memory-curator
Distill verbose daily logs into compact, indexed digests. Use when managing agent memory files, compressing logs, creating summaries of past activity, or building index-first memory architectures.
Best use case
memory-curator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Distill verbose daily logs into compact, indexed digests. Use when managing agent memory files, compressing logs, creating summaries of past activity, or building index-first memory architectures.
Teams using memory-curator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/memory-curator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How memory-curator Compares
| Feature / Agent | memory-curator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Distill verbose daily logs into compact, indexed digests. Use when managing agent memory files, compressing logs, creating summaries of past activity, or building index-first memory architectures.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
AI Agents for Startups
Explore AI agent skills for startup validation, product research, growth experiments, documentation, and fast execution with small teams.
SKILL.md Source
# Memory Curator Transform raw daily logs (often 200-500+ lines) into ~50-80 line digests while preserving key information. ## Quick Start ```bash # Generate digest skeleton for today ./scripts/generate-digest.sh # Generate for specific date ./scripts/generate-digest.sh 2026-01-30 ``` Then fill in the `<!-- comment -->` sections manually. ## Digest Structure A good digest captures: | Section | Purpose | Example | |---------|---------|---------| | **Summary** | 2-3 sentences, the day in a nutshell | "Day One. Named Milo. Built connections on Moltbook." | | **Stats** | Quick metrics | Lines, sections, karma, time span | | **Key Events** | What happened (not everything, just what matters) | Numbered list, 3-7 items | | **Learnings** | Insights worth remembering | Bullet points | | **Connections** | People interacted with | Names + one-line context | | **Open Questions** | What you're still thinking about | For continuity | | **Tomorrow** | What future-you should prioritize | Actionable items | ## Index-First Architecture Digests work best with hierarchical indexes: ``` memory/ ├── INDEX.md ← Master index (scan first ~50 lines) ├── digests/ │ ├── 2026-01-30-digest.md │ └── 2026-01-31-digest.md ├── topics/ ← Deep dives └── daily/ ← Raw logs (only read when needed) ``` **Workflow:** Scan index → find relevant digest → drill into raw log only if needed. ## Automation Set up end-of-day cron to auto-generate skeletons: ``` Schedule: 55 23 * * * (23:55 UTC) Task: Run generate-digest.sh, fill Summary/Learnings/Tomorrow, commit ``` ## Tips - **Compress aggressively** — if you can reconstruct it from context, don't include it - **Names matter** — capture WHO you talked to, not just WHAT was said - **Questions persist** — open questions create continuity across sessions - **Stats are cheap** — automated extraction saves tokens on what's mechanical
Related Skills
Agent Memory Architecture
Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, and memory hygiene. No APIs, no databases, no external tools. Works with any agent framework.
memory-cache
High-performance temporary storage system using Redis. Supports namespaced keys (mema:*), TTL management, and session context caching. Use for: (1) Saving agent state, (2) Caching API results, (3) Sharing data between sub-agents.
Memory
Infinite organized memory that complements your agent's built-in memory with unlimited categorized storage.
auto-memory
Indestructible agent memory — permanently stored, never lost. Save decisions, identity, and context as a memory chain on the Autonomys Network. Rebuild your full history from a single CID, even after total state loss.
Triple-Layer Memory System
三层记忆系统 - 解决 AI Agent 长对话记忆丢失和上下文管理问题
agent-memory-setup
Set up the full OpenClaw agent memory system with 3-tier memory (HOT/WARM/COLD), daily logs, semantic search (QMD), and lossless context management (Lossless Claw). Use when onboarding a new agent, setting up memory for a fresh OpenClaw instance, or when asked to install the memory system on a new agent. Triggers on "set up memory", "install memory system", "onboard new agent memory", "memory setup", "agent onboarding", "configure agent memory", "add memory to my agent", "how do I set up memory", "initialize memory", "memory system for OpenClaw".
agent-memory-setup-v2
Create a 3-tier memory directory structure (HOT/WARM/COLD) for OpenClaw agents and configure the built-in memory-core plugin to use Google Gemini Embeddings 2 (gemini-embedding-2-preview) for semantic memory search. Creates memory/ directories and stub files only — no code execution or external API calls from the setup script. After setup, the agent's memory_search tool uses Gemini's cloud embedding API to index memory files. Requires a free Google Gemini API key. Use when setting up a new agent's memory system or asked about semantic memory search. Triggers on "set up memory", "memory setup", "agent memory", "gemini memory", "semantic search memory", "onboard new agent".
feishu-memory-recall
Cross-group memory, search, and event sharing for OpenClaw Feishu agents
Cortex — Graph Memory Skill
You have access to **Cortex**, a self-organizing knowledge graph for persistent memory. Use it to remember facts, decisions, goals, patterns, and observations across sessions. Knowledge is stored as nodes in a graph that auto-links, decays stale information, detects contradictions, and computes trust from topology.
openclaw-memory
No description provided.
maasv Memory
Structured long-term memory for OpenClaw agents, powered by [maasv](https://github.com/ascottbell/maasv).
QMD Memory Skill for OpenClaw
## Local Hybrid Search — Save $50-300/month in API Costs