AI DevKit is a local-first control layer that gives multiple AI coding agents shared configuration, a terminal dashboard, cross-agent messaging, local memory retrieval, and reusable engineering workflows. It is for developers and teams supervising agents such as Claude Code, Codex CLI, Gemini CLI, OpenCode, Pi, Cursor, Copilot, or Devin. The catalogue entries are composable skills and plugin components used within that agent-management system.
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 skills add codeaholicguy/ai-devkit --skill tddgit clone --depth 1 https://github.com/codeaholicguy/ai-devkitWrote 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/codeaholicguy/ai-devkit/tdd)<a href="https://agentmods.dev/skills/codeaholicguy/ai-devkit/tdd"><img src="https://agentmods.dev/badge/skills/codeaholicguy/ai-devkit/tdd/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/skills/codeaholicguy/ai-devkit/tdd"><img src="https://agentmods.dev/badge/skills/codeaholicguy/ai-devkit/tdd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 66 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00754 |
| Opus 5.5 | $0.00014 | $0.00302 |
| Sonnet 5.5 | $0.00007 | $0.00151 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
tdd 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TDD
Red. Green. Refactor. In that order, every time.
Hard Rules
- No production code without a failing test first.
- If production code was written before its test, delete it and start over with a failing test.
- Never skip the red step. A test that has never failed proves nothing.
Cycle
For each unit of behavior:
- Red — Write a test for the next behavior. Run it. It must fail. Read the failure message — it should describe the missing behavior.
- Green — Write the minimum production code to make the test pass. Nothing more. Run the test. Apply the
verifyskill. - Refactor — Clean up both test and production code. Run the test again. Still green? Done. Apply the
verifyskill.
Then pick the next behavior and repeat.
Rules for Each Step
Red:
- Test one behavior, not one function. Name the test after what the system should do, not what the function is called.
- The test must fail for the right reason — a missing method, wrong return value, unmet condition. Not a syntax error or import failure.
- If the test passes immediately, it's not testing new behavior. Delete it or pick a different behavior.
Green:
- Write the simplest code that passes. Hardcode if needed — the next test will force generalization.
- Do not add code "while you're in there." If it's not required by a failing test, it doesn't exist yet.
- Do not refactor during green. Pass first, clean second.
Refactor:
- Remove duplication between test and production code.
- Extract only when you see real duplication, not predicted duplication.
- Tests must still pass after every refactor move. Run them after each change.
Anti-Patterns
| Pattern | Problem | Fix |
|---|---|---|
| Test-after | Code shapes the test instead of the other way around | Delete the code, write the test first |
| Testing internals | Tests break on refactor, not on behavior change | Test public behavior only |
| Giant red step | Multiple behaviors in one test | One assertion per behavior |
| Gold-plating green | Adding code no test requires | Remove untested code |
| Skipping refactor | Tech debt accumulates immediately | Refactor before the next red |
| Mock-heavy tests | Tests pass but real code fails | Prefer real dependencies, mock at boundaries only |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 67 lines · 36 tokens per session scan A 86b44ef491b0
tdd is a skill published in the GitHub repository codeaholicguy/ai-devkit (1,649 stars, last pushed yesterday), licensed Apache-2.0. It adds 36 tokens to every session and 754 once invoked, about $0.0001 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 skills, from other repositories
test-driven-development
Drives development with tests using the red-green-refactor loop. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality. Use when asked to add tests to a function, fix a bug…
conductor-implement
Execute tasks from a track's implementation plan following TDD workflow.
tdd-explore-ideas
Systematic codebase exploration, architectural critique, and 5-step Idea Assessment with TDD hypothesis verification for Phase 0.
autonomous-tdd-debugger
Empowers the agent to autonomously run tests, read terminal stack traces, and self-heal code until tests pass. Transforms the agent from a passive coder to an active CI pipeline debugger.
tdd-ask-help
Interactive AI Guide and Navigator for the TDD-Spec SDLC ecosystem. Diagnoses current project phase, recommends next actions, routes scenarios, and explains skill purposes.
tdd-retro
Conducts comprehensive session & phase retrospectives to optimize test runner speed, eliminate flaky tests, update project memory, and continuously improve SDLC velocity.