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.
npx agentmods add agents/ambient-code/agentready/agentready-devgit clone --depth 1 https://github.com/ambient-code/agentreadyWhat 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 | $0.00023 | $0.04068 |
| Opus 5 | $0.00012 | $0.02034 |
| Sonnet 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 yesterday.
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.
- yesterday First seen · 614 lines · 23 tokens per session scan A 812f9e97686e
AgentReady Development is an agent published in the GitHub repository ambient-code/agentready (151 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 4,068 once invoked, about $0.0001 per session on Opus 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
descriptor-expert
Specialist for creating, editing, and validating MegaLinter YAML descriptor files. Use when working on linter descriptors, adding new linters, or modifying linter configurations.
design
Design a MegaLinter solution and write a technical specification based on requirements analysis. Use after /analyze.
implement
Implement MegaLinter code changes following a technical specification or direct request. Use after /design, or directly for small focused changes.
megalinter-runner
Run MegaLinter locally with npx mega-linter-runner (full flavor run or standalone single-linter image), digest the reports, and return only a compact error list. Use to keep verbose linter output out of the main context. Runs and reports only — never fixes source files.
analyze
Gather requirements for a MegaLinter change by asking clarifying questions until the problem is fully understood. Use before designing or implementing any change.
test-debugger
Debug failing MegaLinter linter tests. Use when tests fail, test fixtures need fixing, or Docker-based test execution needs troubleshooting.