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prompt-caching

You're a caching specialist who has reduced LLM costs by 90% through strategic caching. You've implemented systems that cache at multiple levels: prompt prefixes, full responses, and semantic similarity matches.

Quality

59%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/prompt-caching/SKILL.md

The canonical home for this skill is prompt-caching in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

47%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a terse, well-sectioned skeleton that respects token budget, but it contains no executable guidance: patterns and anti-patterns are bare one-liners, the sharp-edges 'solutions' are comment stubs, and the core principles list is literally truncated mid-word. It reads as an unfinished draft rather than an actionable skill.

Suggestions

Add concrete, executable guidance for each pattern: Anthropic cache_control breakpoint usage with TTL and minimum-token constraints, a code example for response caching keyed on normalized prompts, and how semantic-similarity matching is implemented.

Fix the truncated 'core principles' list (item '2. K' ends mid-word) and complete it with the intended principles.

Replace the templated 'When to Use' filler sentence with real applicability guidance (when to cache prefixes vs. full responses, when caching is counterproductive such as high temperature) and expand the anti-patterns beyond bare headings.

DimensionReasoningScore

Conciseness

The body is lean and well-sectioned with no padding of concepts Claude already knows, but the opening persona paragraph repeats the frontmatter description verbatim and the 'When to Use' section is a meaningless template sentence ("This skill is applicable to execute the workflow or actions described in the overview.") that could be trimmed.

4 / 5

Actionability

Guidance is almost entirely one-line description ("Use Claude's native prompt caching for repeated prefixes", "Pre-cache documents in prompt instead of RAG retrieval") with no code, commands, or API parameters, and the sharp-edges solutions are comment-style stubs ("// Optimize for cache misses, not just hits") rather than executable instructions.

2 / 5

Workflow Clarity

The only sequence is the numbered 'core principles' list, which is truncated mid-item ("2. K"), and there are no validation checkpoints or error-recovery loops for implementing and invalidating caches.

2 / 5

Progressive Disclosure

Sections (Capabilities, Patterns, Anti-Patterns, Sharp Edges, Related Skills) are clearly organized and no external bundle files exist to reference, but the filler 'When to Use' section and the broken principles list leave minor organization gaps.

4 / 5

Total

12

/

20

Passed

Description

53%Weight 40%Scale 1-5

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 communicates a clear niche (multi-level LLM caching) but is written as a second-person persona statement rather than a third-person capability description, and it contains no 'Use when...' trigger guidance. Concrete caching levels are named, but persona credentials pad the 'what' without adding actionable detail.

Suggestions

Rewrite in third person as a capability statement (e.g., 'Implements multi-level LLM caching: prompt-prefix caching, full-response caching, and semantic-similarity matching to reduce API costs') and drop the persona credential 'reduced LLM costs by 90%'.

Add an explicit trigger clause, e.g., 'Use when the user wants to reduce LLM/API costs, speed up repeated prompts, or implement prompt or response caching.'

Add natural keyword variations such as 'cache hits', 'API costs', and 'repeated prompts' to improve trigger term coverage.

DimensionReasoningScore

Specificity

Names the domain and concrete caching levels ("prompt prefixes, full responses, and semantic similarity matches"), but the framing is persona credential ("reduced LLM costs by 90%") rather than capability, and the second-person voice ("You're a caching specialist") reduces the score by 1 per the rubric guideline.

3 / 5

Completeness

The 'what' is conveyed (multi-level LLM caching) but there is no 'Use when...' clause or equivalent trigger guidance, capping completeness at 3 per the rubric guideline.

3 / 5

Trigger Term Quality

Includes some relevant natural keywords ("prompt caching", "LLM costs", "caching") but misses common variations users would say such as "reduce API costs", "cache hits", or "speed up repeated prompts".

3 / 5

Distinctiveness Conflict Risk

The LLM-specific caching focus ("prompt prefixes", "semantic similarity matches") is mostly distinct from generic caching skills, with only minor overlap risk against general cost-optimization skills.

4 / 5

Total

13

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills/skills/prompt-caching/SKILL.md

Reviewed

Table of Contents

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