langchain-hello-world
Create a minimal working LangChain example with LCEL chains. Use when starting a new LangChain integration, testing your setup, or learning LCEL pipe syntax with prompts and output parsers. Trigger: "langchain hello world", "langchain example", "langchain quick start", "simple langchain code", "first langchain app".
85
83%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-crafted skill description with strong trigger terms and clear 'what/when' guidance. Its main weakness is that the capability description could be more specific about the concrete actions performed (e.g., setting up prompt templates, configuring output parsers, creating runnable chains). Overall it is effective for skill selection among many candidates.
Suggestions
Add more specific concrete actions to the 'what' portion, e.g., 'Sets up a prompt template, pipes it to a chat model, and parses output using LCEL chain syntax' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (LangChain, LCEL chains) and mentions some actions ('Create a minimal working example'), but doesn't list multiple specific concrete actions like 'set up prompt templates, chain with output parsers, configure model invocation'. | 2 / 3 |
Completeness | Clearly answers both 'what' (create a minimal working LangChain example with LCEL chains) and 'when' (starting a new LangChain integration, testing setup, learning LCEL pipe syntax) with explicit trigger phrases listed. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'langchain hello world', 'langchain example', 'langchain quick start', 'simple langchain code', 'first langchain app', plus mentions of 'LCEL pipe syntax', 'prompts', and 'output parsers'. | 3 / 3 |
Distinctiveness Conflict Risk | Very distinct niche targeting specifically LangChain LCEL hello-world examples. The specific trigger terms like 'langchain hello world' and 'LCEL pipe syntax' make it unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, highly actionable skill with excellent executable examples covering the key LCEL patterns. Its main weakness is that it's somewhat verbose for a 'hello world' — five examples plus explanatory tables push it beyond minimal, and the LCEL Runnable interface explanation adds tokens for concepts Claude already understands. Trimming to 2-3 examples and removing the conceptual explanation would make it leaner.
Suggestions
Reduce to 2-3 examples (e.g., basic chain, structured output, and Python equivalent) to match the 'minimal hello world' intent, moving streaming and chat examples to a separate intermediate skill.
Remove the 'How LCEL Works' section — Claude already understands the Runnable interface pattern, and the pipe/invoke semantics are self-evident from the examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable examples, but five examples plus an LCEL explanation table and error handling table make it somewhat verbose for a 'hello world' skill. The 'How LCEL Works' section explains Runnable interface concepts Claude already knows, and some examples are repetitive (Examples 1 and 5 are essentially the same pattern in different languages). | 2 / 3 |
Actionability | All five examples are fully executable, copy-paste ready code with correct imports, proper async/await usage, and realistic output comments. The error handling table provides specific error messages with concrete fixes. This is highly actionable. | 3 / 3 |
Workflow Clarity | For a simple skill focused on creating minimal working examples, the workflow is clear: prerequisites are stated, examples progress from simplest to more complex (basic chain → system prompt → structured output → streaming → Python equivalent), and error handling covers common pitfalls. No destructive or batch operations require validation checkpoints. | 3 / 3 |
Progressive Disclosure | The skill references a prerequisite skill ('langchain-install-auth') and a next step ('langchain-core-workflow-a'), and links to external docs. However, the content is somewhat monolithic — five examples plus reference tables inline could benefit from splitting advanced examples (structured output, streaming) into a separate file, keeping the 'hello world' truly minimal. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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