Claude Cookbooks Reference
2025Emma/vibe-coding-cn
Reference of Claude API examples and guides covering tool use, vision, RAG, classification, summarization, text-to-SQL, prompt caching and agent patterns.
Implements business-facing LLM calls as explicit, traceable workflows, keeping prompt versions, model policy, structured output and tracing scenarios in separate places.
Install LLM Generation Workflows
$ npx skills add lobehub/lobehub --skill llm-generation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lobehub/lobehub llm-generation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/lobehub/lobehub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/llm-generation .claude/skills/llm-generation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "llm-generation" agent skill from https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generation into .claude/skills/llm-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-generation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add lobehub/lobehub --skill llm-generation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lobehub/lobehub llm-generation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lobehub/lobehub.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/llm-generation .agents/skills/llm-generation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "llm-generation" agent skill from https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generation into .agents/skills/llm-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-generation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lobehub/lobehub --skill llm-generation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lobehub/lobehub llm-generation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lobehub/lobehub.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/llm-generation .cursor/skills/llm-generation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "llm-generation" agent skill from https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generation into .cursor/skills/llm-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-generation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/lobehub/lobehub.git --path .agents/skills/llm-generation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add lobehub/lobehub --skill llm-generation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lobehub/lobehub llm-generation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lobehub/lobehub.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/llm-generation .gemini/skills/llm-generation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "llm-generation" agent skill from https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generation into .gemini/skills/llm-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-generation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install lobehub/lobehub llm-generationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add lobehub/lobehub --skill llm-generation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lobehub/lobehub.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/llm-generation .github/skills/llm-generation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "llm-generation" agent skill from https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generation into .github/skills/llm-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-generation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lobehub/lobehub --skill llm-generation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lobehub/lobehub llm-generation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lobehub/lobehub.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/llm-generation .opencode/skills/llm-generation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "llm-generation" agent skill from https://github.com/lobehub/lobehub/tree/canary/.agents/skills/llm-generation into .opencode/skills/llm-generation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-generation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
llm-generation- GitHub stars
- 83k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 533 words
- Files
- 2
- Skills in repo
- 50
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Implements business-facing LLM calls as explicit, traceable workflows, keeping prompt versions, model policy, structured output and tracing scenarios in separate places.
- Works in 5 steps: Assert the emitted scenario,… → Test the prompt's important behavioral… → Test structured-output validation and… → …
- Adding a new structured LLM call to a server service
- SKILL.md covers Locate the Existing Boundary, Prompt Ownership and Versioning, Scenario Semantics and Model Policy, plus 2 more sections
- Calls bun
What it does
The skill covers application prompts, generateObject and generateText calls, model selection and generation tracing in LobeHub, but not provider adapters or agent snapshots. Before editing a call, the agent inspects the existing prompts package, the server-side structured generation wrapper, the constants file of tracing scenario names, the tracing package and the owning service. Reusable generation contracts, meaning the message builder, JSON schema, schema name and prompt version, are kept together in the prompts package, while model configuration, Zod validation, persistence and error handling stay in the server service.
Prompt versions are written as v followed by a major, or major and minor, number such as v1 or v1.2, stored on their own without a feature prefix, and bumped when a change should start a new evaluation or tracing cohort. A scenario names a stable product workflow and lifecycle stage. The agent checks the existing list first, reuses a scenario only for the same user-visible workflow, and adds a new one instead of borrowing a nearby one, which would contaminate latency, cost and quality data.
When your agent uses it
- Adding a new structured LLM call to a server service
- Versioning or changing an application prompt
- Choosing or adding a tracing scenario for a generation
Example prompts
- “Add a generateObject call that drafts goal criteria and trace it under the right scenario.”
- “Bump the prompt version for the summary chain because its output schema changed.”
- “Which tracing scenario should this verification-planning call use?”
Requirements
- A checkout of the LobeHub repository
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Assert the emitted scenario, promptVersion, and schemaName where applicable.
- Test the prompt's important behavioral constraints without snapshotting the entire prose.
- Test structured-output validation and relevant failure behavior.
- Search for stale inline prompts, old version strings, and incorrectly reused scenarios.
- Run bun run check and bun run check --type for cross-package changes.
What it can do on your machine
Read from SKILL.md and the folder at commit 35d442e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
bun
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
LLM Generation Workflows loads about 1.1k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 533 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 533 words (~1,089 tokens).
“Implement business-facing LLM calls as explicit, independently observable workflows. Keep prompt identity, model policy, structured output, and tracing responsibilities separate.”
- name
- llm-generation
Files
SKILL.md and 1 other file in .agents/skills/llm-generation of lobehub/lobehub.
- SKILL.md
- agents/openai.yaml
Open the folder on GitHubat commit 35d442e
Compare with similar skills
LLM Generation Workflows next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| LLM Generation Workflows this skilllobehub/lobehub | 83k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Claude Cookbooks Reference2025Emma/vibe-coding-cn | 23k | 1 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Prompt Engineering Patternswshobson/agents | 40k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Add AI Chat Toolryokun6/ryos | 1.3k | — | ~2.2k | Automated safety check: Pass | AGPL-3.0 | |
| Guidance Constrained GenerationOrchestra-Research/AI-Research-SKILLs | 13k | 5 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Lintlang Audithermes-labs-ai/lintlang | 138 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 |
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Prompt Engineering Patterns
wshobson/agents
Reference for designing and tuning production LLM prompts: few-shot examples, chain-of-thought, structured outputs, templates and system prompts.
Add AI Chat Tool
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Guidance Constrained Generation
Orchestra-Research/AI-Research-SKILLs
Constrains language model output with regex, selections and grammars using the Guidance library, so JSON, XML, code or formatted fields come out valid.
Lintlang Audit
hermes-labs-ai/lintlang
Audit a named AI agent config, system prompt, tool-definition or instruction file (YAML, JSON, Markdown, text, or Python) with the released LintLang CLI, on request.
Sap AI Core
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Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP.
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Audits stale Git worktrees and branches with a bundled script, classifies each one, and deletes only after you approve the exact candidates.
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Related
Works with
Categories
Questions about LLM Generation Workflows
What does LLM Generation Workflows do?
Implements business-facing LLM calls as explicit, traceable workflows, keeping prompt versions, model policy, structured output and tracing scenarios in separate places. The skill covers application prompts, generateObject and generateText calls, model selection and generation tracing in LobeHub, but not provider adapters or agent snapshots. Before editing a call, the agent inspects the existing prompts package, the server-side structured generation wrapper, the constants file of tracing scenario names, the tracing package and the owning service.
When should I use LLM Generation Workflows?
LLM Generation Workflows fits situations like: adding a new structured LLM call to a server service; versioning or changing an application prompt; choosing or adding a tracing scenario for a generation.
How do I install LLM Generation Workflows in Claude Code?
Run `npx skills add lobehub/lobehub --skill llm-generation -a claude-code`. Or copy the skill folder (.agents/skills/llm-generation in lobehub/lobehub) into .claude/skills/llm-generation in your project. Claude Code loads it when a task matches its description.
How do I install LLM Generation Workflows in Codex?
Run `npx skills add lobehub/lobehub --skill llm-generation -a codex`. Or copy the skill folder (.agents/skills/llm-generation in lobehub/lobehub) into .agents/skills/llm-generation in your project. Codex loads it when a task matches its description.
Can I use LLM Generation Workflows in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lobehub/lobehub --skill llm-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llm-generation, .gemini/skills/llm-generation, .github/skills/llm-generation and .opencode/skills/llm-generation in your project.
What does LLM Generation Workflows need to run?
Going by SKILL.md and its folder, LLM Generation Workflows needs the command-line tools its instructions call (bun). Our summary lists: A checkout of the LobeHub repository.
Does LLM Generation Workflows access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is LLM Generation Workflows safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does LLM Generation Workflows use?
LLM Generation Workflows has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does LLM Generation Workflows use?
About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to LLM Generation Workflows?
Skills that share tags, products or a category with LLM Generation Workflows: Claude Cookbooks Reference (2025Emma/vibe-coding-cn, 23k stars), Prompt Engineering Patterns (wshobson/agents, 40k stars), Add AI Chat Tool (ryokun6/ryos, 1.3k stars) and Guidance Constrained Generation (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains LLM Generation Workflows?
lobehub (a GitHub organization) maintains it in lobehub/lobehub, which has 83,074 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on October 9, 2026.
Source: lobehub/lobehub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.