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Search library/framework documentation via llms.txt (context7.com).
Passed 18 of 22 test cases with the skill, and 9 of 22 without it.
and 11 more in the full report
11 fixed8 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Context7 is unavailable, and I need documentation for date picker usage in shadcn. State the exact two-step order of local CLI scripts to run for topic-specific retrieval.
| Checked | Without skill | With skill |
|---|---|---|
| Executes detect-topic.js before calling fetch-docs.js | Missed | Passed |
| Runs fetch-docs.js as the second step in the fallback sequence | Missed | Passed |
The instructions your model reads when the skill loads.
Output style: Follow
process/development-protocols/communication-standards.md— answer-first, plain language, no unexplained jargon, TL;DR on long responses.
Use Context7 MCP as the default path for documentation lookup. The local scripts in this skill are fallback helpers for llms.txt-based discovery when Context7 does not cover the target cleanly.
Default workflow: Context7 first
Fallback script workflow:
# 1. DETECT query type (topic-specific vs general)
node scripts/detect-topic.js "<user query>"
# 2. FETCH documentation using script output
node scripts/fetch-docs.js "<user query>"
# 3. ANALYZE results (if multiple URLs returned)
cat llms.txt | node scripts/analyze-llms-txt.js -
Scripts handle URL construction, fallback chains, and error handling automatically.
detect-topic.js - Classify query type
{topic, library, isTopicSpecific}fetch-docs.js - Retrieve documentation
test cases passed, vs 9 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.