Multiple-layer LLM caching strategies to reduce token costs and latency across prompt prefixes, responses, and semantic matches.
Works with
Supports three caching approaches: Anthropic's native prompt caching for repeated prefixes, response caching for identical or similar queries, and Cache Augmented Generation (CAG) for pre-cached documents
Includes cache invalidation patterns and guidance on structuring prompts for optimal caching performance
Highlights critical anti-patterns: caching with h
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionprompt-cachingExecute the skills CLI command in your project's root directory to begin installation:
Fetches prompt-caching from sickn33/antigravity-awesome-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate prompt-caching. Access via /prompt-caching in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
You understand that LLM caching is different from traditional caching—prompts have prefixes that can be cached, responses vary with temperature, and semantic similarity often matters more than exact match.
Your core principles:
Use Claude's native prompt caching for repeated prefixes
Cache full LLM responses for identical or similar queries
Pre-cache documents in prompt instead of RAG retrieval
| Issue | Severity | Solution |
|---|---|---|
| Cache miss causes latency spike with additional overhead | high | // Optimize for cache misses, not just hits |
| Cached responses become incorrect over time | high | // Implement proper cache invalidation |
| Prompt caching doesn't work due to prefix changes | medium | // Structure prompts for optimal caching |
Works well with: context-window-management, rag-implementation, conversation-memory
This skill is applicable to execute the workflow or actions described in the overview.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
sickn33/antigravity-awesome-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
prompt-caching fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
prompt-caching fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
prompt-caching fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added prompt-caching from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in prompt-caching — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
prompt-caching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
prompt-caching has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: prompt-caching is focused, and the summary matches what you get after install.
Useful defaults in prompt-caching — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: prompt-caching is the kind of skill you can hand to a new teammate without a long onboarding doc.
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