langchain-builder
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Env read
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: langchain-builder
description: Build LangChain chains, agents, and tool integrations with scaffolding, validation, and template…
category: ai
runtime: Python
---
# langchain-builder output preview
## PART A: Task fit
- Use case: Build LangChain chains, agents, and tool integrations with scaffolding, validation, and template generation. Use when this capability is needed. Scaffolds LangChain projects, generates chain and agent configurations, creates prompt templates, and validates chain configs for common errors. Produces ready-to-run Python files with proper imports and structur….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “What it does / Commands / Examples” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Build LangChain chains, agents, and tool integrations with scaffolding, validation, and template generation. Use when this capability is needed. Scaffolds LangChain projects, generates chain and agent configurations, creates prompt templates, and validates chain configs for common errors. Produces ready-to-run Python files with proper imports and structur…”.
- **02** When the source has headings, the agent prioritizes “What it does / Commands / Examples” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, read environment variables; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, read environment variables; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, read environment variables.
Validate with a small run
Start with a small task and check whether the result follows “What it does / Commands / Examples”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: langchain-builder
description: Build LangChain chains, agents, and tool integrations with scaffolding, validation, and template…
category: ai
source: tomevault-io/skills-registry
---
# langchain-builder
## When to use
- Build LangChain chains, agents, and tool integrations with scaffolding, validation, and template generation. Use when…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “What it does / Commands / Examples” and keep inference separate from source facts.
- read files, write/modify files, read environment variables; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "langchain-builder" {
input -> user goal + target files + boundaries + acceptance criteria
context -> What it does / Commands / Examples
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, read environment variables | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}LangChain Builder
Build LangChain chains, agents, and tool integrations with scaffolding, validation, and template generation.
What it does
Scaffolds LangChain projects, generates chain and agent configurations, creates prompt templates, and validates chain configs for common errors. Produces ready-to-run Python files with proper imports and structure.
Commands
| Command | Description |
|---|---|
init <project> |
Scaffold a new LangChain project with requirements.txt and project structure |
chain <type> --name <n> |
Generate a chain template: llm, sequential, router |
agent <tools> |
Generate an agent config with specified tools (search, calculator, python_repl) |
prompt <description> |
Create a prompt template from a natural language description |
validate <file> |
Check a chain/agent Python file for common configuration errors |
Examples
python3 langchain_builder.py init my-rag-app
python3 langchain_builder.py chain llm --name summarizer
python3 langchain_builder.py agent search,calculator
python3 langchain_builder.py prompt "Summarize a document in 3 bullet points"
python3 langchain_builder.py validate my_chain.pyChain types
- llm — Single LLMChain with prompt + model
- sequential — SequentialChain connecting multiple LLMChains
- router — RouterChain that selects sub-chains based on input classification
Generated files include
- Proper
langchainimports (community packages where needed) - Environment variable loading via
python-dotenv - Error handling and type hints
- Runnable patterns (LangChain Expression Language where applicable)
Source: Danielhogben/hermes-skills — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review