Guidance
Skill AktivControl 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
To provide developers with a powerful tool for precisely controlling LLM output, guaranteeing valid structured formats like JSON and XML, and building complex multi-step generation workflows.
Funktionen
- Control LLM output syntax with regex and grammars
- Guarantee valid JSON/XML/code generation
- Enforce structured formats (dates, emails, IDs)
- Build multi-step workflows with Pythonic control flow
- Reduce latency vs traditional prompting
Anwendungsfälle
- When you need to guarantee valid JSON output for an API response.
- When enforcing a specific date or email format for user input.
- When building agent workflows that require structured intermediate thoughts or observations.
- When reducing token waste and latency by directly generating valid outputs.
Nicht-Ziele
- Performing LLM inference directly; it relies on configured backends.
- Handling arbitrary file system operations or system commands.
- Replacing the core functionality of LLM providers like OpenAI or Anthropic.
Trust
- warning:Issues AttentionIn the last 90 days, 17 issues were opened and 4 were closed, indicating a low closure rate of 23.5%, suggesting slow response times for open issues.
Execution
- info:Pinned dependenciesDependencies are listed in SKILL.md but not explicitly pinned with lockfiles, which could lead to versioning conflicts.
Installation
npx skills add davila7/claude-code-templatesFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
Vertrauenssignale
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