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Agentic Tdd

OrganizationPopular
reticlehq
agentic-tdd

Test-driven development for behaviour a unit test cannot reach, by writing the expectation against the running app before writing the code. Declare the consequence first, watch it fail, implement, watch it pass. Use when building a user-facing feature, when the user asks for TDD on UI or full-stack work, when a unit test cannot express the outcome that matters, or when you want a red-green loop that runs against the real app instead of mocks.

Overview

Publisherreticlehq
Repositoryreticle
Skill nameagentic-tdd
Stars
1.2K
Forks
176
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by reticlehq on GitHub. Read the source before you install it.

Installation

Install the Agentic Tdd AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/reticlehq/reticle/tree/main/skills/agentic-tdd
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/reticlehq/reticle.git /tmp/reticle
mkdir -p .claude/skills
cp -r /tmp/reticle/skills/agentic-tdd .claude/skills/agentic-tdd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agentic Tdd in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agentic Tdd on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agentic Tdd is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Red, green, refactor: against the running app

Unit tests drive the units. They cannot say "clicking Deploy posts to /api/deploy, moves the store to deploying, and shows the banner": that outcome only exists when the whole app runs. So the loop stalls exactly where the interesting bugs are, and the agent falls back to writing code and hoping.

This runs the same discipline one level up, using Reticle to drive the real app. Not installed? RETICLE_INSTALL_SOURCE=npx_skill npx @reticlehq/server@latest init, then the install-and-verify skill.

Why this is TDD and not just testing afterwards

The whole value of test-first is that the oracle is written while you still do not know the answer. An expectation written after seeing the result can always be adjusted into agreeing with whatever happened, and an agent is especially good at that adjustment. It will find a reading of the output under which the code it just wrote is correct.

reticle_act_and_wait({ ref, action, until }) enforces the order structurally: until is an argument to the action, so the consequence is named before the action runs. That is the red-green loop, made unfakeable.

1. RED: write the expectation, watch it fail

Before you write the feature, state what the app must do:

reticle_act_and_wait({ sessionId, ref, action: "click", until: { kind: "allOf", predicates: [
  { kind: "net",     method: "POST", urlContains: "/api/deploy", status: 200 },
  { kind: "signal",  name: "deploy:started" },
  { kind: "element", query: { testid: "deploy-banner" } },
  { kind: "console", level: "error", absent: true },
]}})

You want verified: "no" here. A red you did not see is a test you cannot trust. If this comes back yes before you have written anything, the expectation is not specific enough to the change. Tighten it until it fails for the right reason.

verified: "unknown" is not a red. It means Reticle could not tell, so the loop has no signal at all. Fix that before writing code, usually by naming a consequence the app can actually produce.

2. GREEN: implement until the same call passes

Write the smallest change that makes it hold, then re-run the same call, unchanged. That last word is the discipline: editing the predicate to match what you built converts TDD into narration. If the assertion has to change, say out loud why the original expectation was wrong.

Prefer re-asserting over re-driving when the verdict was unknown / unsettled: reticle_assert({ predicate, since, timeout_ms: 8000 }). Re-driving repeats a side effect that already happened.

3. REFACTOR: the expectation is the safety net

Now change the implementation freely and re-run. Predicates are bound to behaviour (a request, a signal, a state path), not to markup, so a refactor that preserves behaviour stays green while a DOM-shaped test would go red for no reason.

4. Keep the loop for the next change

A journey worth writing test-first is a journey worth re-running forever. Save it once:

reticle_run({ tool: "reticle_flow_save", sessionId, args: { flowName: "deploy" } })
reticle_run({ tool: "reticle_verify", sessionId, args: { action: "flows" } })   // every saved flow, no model per flow

That turns the red-green loop into a regression suite you never hand-wrote.

What to assert on, in order of strength

  1. A signal the app fires itself ({ kind: "signal" }): the app declaring success in its own vocabulary. Strongest available.
  2. State (reticle_look { action: "state" }): what the app believes. Catches a UI that moved while the store did not.
  3. Network: the request, method and status. Catches a mock standing in for the real thing.
  4. An element appearing: necessary, never sufficient. Anything can render.
  5. Absence of console errors: always include it, never rely on it alone. Absence-only predicates pass on a control wired to nothing.

Honesty

Never weaken a check to turn a verdict green. In this loop that is not a small sin: it is the loop running backwards, and it produces a green suite over a feature that does not work.


Predicate reference: curl https://docs.reticle.sh/predicates.md. Everything else: curl https://docs.reticle.sh/llms.txt.

Frequently asked questions

What does the Agentic Tdd AI skill do?

Test-driven development for behaviour a unit test cannot reach, by writing the expectation against the running app before writing the code. Declare the consequence first, watch it fail, implement, watch it pass. Use when building a user-facing feature, when the user asks for TDD on UI or full-stack work, when a unit test cannot express the outcome that matters, or when you want a red-green loop that runs against the real app instead of mocks.

Why use Agentic Tdd on TypingMind?

Because you install it once and use it with any model. Agentic Tdd is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agentic Tdd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reticlehq/reticle/tree/main/skills/agentic-tdd. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agentic Tdd?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agentic Tdd?

As many as you like. As long as a model supports skills, you can use Agentic Tdd with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agentic Tdd AI skill free?

Yes. It is published on GitHub by reticlehq under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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