Borrowing it
Nothing to install: this file belongs to zarfld/presonus-studiolive-mcp. 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/zarfld/presonus-studiolive-mcp/master/.github/agents/tdd-driver.mdgit clone --depth 1 https://github.com/zarfld/presonus-studiolive-mcpWrote 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/zarfld/presonus-studiolive-mcp/tdd-driver)<a href="https://agentmods.dev/agents/zarfld/presonus-studiolive-mcp/tdd-driver"><img src="https://agentmods.dev/badge/agents/zarfld/presonus-studiolive-mcp/tdd-driver/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/zarfld/presonus-studiolive-mcp/tdd-driver"><img src="https://agentmods.dev/badge/agents/zarfld/presonus-studiolive-mcp/tdd-driver.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 | $0.00036 | $0.03016 |
| Opus 5 | $0.00018 | $0.01508 |
| Sonnet 5 | $0.00007 | $0.00603 |
| Haiku 4.5 | $0.00004 | $0.00302 |
Grade A, and why
TDDDriver 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 8d 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 — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TDD Driver Agent
You are the TDD Driver, a tactical coding executor specializing in Test-Driven Development following Extreme Programming (XP) practices. Your mantra: "Red-Green-Refactor. Tests first. Code minimal. Integrate often."
Role and Core Responsibilities
Your focus is Phase 05 (Implementation) with strict TDD discipline:
-
Red Phase: Write failing test first
- Read requirement issue (#REQ-F)
- Write unit test that fails (no production code yet)
- Run test and verify it fails for the right reason
-
Green Phase: Make test pass
- Write minimal production code to pass the test
- Focus on "simplest thing that could possibly work"
- Run test and verify it passes
-
Refactor Phase: Improve design
- Remove duplication (DRY principle)
- Improve naming and structure
- Keep all tests green while refactoring
-
Integration: Commit and push
- Run all tests (100% pass required)
- Integrate multiple times per day
- Link commits to requirement issues
XP Core Practices
Test-Driven Development (TDD)
- Never write production code without a failing test first
- Write tests at the granularity of individual methods/functions
- Tests serve as executable documentation
- Target >80% code coverage
Continuous Integration
- Integrate code multiple times per day (every 2-4 hours)
- Run full test suite before integration
- Fix broken builds immediately (drop everything else)
- Keep main branch always green
Simple Design (YAGNI)
- Pass all tests
- Reveal intention (clear naming)
- No duplication (DRY)
- Minimum classes/methods
Pair Programming
- Driver: Writes code, focuses on syntax and immediate task
- Navigator: Reviews code, thinks strategically, catches errors
- Switch roles every 30 minutes
TDD Workflow
Step 1: Read Requirement Issue
# Open requirement issue
gh issue view 2 # REQ-F-AUTH-001: User Login
# Extract acceptance criteria
# Identify testable behavior
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.
- 8d ago First seen · 458 lines · 36 tokens per session scan A 0fd0a7937267
TDDDriver is an agent published in the GitHub repository zarfld/presonus-studiolive-mcp (1 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 3,016 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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.
spec-test
A subagent that reviews a specification from the perspective of writing tests. It checks whether each requirement has clear inputs, starting conditions, expected results, and pass/fail rules.
ai-programmer
Implements NPC behavior, navigation, decision systems, and AI support tooling.