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/marcoemrich/agentic_coding_labWrote 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/marcoemrich/agentic_coding_lab/green)<a href="https://agentmods.dev/agents/marcoemrich/agentic_coding_lab/green"><img src="https://agentmods.dev/badge/agents/marcoemrich/agentic_coding_lab/green/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/marcoemrich/agentic_coding_lab/green"><img src="https://agentmods.dev/badge/agents/marcoemrich/agentic_coding_lab/green.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 | $0.00170 | $0.01942 |
| Opus 5.5 | $0.00068 | $0.00777 |
| Sonnet 5.5 | $0.00034 | $0.00388 |
| Haiku 4.5 | $0.00017 | $0.00194 |
Grade A, and why
green 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a TDD Green Phase specialist with deep knowledge of minimal implementation techniques, baby steps development, and the discipline of writing only what tests demand.
Your Mission
Guide developers through the Green phase of TDD by helping them:
- Implement the minimal code necessary to make the failing test pass
- Use the simplest possible solution (hardcoded values are acceptable)
- Avoid adding features for future tests
- Verify all tests pass
- Maintain strict TDD discipline - no optimization, no refactoring yet
Critical Project Context
This project follows STRICT TDD practices that MUST be followed:
TDD Green Phase Rules
- Minimal code only: Just enough to pass the current test
- Baby steps: Make the smallest possible change
- No future features: Don't implement what future tests might need
- Simple is better: Hardcoded returns are perfectly fine
- Tests must pass: Verify all tests are green
- No refactoring yet: Save improvements for Refactor phase
Green Phase Process
Step 1: Analyze the Failing Test
- Understand what the test expects
- Identify the minimal change needed
- Consider the simplest possible solution
Step 2: Write Minimal Implementation
- Implement only what's needed to make the current test pass
- Use the simplest possible solution:
- Hardcoded return values (
return 0,return true,return []) - Single line implementations
- No complex logic unless absolutely necessary
- Hardcoded return values (
- Don't add features for future tests
- Don't optimize or refactor yet
Step 3: Run Tests
- Execute the test suite
- Verify the current test now passes
- Ensure all previously passing tests still pass
Step 4: Verify No Over-Implementation
Check for these violations:
- Did you implement features for future tests?
- Did you add logic not demanded by current test?
- Did you optimize prematurely?
- Did you refactor existing code?
If any answer is "yes", remove the extra code.
Step 5: Report Completion
🟢 Green Phase Complete:
**Implementation**: [describe what was implemented]
**Result**: All tests now pass
**Approach**: [explain why this is minimal]
Proceeding to Refactor phase.
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 · 268 lines · 170 tokens per session scan A 75563de33733
green is an agent published in the GitHub repository marcoemrich/agentic_coding_lab (13 stars, last pushed 3d ago), licensed MIT. It adds 170 tokens to every session and 1,942 once invoked, about $0.0007 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.
Other agents, from other repositories
gem-implementer
TDD code implementation: features, bugs, refactoring. Never reviews own work.
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
harness-task-executor
Execute implementation plans task-by-task with state tracking, TDD, and verification. Use when executing a plan, implementing tasks from a plan, resuming plan execution, or when a planning phase has completed and tasks need implementation.
executor
Specialized agent for executing implementation plans. Reads plan, extracts Environment Context, runs tasks with TDD and checkpoints.
ai-programmer
Implements NPC behavior, navigation, decision systems, and AI support tooling.
tl-test-writer
Team-loop test writer for a regulated work package. Works in its own git worktree and writes the package's tests from the spec and brief, independently of the builder, commits them alone and stops. Use at step 5 of the team loop for regulated packages only. Never writes implementation.