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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.

57

Quality

72%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Overview
Quality
Evals
Security
Files

Quality

Content

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 concrete executable code for all four cost optimization patterns and a clear composition example. Its main weaknesses are moderate redundancy (overlapping sections for when-to-use and anti-patterns vs best practices), and missing validation/feedback loops in the workflow for handling partial batch failures or verifying cost tracking accuracy. The pricing table with specific dates is a minor concern for maintenance.

Suggestions

Remove the 'When to Use' section since it duplicates 'When to Activate', and consolidate 'Anti-Patterns' into 'Best Practices' as brief 'Don't' bullets to reduce redundancy.

Add explicit validation checkpoints to the composition workflow — e.g., verify cost calculation after each API call, handle partial batch failures with a recovery strategy, and include a post-batch cost summary check.

DimensionReasoningScore

Conciseness

The skill is mostly efficient with good code examples, but includes some redundancy: 'When to Activate' and 'When to Use' sections overlap significantly, 'Anti-Patterns to Avoid' largely mirrors 'Best Practices' in negated form, and some explanatory text (e.g., describing what prompt caching does) is unnecessary for Claude.

2 / 3

Actionability

All four core patterns include fully executable Python code with concrete examples. The composition section shows how to wire them together in a real pipeline function. Model names, thresholds, pricing, and retry logic are all specific and copy-paste ready.

3 / 3

Workflow Clarity

The composition section shows a clear 4-step sequence, but lacks explicit validation checkpoints — there's no verification that the budget check is accurate before proceeding, no guidance on what to do when a batch partially fails, and no feedback loop for adjusting model routing thresholds based on results.

2 / 3

Progressive Disclosure

The content is well-structured with clear headers and logical sections, but it's a monolithic file with no references to supporting files. The pricing table, anti-patterns, and detailed code examples could be split into separate reference files. However, given no bundle files exist, the inline approach is somewhat justified.

2 / 3

Total

9

/

12

Passed

Description

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. Its main weaknesses are the absence of an explicit 'Use when...' clause and missing some natural user-facing trigger terms like 'save money' or 'reduce API costs' that users would commonly say.

Suggestions

Add a 'Use when...' clause, e.g., 'Use when the user wants to reduce LLM API costs, optimize token spending, or implement cost-efficient AI workflows.'

Include more natural user-facing trigger terms such as 'save money', 'reduce costs', 'API spending', 'token usage', 'cheaper API calls', or 'billing optimization'.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: model routing by task complexity, budget tracking, retry logic, and prompt caching. These are distinct, actionable capabilities rather than vague abstractions.

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-facing variations like 'save money', 'reduce costs', 'API spending', 'token usage', 'cheaper', or 'billing'.

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

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.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Reviewed

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