memory-summarization
SolidConversation summarization for memory compression and context management
Install
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
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
summarization
Use when condensing prose while preserving meaning: session findings, wrap reports, research briefs, executive summaries, TLDRs, agent handoffs, progressive summaries, audit summaries, and long-document distillation. Covers extractive vs abstractive summarization, what to keep vs drop, evidence preservation, summary levels, handoff summaries, and audit-report condensation without hiding findings. Do NOT use for byte/data compression algorithms (use `compression`), context-window budget math or compaction triggers (use `context-window`), working-set selection (use `context-management`), prose tone repair (use `writing-humanizer`), or quality scoring (use `evaluation`).
langchain-memory
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
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.
recallmax
FREE — God-tier long-context memory for AI agents. Injects 500K-1M clean tokens, auto-summarizes with tone/intent preservation, compresses 14-turn history into 800 tokens.
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.