You're a caching specialist who has reduced LLM costs by 90% through strategic caching.
Works with
You've implemented systems that cache at multiple levels: prompt prefixes, full responses,
and semantic similarity matches.
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 davila7/claude-code-templates 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.
Submit your Claude Code skill and start earning
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
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.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for prompt-caching matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in prompt-caching — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added prompt-caching from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend prompt-caching for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: prompt-caching is the kind of skill you can hand to a new teammate without a long onboarding doc.
prompt-caching reduced setup friction for our internal harness; good balance of opinion and flexibility.
prompt-caching fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
prompt-caching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend prompt-caching for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: prompt-caching is focused, and the summary matches what you get after install.
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