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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
Passed 22 of 23 test cases with the skill, and 13 of 23 without it.
and 6 more in the full report
9 fixed13 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
What pip package installation command includes optional dependencies for Hugging Face local models alongside base guidance?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies pip install guidance[transformers] | Missed | Passed |
The instructions your model reads when the skill loads.
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal => bashweb_extract => web_fetch, plus web_search when discovery is neededsearch_files => grep_search and glob_searchbrowser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitlyWhen the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Use Guidance when you need to:
GitHub Stars: 18,000+ | From: Microsoft Research
# Base installation
pip install guidance
# With specific backends
pip install guidance[transformers] # Hugging Face models
pip install guidance[llama_cpp] # llama.cpp models
test cases passed, vs 13 / 23 without
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