langchain-sdk-patterns
Apply production-ready LangChain SDK patterns for structured output, fallbacks, batch processing, streaming, and caching. Trigger: "langchain SDK patterns", "langchain best practices", "idiomatic langchain", "langchain architecture", "withStructuredOutput", "withFallbacks", "abatch".
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 strong skill description that clearly identifies its domain (LangChain SDK), lists specific capabilities (structured output, fallbacks, batch processing, streaming, caching), and provides explicit trigger terms including both natural language phrases and specific API method names. The description is concise, uses third-person voice, and would be easily distinguishable from other skills in a large skill library.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: structured output, fallbacks, batch processing, streaming, and caching. These are clearly defined capabilities within the LangChain SDK domain. | 3 / 3 |
Completeness | Clearly answers 'what' (apply production-ready LangChain SDK patterns for structured output, fallbacks, batch processing, streaming, and caching) and 'when' via an explicit 'Trigger:' clause listing specific trigger terms. The trigger clause serves the same function as a 'Use when...' clause. | 3 / 3 |
Trigger Term Quality | Includes a strong mix of natural language terms ('langchain best practices', 'langchain architecture') and specific technical terms users would actually type ('withStructuredOutput', 'withFallbacks', 'abatch', 'idiomatic langchain'). Good coverage of how users would phrase requests. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — focuses specifically on LangChain SDK patterns with named methods like 'withStructuredOutput', 'withFallbacks', and 'abatch'. Unlikely to conflict with general coding skills or other framework-specific skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%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 code examples covering key LangChain patterns. Its main weaknesses are length (could benefit from splitting patterns into separate files with a concise overview) and the lack of multi-step workflow guidance with validation checkpoints. The mixed TypeScript/Python without clear rationale slightly hurts conciseness.
Suggestions
Split the 7 patterns into a separate PATTERNS.md reference file and keep SKILL.md as a concise overview with one quick-start example and links to each pattern.
Add a brief note explaining why Pattern 6 is in Python while all others are TypeScript, or provide a TypeScript equivalent for consistency.
For Pattern 3 (batch processing), add a validation step such as checking results.length === inputs.length or handling partial failures, since batch operations should include verification.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code examples, but it's quite long (~180 lines) and some patterns like the manual retry wrapper and RunnableLambda could be more terse. The anti-patterns and error handling tables add value but the overall volume is high for a skill file. Mixing Python and TypeScript without clear justification also adds unnecessary cognitive load. | 2 / 3 |
Actionability | Every pattern includes fully executable, copy-paste ready code with correct imports, concrete model names, and realistic examples. The code is complete TypeScript/Python, not pseudocode, and includes inline comments explaining behavior (e.g., cache hit timing, concurrency control). | 3 / 3 |
Workflow Clarity | Each pattern is presented as an independent recipe rather than a sequenced workflow. There are no validation checkpoints or feedback loops for combining patterns or handling failures across a multi-step pipeline. The error handling table helps but doesn't constitute a workflow with explicit verification steps. | 2 / 3 |
Progressive Disclosure | The content is structured with clear headings and a resources section linking to external docs, plus a 'Next Steps' pointer. However, the file is monolithic — all 7 patterns are inline when some could be split into separate reference files. The anti-patterns and error tables could also be externalized to keep the skill file leaner. | 2 / 3 |
Total | 9 / 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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