jbvc/cost-aware-llm-pipeline
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
73
73%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description is strong in specificity and distinctiveness, listing concrete optimization patterns for a well-defined domain. Its main weaknesses are the lack of an explicit 'Use when...' clause and missing some natural user-facing trigger terms like 'save money on API calls' or 'reduce token costs'. Adding explicit trigger guidance would significantly improve skill selection accuracy.
Suggestions
Add a 'Use when...' clause, e.g., 'Use when the user wants to reduce LLM API costs, optimize token usage, or implement cost-efficient model selection.'
Include more natural user-facing trigger terms such as 'save money', 'API costs', 'token usage', 'reduce spending', 'cheaper models', and 'rate limits'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: model routing by task complexity, budget tracking, retry logic, and prompt caching. These are distinct, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric guidelines. | 2 / 3 |
Trigger Term Quality | Includes relevant terms like 'LLM API', 'cost optimization', 'model routing', 'prompt caching', and 'retry logic', but misses common user-facing variations like 'save money', 'API costs', 'token usage', 'rate limits', or 'cheaper models'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of LLM API cost optimization with specific patterns like model routing, budget tracking, and prompt caching creates a clear niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 10 / 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 executable code examples covering four composable cost-optimization patterns. Its main weaknesses are moderate verbosity (redundant sections, over-explanation) and lack of explicit validation/feedback loops in the workflow for batch processing scenarios. The content would benefit from trimming duplicate sections and adding verification steps.
Suggestions
Merge 'When to Activate' and 'When to Use' into a single brief section, and consolidate 'Best Practices' and 'Anti-Patterns' to eliminate redundancy.
Add an explicit validation/feedback loop in the composition workflow — e.g., checking result quality and potentially re-routing to a more capable model if the cheaper model's output is insufficient.
Consider moving the pricing table and detailed code implementations to referenced files, keeping SKILL.md as a concise overview with the composition pattern and quick-reference examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some redundant sections ('When to Activate' and 'When to Use' overlap significantly), the anti-patterns section largely mirrors the best practices in negated form, and some explanatory comments are unnecessary for Claude (e.g., explaining what immutable means, why to use frozen dataclasses). | 2 / 3 |
Actionability | All four core patterns include fully executable Python code with concrete implementations. The composition section shows how to wire them together, and the pricing table provides specific numbers needed for cost calculations. | 3 / 3 |
Workflow Clarity | The composition section shows a clear 4-step sequence, and the budget check acts as a validation checkpoint. However, there's no explicit validation/verification step after processing (e.g., checking if the result quality is acceptable before continuing a batch), and no feedback loop for adjusting thresholds based on results. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and logical sections, but everything is inline in a single file. The pricing table and detailed code examples could be split into referenced files, and the anti-patterns/best-practices sections add bulk that could be condensed or externalized. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
Reviewed
Table of Contents