langchain-cost-tuning
Optimize LangChain API costs with token tracking, model tiering, caching, prompt compression, and budget enforcement. Trigger: "langchain cost", "langchain tokens", "reduce langchain cost", "langchain billing", "langchain budget", "token optimization".
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 specific capabilities (token tracking, model tiering, caching, prompt compression, budget enforcement) within a well-defined niche (LangChain API cost optimization). The explicit trigger terms cover natural user language variations effectively, and the description is concise without unnecessary padding.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: token tracking, model tiering, caching, prompt compression, and budget enforcement. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize LangChain API costs with specific techniques) and 'when' (explicit trigger terms listed with a 'Trigger:' clause that serves the same function as 'Use when'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'langchain cost', 'langchain tokens', 'reduce langchain cost', 'langchain billing', 'langchain budget', 'token optimization'. Good coverage of variations a user might naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific niche combining LangChain with cost optimization. The trigger terms are all LangChain-specific, making it very unlikely to conflict with general cost optimization or general LangChain 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, actionable skill with excellent executable code examples covering five distinct cost optimization strategies. Its main weaknesses are the monolithic structure (all strategies inline rather than split across files), time-sensitive pricing data that will become stale, and the lack of a clear workflow for validating that optimizations are actually reducing costs. The content would benefit from being more concise and better structured for progressive disclosure.
Suggestions
Add a validation/verification step showing how to compare costs before and after applying optimizations (e.g., using the CostTracker to measure baseline vs. optimized costs).
Move the pricing table to a separate reference file or add a note that it should be verified against current provider pricing pages, since hardcoded prices will become stale.
Split detailed code examples into separate files (e.g., CACHING.md, MODEL_TIERING.md) and keep SKILL.md as a concise overview with brief snippets and links to each strategy.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary elements: the pricing table with specific 2026 dates will become stale, the verbose prompt example in Strategy 4 is intentionally padded to illustrate a point but adds bulk, and some inline comments explain obvious things. The overall structure is reasonable but could be tightened. | 2 / 3 |
Actionability | All five strategies include fully executable, copy-paste ready code examples in TypeScript and Python. The code is concrete with real imports, class definitions, and usage patterns. The cost optimization checklist and error handling table provide additional actionable guidance. | 3 / 3 |
Workflow Clarity | The strategies are presented as independent modules rather than a sequenced workflow, which is appropriate for a toolkit-style skill. However, there's no guidance on which strategies to apply first, no validation steps to verify cost savings are actually occurring, and no feedback loop for checking if the budget enforcer or caching is working correctly. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear section headers and a logical progression from tracking to enforcement. However, at ~200 lines it's quite long for a single file with no references to supplementary files for detailed content (e.g., the pricing table and detailed code examples could be split out). The 'Next Steps' reference to langchain-performance-tuning is a good touch but the main body is monolithic. | 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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