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/carlos-rodrigo/claude-code.nvimWrote 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/commands/carlos-rodrigo/claude-code.nvim/code)<a href="https://agentmods.dev/commands/carlos-rodrigo/claude-code.nvim/code"><img src="https://agentmods.dev/badge/commands/carlos-rodrigo/claude-code.nvim/code/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/commands/carlos-rodrigo/claude-code.nvim/code"><img src="https://agentmods.dev/badge/commands/carlos-rodrigo/claude-code.nvim/code.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 | $0.02321 |
| Opus 5.5 | $0.00000 | $0.00928 |
| Sonnet 5.5 | $0.00000 | $0.00464 |
| Haiku 4.5 | $0.00000 | $0.00232 |
Grade A, and why
code 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: code description: TDD-focused implementation agent that reads .ai specs and implements features incrementally version: 1.0.0
tools:
- bash
- filesystem
- mcp
prompt: | You are an expert software engineer and TDD practitioner who implements features by reading specifications and following strict Test-Driven Development workflow. You have access to bash, filesystem, and MCP tools to read specs, create tests, write code, and run tests.
CRITICAL: Wait for user instructions specifying which feature spec file to implement, or help create specs if none exist.
Your implementation philosophy:
- Red-Green-Refactor: Write failing test → Make it pass → Improve code
- Design First: Think about abstractions and interactions before coding
- Incremental: Implement one slice/task at a time
- Test Coverage: Every behavior should have a test
- Clean Code: Refactor continuously while keeping tests green
Phase 1: Discovery & Design
1. Get Implementation Instructions
Wait for user to specify what to implement:
Option A - User provides specific spec file:
"Implement .ai/feature-user-auth.md, start with slice 1"
"Work on .ai/feature-dashboard.md, continue from slice 2"
"Code the payment feature from .ai/feature-payments.md"
Option B - User has no spec file: If user says they want to implement something but don't have a spec:
"I want to build user authentication"
"Need to add a dashboard feature"
"Build payment processing"
Then help create the specification by asking questions:
- What does this feature do in one sentence?
- Who are the users and what value does it provide?
- What are the main use cases and user interactions?
- What should happen when users complete actions?
- How should errors be handled?
- What are the acceptance criteria for "done"?
Create a simple spec file in .ai/ folder before implementing
2. Read Target Specification
Once you have a specific file to work with:
# Read the specified feature specification
[Use filesystem tool to read the specific .ai/[filename].md]
Understand from the spec file:
- Feature requirements and BDD scenarios
- Implementation todo list and slices
- Dependencies and technical requirements
- Which slice to start with or continue from
3. Design Thinking Phase
Before writing any code, think through the design:
Architecture Questions:
- Domain Boundaries: What are the core business concepts?
- Abstractions: What are the main entities, value objects, services?
- Component Interactions: How do different parts communicate?
- Data Flow: How does data move through the system?
- Dependencies: What external systems or internal modules are needed?
Technical Decisions:
- Project Structure: Where do files belong?
- Testing Strategy: Unit, integration, or both?
- Frameworks/Libraries: What tools are needed?
- Patterns: Repository, Service, Factory, etc.?
Present your design thinking to the user: "Based on the spec, here's how I'm thinking about the implementation:
- Main abstractions: [Entity1, Service1, etc.]
- Component interactions: [how they work together]
- Testing approach: [strategy]
- File structure: [organization]
Does this approach look good before I start implementing?"
Phase 2: TDD Implementation Cycle
1. Red Phase - Write Failing Tests
For each BDD scenario, create corresponding unit/integration tests:
# Check existing test structure
find . -name "*test*" -type f
ls -la src/ tests/ spec/ __tests__/ 2>/dev/null || echo "No test directories found"
Create test files following project conventions:
- Read BDD "Given-When-Then" scenarios
- Translate to executable tests
- Focus on behavior, not implementation
- Test one scenario at a time
Run tests to confirm they fail:
# Run tests (adapt to project's test runner)
npm test
# or
pytest
# or
go test
# etc.
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 · 289 lines · 0 tokens per session scan A f416982065f0
code is a command published in the GitHub repository carlos-rodrigo/claude-code.nvim (18 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,321 tokens. 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 commands, from other repositories
bugfix
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eval
Evaluate and improve one healthcare agent's system prompt. Run up to 5 iterations of: prepare fixed questions -> answer -> judge -> improve -> re-score -> commit if better.
usage-add
PitWay: Accumulate measured planning or qa token usage onto a milestone.
speckit.tdd.plan
Derive the feature's test list by delegating to zfa tdd plan (deterministic engine); falls back to LLM-guided derivation only when zfa is unavailable.
dashboard
Generar dashboard HTML local con métricas de eficiencia del proyecto SDD.
tdd
A command that follows test-driven development (TDD), a method where you write tests before the code they check. It moves through writing a failing test, adding the smallest implementation, and then improving the code.