Source driven development
Skill manastalukdar/ai-devstudio/skills/source-driven-development
Fetch official docs before writing any framework-specific code — implement to documented patterns, cite the source, and flag any deviation from the official approach.From its SKILL.md
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SKILL.md
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Source-Driven Development
I'll look up the current official documentation for every framework, library, or API involved before writing implementation code. Training data may be stale. Docs are not.
Arguments: $ARGUMENTS — feature or task to implement; include library/framework names if known
Token Optimization
Expected range: 400–1,200 tokens (doc fetch + implementation)
Early exit: If the API being used is project-internal (no external docs to fetch), skip doc lookup and proceed directly
Caching: Cache fetched docs in .claude/cache/source-driven/<library>-<version>.md for 24 hours
Patterns used: Grep-before-Read (check cache before fetching), progressive disclosure (API summary first, full docs on demand)
Step 1 — Identify Documentation Targets
From $ARGUMENTS and the project context, identify every external library or API to be used:
# Check installed versions to target the correct docs
cat package.json 2>/dev/null | grep -E '"dependencies"|"devDependencies"' -A 30 | head -40
cat requirements.txt 2>/dev/null | head -20
cat Cargo.toml 2>/dev/null | grep -A 20 '\[dependencies\]'
List each target:
Libraries to document:
1. [library-name] @ [version]
2. [library-name] @ [version]
Step 2 — Fetch Current Documentation
For each library, fetch the relevant documentation section using the context7 MCP server if available, otherwise fall back to web search:
Fetching: [library] v[version] — [specific API or feature]
Source: [URL or context7 library ID]
Check the cache first:
ls .claude/cache/source-driven/ 2>/dev/null | grep "^<library>"
Step 3 — Summarize Relevant API Surface
From the fetched docs, extract only the API surface needed for this task:
[Library] v[version] — relevant API:
[Method/hook/config name]
Signature: [exact signature from docs]
Purpose: [one line]
Key notes: [anything non-obvious or changed since common usage]
Source: [URL]
Flag any breaking changes or deprecations between the cached/assumed version and the current docs.
Step 4 — Implement to Documented Patterns
Write the implementation using only the documented API surface from Step 3. For each non-trivial API call, add a source citation comment:
// Docs: https://example.com/library/api#method-name
const result = library.method(params)
If the documented approach differs from the pattern you would have written from training data, note the difference:
Note: implemented using [documented pattern], not [common pattern from training].
Reason: [brief explanation from docs].
Step 5 — Flag Deviations
If implementation requires deviating from the documented pattern (e.g., a known bug workaround, performance constraint), document it explicitly:
Deviation: [what differs from docs]
Reason: [why]
Risk: [what breaks if the library is updated to fix the issue]
Edge Cases
- Docs not publicly available (private APIs, internal SDKs): proceed with training data, flag the uncertainty explicitly
- Library version not in docs (pre-release, fork): note the version gap; use the closest available docs
- context7 not configured: fall back to web search; note that results may not be perfectly current
- Multiple conflicting doc sources (blog post vs. official docs): always prefer the official library docs; cite both and note the discrepancy
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