cost-aware-llm-pipeline

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

1.31x

Quality

66%

Does it follow best practices?

Impact

100%

1.31x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/cost-aware-llm-pipeline/SKILL.md

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 cost optimization patterns for LLM APIs. Its main weakness is the absence of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The trigger terms are somewhat technical and could benefit from more natural user-facing language.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about reducing LLM API costs, managing API budgets, or optimizing token spending.'

Include more natural user-facing trigger terms like 'save money on API calls', 'reduce token costs', 'API spending', or 'cheaper model selection'.

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-facing variations like 'save money', 'reduce costs', 'API spending', 'token usage', or 'cheaper'.	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 redundancy (overlapping sections for when to use, best practices vs anti-patterns) and missing validation/feedback loops in the workflow composition. The pricing table with specific dates is a maintenance concern but is reasonably scoped.

Suggestions

Merge 'When to Activate' and 'When to Use' into a single section, 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 response status, log model selection decisions, and handle partial batch failures with a recovery strategy.

Remove explanatory sentences that describe what concepts are (e.g., 'Cache long system prompts to avoid resending them on every request') since Claude already understands these patterns.

Dimension	Reasoning	Score
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 result is logged, 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 reasonably well-structured with clear headers and sections, but everything is inline in a single file with no references to supporting files. The pricing table and anti-patterns could be separated. However, given no bundle files exist, the monolithic approach is somewhat justified, though the file is long enough that splitting would help.	2 / 3
	Total	9 / 12 Passed

Validation

90%

Warnings & errors only

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

Repository: affaan-m/everything-claude-code
Commit: 841beea

Reviewed: 8 days ago

Table of Contents

Discovery Implementation Validation

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