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forge

Use when implementing any feature or bugfix, before writing implementation code

Overview

PublisherGadaaLabs
Repositoryclaude-code-on-steroids
Skill nameforge
Stars
67
Forks
10
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Forge 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.
  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/GadaaLabs/claude-code-on-steroids.git /tmp/claude-code-on-steroids
mkdir -p .claude/skills
cp -r /tmp/claude-code-on-steroids/skills/forge .claude/skills/forge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Forge 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 Forge 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 Forge 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.

Test-Driven Development (TDD)

Overview

FORGEA forge is where raw metal is shaped under heat and pressure into something precise and durable. When invoked: writes the failing test first, refuses to write implementation until the test exists and fails for the right reason, then drives the code to pass it. Red → Green → Refactor, every time.

Write the test first. Watch it fail. Write minimal code to pass.

Core principle: If you didn't watch the test fail, you don't know if it tests the right thing.

Violating the letter of the rules is violating the spirit of the rules.

When to Use

Always:

  • New features
  • Bug fixes
  • Refactoring
  • Behavior changes

Exceptions (ask your human partner):

  • Throwaway prototypes
  • Generated code
  • Configuration files

Thinking "skip TDD just this once"? Stop. That's rationalization.

Verify APIs Before Writing Tests

Before writing any test that references an external API, library method, or internal function — verify it exists.

Writing a test against an invented interface is worse than writing no test. It creates:

  • False confidence (test "passes" because it tests a phantom)
  • Discovery failure at implementation time instead of planning time
  • A broken test suite that misleads future developers

Verification Steps (Required)

For any external library call in the test:

bash
# Confirm the import path exists in node_modules
ls node_modules/<pkg>/dist/ | grep <module>

# Confirm the method signature in type definitions
grep -r "methodName" node_modules/<pkg>/dist/index.d.ts

For any internal function in the test:

bash
# Confirm the export exists
grep -n "export.*functionName\|module.exports" path/to/file.ts

Red flag: If you cannot find the function in source or types, the test is guessing. Stop. Verify the correct API first.

Anti-Patterns

PatternProblem
import { method } from 'lib' without checking typesInvented import
expect(result.property) without checking the return typeInvented shape
Using a function signature from memoryMay be wrong version
"The implementer will wire it up"Test is untethered

The Iron Law

NO PRODUCTION CODE WITHOUT A FAILING TEST FIRST

Write code before the test? Delete it. Start over.

No exceptions:

  • Don't keep it as "reference"
  • Don't "adapt" it while writing tests
  • Don't look at it
  • Delete means delete

Implement fresh from tests. Period.

Red-Green-Refactor

1. RED — Write one minimal failing test → run it → confirm it fails for the right reason (not a typo) 2. GREEN — Write the simplest code that passes — nothing more 3. REFACTOR — Clean up while keeping tests green → repeat

If test passes immediately: you're testing existing behavior. Fix the test. If test errors: fix the error, re-run until it fails correctly.

RED - Write Failing Test

Write one minimal test showing what should happen.

const result = await retryOperation(operation);

expect(result).toBe('success'); expect(attempts).toBe(3); });

Clear name, tests real behavior, one thing
</Good>

<Bad>
```typescript
test('retry works', async () => {
  const mock = jest.fn()
    .mockRejectedValueOnce(new Error())
    .mockRejectedValueOnce(new Error())
    .mockResolvedValueOnce('success');
  await retryOperation(mock);
  expect(mock).toHaveBeenCalledTimes(3);
});

Vague name, tests mock not code

Requirements:

  • One behavior
  • Clear name
  • Real code (no mocks unless unavoidable)

Verify RED - Watch It Fail

MANDATORY. Never skip.

bash
npm test path/to/test.test.ts

Confirm:

  • Test fails (not errors)
  • Failure message is expected
  • Fails because feature missing (not typos)

Test passes? You're testing existing behavior. Fix test.

Test errors? Fix error, re-run until it fails correctly.

GREEN - Minimal Code

Write simplest code to pass the test.

Don't add features, refactor other code, or "improve" beyond the test.

Verify GREEN - Watch It Pass

MANDATORY.

bash
npm test path/to/test.test.ts

Confirm:

  • Test passes
  • Other tests still pass
  • Output pristine (no errors, warnings)

Test fails? Fix code, not test.

Other tests fail? Fix now.

REFACTOR - Clean Up

After green only:

  • Remove duplication
  • Improve names
  • Extract helpers

Keep tests green. Don't add behavior.

Repeat

Next failing test for next feature.

Good Tests

QualityGoodBad
MinimalOne thing. "and" in name? Split it.test('validates email and domain and whitespace')
ClearName describes behaviortest('test1')
Shows intentDemonstrates desired APIObscures what code should do

Why Order Matters

"I'll write tests after to verify it works"

Tests written after code pass immediately. Passing immediately proves nothing:

  • Might test wrong thing
  • Might test implementation, not behavior
  • Might miss edge cases you forgot
  • You never saw it catch the bug

Test-first forces you to see the test fail, proving it actually tests something.

"I already manually tested all the edge cases"

Manual testing is ad-hoc. You think you tested everything but:

  • No record of what you tested
  • Can't re-run when code changes
  • Easy to forget cases under pressure
  • "It worked when I tried it" ≠ comprehensive

Automated tests are systematic. They run the same way every time.

"Deleting X hours of work is wasteful"

Sunk cost fallacy. The time is already gone. Your choice now:

  • Delete and rewrite with TDD (X more hours, high confidence)
  • Keep it and add tests after (30 min, low confidence, likely bugs)

The "waste" is keeping code you can't trust. Working code without real tests is technical debt.

"TDD is dogmatic, being pragmatic means adapting"

TDD IS pragmatic:

  • Finds bugs before commit (faster than debugging after)
  • Prevents regressions (tests catch breaks immediately)
  • Documents behavior (tests show how to use code)
  • Enables refactoring (change freely, tests catch breaks)

"Pragmatic" shortcuts = debugging in production = slower.

"Tests after achieve the same goals - it's spirit not ritual"

No. Tests-after answer "What does this do?" Tests-first answer "What should this do?"

Tests-after are biased by your implementation. You test what you built, not what's required. You verify remembered edge cases, not discovered ones.

Tests-first force edge case discovery before implementing. Tests-after verify you remembered everything (you didn't).

30 minutes of tests after ≠ TDD. You get coverage, lose proof tests work.

Common Rationalizations

ExcuseReality
"Too simple to test"Simple code breaks. Test takes 30 seconds.
"I'll test after"Tests passing immediately prove nothing.
"Tests after achieve same goals"Tests-after = "what does this do?" Tests-first = "what should this do?"
"Already manually tested"Ad-hoc ≠ systematic. No record, can't re-run.
"Deleting X hours is wasteful"Sunk cost fallacy. Keeping unverified code is technical debt.
"Keep as reference, write tests first"You'll adapt it. That's testing after. Delete means delete.
"Need to explore first"Fine. Throw away exploration, start with TDD.
"Test hard = design unclear"Listen to test. Hard to test = hard to use.
"TDD will slow me down"TDD faster than debugging. Pragmatic = test-first.
"Manual test faster"Manual doesn't prove edge cases. You'll re-test every change.
"Existing code has no tests"You're improving it. Add tests for existing code.

Red Flags - STOP and Start Over

  • Code before test
  • Test after implementation
  • Test passes immediately
  • Can't explain why test failed
  • Tests added "later"
  • Rationalizing "just this once"
  • "I already manually tested it"
  • "Tests after achieve the same purpose"
  • "It's about spirit not ritual"
  • "Keep as reference" or "adapt existing code"
  • "Already spent X hours, deleting is wasteful"
  • "TDD is dogmatic, I'm being pragmatic"
  • "This is different because..."

All of these mean: Delete code. Start over with TDD.

Example: Bug Fix

Bug: Empty email accepted

RED

typescript
test('rejects empty email', async () => {
  const result = await submitForm({ email: '' });
  expect(result.error).toBe('Email required');
});

Verify RED

bash
$ npm test
FAIL: expected 'Email required', got undefined

GREEN

typescript
function submitForm(data: FormData) {
  if (!data.email?.trim()) {
    return { error: 'Email required' };
  }
  // ...
}

Verify GREEN

bash
$ npm test
PASS

REFACTOR Extract validation for multiple fields if needed.

Verification Checklist

Before marking work complete:

  • Every new function/method has a test
  • Watched each test fail before implementing
  • Each test failed for expected reason (feature missing, not typo)
  • Wrote minimal code to pass each test
  • All tests pass
  • Output pristine (no errors, warnings)
  • Tests use real code (mocks only if unavoidable)
  • Edge cases and errors covered

Can't check all boxes? You skipped TDD. Start over.

When Stuck

ProblemSolution
Don't know how to testWrite wished-for API. Write assertion first. Ask your human partner.
Test too complicatedDesign too complicated. Simplify interface.
Must mock everythingCode too coupled. Use dependency injection.
Test setup hugeExtract helpers. Still complex? Simplify design.

Debugging Integration

Bug found? Write failing test reproducing it. Follow TDD cycle. Test proves fix and prevents regression.

Never fix bugs without a test.

Testing Anti-Patterns

When adding mocks or test utilities, read @testing-anti-patterns.md to avoid common pitfalls:

  • Testing mock behavior instead of real behavior
  • Adding test-only methods to production classes
  • Mocking without understanding dependencies

Final Rule

Production code → test exists and failed first
Otherwise → not TDD

No exceptions without your human partner's permission.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Forge AI skill do?

Use when implementing any feature or bugfix, before writing implementation code

Why use Forge on TypingMind?

Because you install it once and use it with any model. Forge 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 Forge in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GadaaLabs/claude-code-on-steroids/tree/main/skills/forge. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Forge?

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 Forge?

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

Is the Forge AI skill free?

It is published on GitHub by GadaaLabs. Check the repository for licensing terms. 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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