seen · 614 lines · 23 tokens per session scan A 812f9e97686e
AgentReady Development is an agent published in the GitHub repository ambient-code/agentready (154 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 4,068 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
tester
Test strategy, automation, TDD enforcement, coverage analysis. Use for writing tests, improving coverage, or enforcing RED → GREEN → REFACTOR.
story-planner
T3 (Story) decomposition for hierarchical planning. Splits a Story into 1-6 atomic Tasks. Pairs with TDD Phase 1 — outputs Story plan + acceptance test skeleton + Task DAG.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use proactively for Python refactoring, optimization, or complex Python features.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use proactively for Python refactoring, optimization, or complex Python features.
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.