langchain-performance-tuning
Optimize LangChain application performance: latency, throughput, streaming, caching, batch processing, and connection pooling. Trigger: "langchain performance", "langchain optimization", "langchain latency", "langchain slow", "speed up langchain".
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 communicates its purpose, lists specific optimization techniques, and provides explicit trigger terms. The description is concise yet comprehensive, covering both what the skill does and when it should be activated. The only minor note is that it uses a 'Trigger:' label rather than the more conventional 'Use when...' phrasing, but the intent is equally clear.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: latency optimization, throughput improvement, streaming, caching, batch processing, and connection pooling. These are all distinct, concrete performance optimization techniques. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize LangChain performance across latency, throughput, streaming, caching, batch processing, connection pooling) and 'when' (explicit trigger terms listed). The 'Trigger:' clause serves as an explicit 'Use when' equivalent. | 3 / 3 |
Trigger Term Quality | Includes excellent natural trigger terms that users would actually say: 'langchain performance', 'langchain optimization', 'langchain latency', 'langchain slow', 'speed up langchain'. These cover both technical and colloquial phrasings. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — scoped specifically to LangChain performance optimization, which is a clear niche. The combination of 'LangChain' + 'performance/optimization' makes it very unlikely to conflict with general coding skills or other LangChain skills focused on different aspects. | 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, actionable performance tuning guide with excellent executable code examples covering the key optimization areas. Its main weaknesses are the lack of a validation/feedback loop (benchmark → optimize → re-benchmark → compare) and some verbosity in explanatory comments that Claude doesn't need. The inline content is comprehensive but could benefit from splitting detailed implementations into referenced files.
Suggestions
Add an explicit re-benchmarking validation step after applying optimizations (e.g., 'Step 7: Re-run benchmark from Step 1, compare results, iterate if targets not met') to create a feedback loop.
Remove explanatory comments that state the obvious (e.g., '// Non-streaming: user waits 2-3s', '// Shorter prompts = fewer input tokens') — Claude already understands these concepts.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Generally efficient with good code examples, but includes some unnecessary commentary (e.g., 'Shorter prompts = fewer input tokens = lower latency + cost' is obvious to Claude) and the verbose vs concise prompt comparison is somewhat padded. The inline comments explaining what streaming/batching do are slightly redundant. | 2 / 3 |
Actionability | Provides fully executable TypeScript and Python code examples throughout, with concrete model names, pricing, specific API calls, and copy-paste ready patterns including SSE streaming endpoints, batch processing, caching implementations, and benchmarking utilities. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced from benchmarking through optimization, but there are no validation checkpoints or feedback loops. After applying optimizations, there's no explicit step to re-benchmark and verify improvements, which is important for performance tuning workflows. | 2 / 3 |
Progressive Disclosure | Content is well-structured with clear headers and a summary table, but the skill is quite long (~150+ lines of content) with all details inline. The caching section covers three languages/approaches that could be split out. References to external docs are present but the 'Next Steps' cross-reference to another skill is good. | 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 | |
- Commit
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