prompt-compression
SolidToken-efficient prompt compression techniques for cost optimization
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Quality Score: 94/100
Skill Content
Details
- Author
- a5c-ai
- Repository
- a5c-ai/babysitter
- Created
- 4 months ago
- Last Updated
- today
- Language
- JavaScript
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
prompt-compressor
Compress verbose prompts & context before LLM processing. This skill should be used when input exceeds 1500 tokens, contains redundant phrasing, or includes unnecessary context. Reduces tokens by 40-60%.
optimizing-prompts
This skill optimizes prompts for Large Language Models (LLMs) to reduce token usage, lower costs, and improve performance. It analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more concise and effective. It is used when the user wants to reduce LLM costs, improve response speed, or enhance the quality of LLM outputs by optimizing the prompt. Trigger terms include "optimize prompt", "reduce LLM cost", "improve prompt performance", "rewrite prompt", "prompt optimization".
token-optimizer
Maximize Claude's output quality while minimizing input token usage. Use this skill whenever a user wants to compress prompts, reduce token consumption, extract maximum output from Claude, write high-density instructions, optimize system prompts, or improve AI communication efficiency. Trigger on phrases like "optimize my prompt", "too many tokens", "make this shorter but better", "get more from Claude", "compress this prompt", "write a better system prompt", "token efficient", or any request to improve how someone communicates with Claude or any LLM. Also trigger when building AI-powered tools, chatbots, agents, or any system where prompt cost or quality matters.
context-compression
When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request.
context-compression
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.