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/doubleagent.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/doubleagent)<a href="https://agentmods.dev/agents/ambient-code/agentready/doubleagent"><img src="https://agentmods.dev/badge/agents/ambient-code/agentready/doubleagent/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/doubleagent"><img src="https://agentmods.dev/badge/agents/ambient-code/agentready/doubleagent.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 | $0.00000 | $0.04014 |
| Opus 5.5 | $0.00000 | $0.01606 |
| Sonnet 5.5 | $0.00000 | $0.00803 |
| Haiku 4.5 | $0.00000 | $0.00401 |
Grade A, and why
doubleagent 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], ...) This is a copy
97% identical to AgentReady Development — 12 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 — 608 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: 2025-11-21 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.py # 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 · 608 lines · 0 tokens per session scan A f5b9e52d0f9f
doubleagent is an agent published in the GitHub repository ambient-code/agentready (154 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,014 tokens. A static security scan graded it A with 1 finding (runs shell commands). It is 97% identical to AgentReady Development, differing in 12 lines, and is treated as a copy.
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