| name | source-driven-development |
| description | Grounds every implementation decision in official documentation. Use when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters. |
Source-Driven Development
Overview
Every framework decision and library usage should be grounded in official documentation. AI agents have training cutoffs and can produce outdated, deprecated, or hallucinated API patterns. Source-driven development means: verify before you code, cite what you verified, and flag what you couldn't confirm.
When to Use
- Using any external library or framework
- Implementing a pattern that isn't already established in the codebase
- Unsure whether an API or approach is current
- Seeing a pattern that looks right but isn't confirmed in the existing codebase
The Process
Step 1: Identify What Needs Verification
Before writing any code that uses an external library:
- List the specific APIs, functions, or patterns you plan to use
- Note the version you're targeting (check
requirements.txt, package.json, or pyproject.toml)
- Flag anything you're not 100% confident is current
Step 2: Verify in Official Sources
Priority order for sources:
- Official docs — the library's own documentation site
- Official GitHub — the library's
README.md, CHANGELOG.md, or examples/
- Release notes — for the specific version in use
- Existing codebase — how the library is already used in this project
Never use as authoritative sources:
- Stack Overflow answers (can be outdated)
- Blog posts (may target a different version)
- Your own training knowledge without verification
- ChatGPT or other AI outputs as a source of truth
Step 3: Cite Your Sources
When writing code that uses verified patterns, leave a reference comment for future maintainers:
async def get_db() -> AsyncGenerator[AsyncSession, None]:
async with AsyncSessionLocal() as session:
yield session
const { data, isLoading } = useQuery({
queryKey: ['skills', repoId],
queryFn: () => fetchSkills(repoId),
});
Step 4: Flag What You Couldn't Verify
If you're using a pattern you cannot confirm is current, say so explicitly:
app.add_middleware(GZipMiddleware, minimum_size=1000)
And note it in the PR description or task update.
GitScape-Specific Sources
For the GitScape stack, always verify against:
Check the version in api/requirements.txt and web/package.json before citing docs for a specific version.
Anti-Patterns to Avoid
| Anti-Pattern | Why It Fails |
|---|
| Using an API without checking the version | Libraries break across major versions |
| Copying a pattern from memory | Training data may lag 12-24 months behind current releases |
| "This looks right" as verification | Looks right ≠ is right |
| Citing an AI as a source | AI outputs require human-verified sources, not circular citation |
Common Rationalizations
| Rationalization | Reality |
|---|
| "I know this library well" | APIs change. Verify. The minute spent checking prevents the hour debugging. |
| "I'll verify later" | You won't. And by then the wrong pattern is baked in. |
| "The existing code uses this pattern" | Great — that's evidence. Check if the existing usage is from a current dependency version. |
Red Flags
- Library usage with no reference to a source
- Patterns that differ from every other usage in the codebase without explanation
- Using a deprecated API (check for deprecation warnings in docs)
- Citing a blog post or Stack Overflow answer as the source
Verification
After writing code that uses external libraries: