Ralph Orchestrator is a framework that repeatedly runs AI-agent tasks until they finish or reach an iteration limit. Developers use it to coordinate autonomous coding work through command-line, web-dashboard, and MCP-server interfaces, with state managed per workspace. The catalogue entries provide agent skills, agents, instructions, and plugins for operating Ralph.
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 skills/mikeyobrien/ralph-orchestrator/test-driven-developmentnpx skills add mikeyobrien/ralph-orchestrator --skill test-driven-developmentgit clone --depth 1 https://github.com/mikeyobrien/ralph-orchestratorWrote 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/skills/mikeyobrien/ralph-orchestrator/test-driven-development)<a href="https://agentmods.dev/skills/mikeyobrien/ralph-orchestrator/test-driven-development"><img src="https://agentmods.dev/badge/skills/mikeyobrien/ralph-orchestrator/test-driven-development.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.01078 |
| Opus 5 | $0.00020 | $0.00539 |
| Sonnet 5 | $0.00008 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
test-driven-development 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 5d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test-Driven Development
Overview
One skill for all TDD workflows. Enforces test-first development using existing repository patterns. Three input modes handle different entry points — specs, task files, or ad-hoc descriptions — but the core cycle is always RED → GREEN → REFACTOR.
Input Modes
Detect the input type and follow the corresponding mode:
Mode A: From Spec (.spec.md)
Use when the input references a .spec.md file with Given/When/Then acceptance criteria.
- Locate and parse the spec file — extract all Given/When/Then triples
- Generate one test stub per criterion with
todo!()bodies:/// Spec: <spec-file> — Criterion #<N> /// Given <given text> /// When <when text> /// Then <then text> #[test] fn <spec_name>_criterion_<N>_<slug>() { todo!("Implement: <then text>"); } - Verify stubs compile but fail:
cargo test --no-run -p <crate> - Proceed to the TDD Cycle to make stubs pass
Programmatic support: ralph_core::preflight::{extract_acceptance_criteria, extract_criteria_from_file, extract_all_criteria} can parse criteria from spec files.
Mode B: From Task (.code-task.md)
Use when the input references a .code-task.md file or a specific implementation task.
- Read the task and identify acceptance criteria or requirements
- Discover patterns (see Pattern Discovery)
- Design test scenarios covering normal operation, edge cases, and error conditions
- Write failing tests for all requirements before any implementation
- Proceed to the TDD Cycle
Mode C: From Description
Use for ad-hoc tasks without a spec or task file.
- Clarify requirements from the description
- Discover patterns (see Pattern Discovery)
- Write failing tests targeting the described behavior
- Proceed to the TDD Cycle
Pattern Discovery
Before writing tests, discover existing conventions:
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.
- 5d ago First seen · 137 lines · 41 tokens per session scan A 8c41256017cb
test-driven-development is a skill published in the GitHub repository mikeyobrien/ralph-orchestrator (3,122 stars, last pushed 3d ago), licensed MIT. It adds 41 tokens to every session and 1,078 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 2026-08-30.
Other skills, from other repositories
test-generation
Use when the user asks for tests, mentions TDD, or when new code has been written and needs test coverage.
tdd
Test-driven development workflow with philosophy guide - plan → write tests → implement → validate.
implement_task
Implementation agent that executes a single task and creates handoff on completion.
tdd-migrate
TDD workflow for migrations - orchestrate agents, zero main context growth.
tdd-migration-pipeline
Orchestrator-only workflow for migrating/rewriting codebases with full TDD and agent delegation.
tdd-patterns
Guide test-driven development through the mandatory Red-Green-Refactor cycle (failing test before code), enforce test quality (one behavior per test, real code over mocks, no implementation-detail testing), and enforce test runner discipline (run mode, no watch mode). Use when implementing features or fixing bugs…