tdg-personal/cost-aware-llm-pipeline
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
77
77%
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, clearly listing concrete capabilities in a well-defined niche around LLM API cost optimization. Its main weaknesses are the absence of an explicit 'Use when...' clause and missing some natural user trigger terms (e.g., 'reduce costs', 'save money on API calls'). Adding explicit trigger guidance would significantly improve skill selection accuracy.
Suggestions
Add a 'Use when...' clause such as 'Use when the user wants to reduce LLM API costs, optimize token spending, or implement cost-efficient API call patterns.'
Include more natural user-facing trigger terms like 'reduce API costs', 'save money on tokens', 'cheaper API calls', 'token usage optimization', or 'API billing'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: model routing by task complexity, budget tracking, retry logic, and prompt caching. These are distinct, well-defined 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 dimension at 2 per the rubric. | 2 / 3 |
Trigger Term Quality | Includes relevant terms like 'cost optimization', 'LLM API', 'model routing', 'budget tracking', 'retry logic', 'prompt caching', but misses common user phrasings like 'reduce API costs', 'save money on tokens', 'cheaper API calls', or 'token usage'. | 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 some redundancy between sections (When to Activate/When to Use, Best Practices/Anti-Patterns), missing validation checkpoints in the workflow for batch processing scenarios, and all content being inline rather than using progressive disclosure for reference material like pricing tables.
Suggestions
Remove the redundant 'When to Use' section since it overlaps heavily with 'When to Activate', and consolidate anti-patterns into the best practices section as brief 'avoid' notes.
Add explicit validation/feedback steps to the composition workflow — e.g., verify cost calculation accuracy against API response usage metadata, and define behavior when budget is exceeded mid-batch (graceful degradation vs hard stop).
Move the pricing reference table to a separate PRICING.md file with a clear link, since pricing data is time-sensitive and benefits from isolated updates.
| 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 text is unnecessary for Claude (e.g., explaining what prompt caching does, what immutable means). | 2 / 3 |
Actionability | All four core patterns include fully executable Python code with concrete constants, type hints, and clear function signatures. The composition section shows how to wire them together in a real pipeline function. The pricing table provides specific numbers needed for implementation. | 3 / 3 |
Workflow Clarity | The composition section shows a clear 4-step sequence with budget checking before API calls, but lacks explicit validation checkpoints — there's no verification that cost tracking is accurate, no guidance on what to do when budget is exceeded mid-batch, and no feedback loop for adjusting thresholds based on results. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and numbered sections, but it's a monolithic file with no references to external files for deeper content. The pricing table and anti-patterns could be separated, and the skill would benefit from linking to detailed examples or configuration guides. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
Reviewed
Table of Contents