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sql-memorySQL 记忆

Agent Skill

用于辅助数据库表结构、查询语句、迁移脚本和数据维护任务。它适合让 Agent 分析 schema、编写 SQL、排查查询问题、整理索引或生成迁移建议。使用时需要明确数据库类型、连接环境和目标表,区分只读分析与写入变更;涉及删除、更新、迁移和批量导入时,应优先 dry-run、备份或事务保护,避免误操作。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sql-memory(SQL 记忆)
来源仓库:https://github.com/oblio-falootin/sql-memory
安装命令:
openclaw skills install sql-memory
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install sql-memory

简介

OpenClaw 代理的语义记忆持久化存储层。

  • 保存具有重要性评分的代理记忆和历史记录。
  • 支持分层记忆汇总如每日到月度的聚合。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 用于长期知识管理和上下文延续。sql-memory 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适用于需要记忆保持的任务型 Agent 系统。

SKILL.md

name
sql-memory
description
Semantic memory layer for OpenClaw agents. Use when: (1) persisting agent memories with importance scoring, (2) hierarchical memory rollups (daily→weekly→monthly→yearly), (3) queuing tasks for agents, (4) logging activity and audit trails, (5) managing knowledge bases with semantic search. Provides remember/recall/search/queue_task/log_event APIs. Built on sql-connector for reliable parameterized SQL execution.

SQL Memory Skill

Semantic memory layer for OpenClaw agents

Overview

Provides agent-friendly memory operations: remember, recall, search, forget, plus task queue management, knowledge indexing, activity logging, and hierarchical memory rollups. All operations go through the SQL Connector skill for reliable, parameterized SQL execution.

See scripts/sql_memory.py for full implementation.

Dependencies

  • sql-connector — provides the underlying database connection and query execution

Quick Start

from sql_memory import SQLMemory, get_memory

mem = get_memory('cloud')

# Remember something
mem.remember('facts', 'vex_timezone', 'VeX is in EST/EDT timezone', importance=7)

# Recall it
entry = mem.recall('facts', 'vex_timezone')

# Search across all memories
results = mem.search_memories('timezone')

# Queue a task
mem.queue_task('nlp_agent', 'analyze_document', '{"doc": "..."}', priority=3)

# Log an event
mem.log_event('training_complete', 'nlp_agent', 'Finished training cycle 42')

# Store knowledge
mem.store_knowledge('stamps', 'inverted_jenny', 'Rare 1918 misprint...', 'catalog')

Schema

All tables live in the memory schema (SQL Server database):

TablePurpose
memory.MemoriesLong-term curated memories with importance scoring
memory.TaskQueueTask queue for agent work items
memory.ActivityLogEvent/activity logging for audit trail
memory.KnowledgeIndexDomain-specific knowledge store
memory.SessionsSession tracking for agents

Memory Rollups

Hierarchical consolidation keeps memories fresh and relevant:

Daily memories → Weekly rollup (Sundays 3AM)
Weekly rollups → Monthly rollup (1st of month)
Monthly → Quarterly (Jan/Apr/Jul/Oct)
Quarterly → Yearly (Jan 1st)

Each rollup:

  1. Summarizes source entries
  2. Creates a consolidated entry with back-references
  3. Reduces importance of source entries
  4. Tags sources as rolled_up

Importance Scale

LevelMeaningExample
1-2Ephemeral, archiveOld workspace file
3-4Context, nice-to-knowDebug notes
5-6Standard operationalTask completion
7-8Important milestoneArchitecture decision
9CriticalSystem design choice
10PermanentCore identity/values

API Reference

Memory Operations

MethodDescriptionExample
remember(cat, key, content, importance, tags)Store a memorymem.remember('facts', 'name', 'Oblio', 7)
recall(cat, key)Retrieve a memorymem.recall('facts', 'name')
search_memories(query, limit)Semantic searchmem.search_memories('timezone', limit=5)
forget(cat, key)Delete a memorymem.forget('facts', 'name')

Task Queue

MethodDescription
queue_task(agent, type, payload, priority)Add a task
claim_task(id)Mark task as processing
complete_task(id, result)Mark task as completed
fail_task(id, error, retries, max)Fail with retry logic

Activity Logging

MethodDescription
log_event(type, agent, detail, extra)Log an activity
get_recent_activity(hours, agent)Query recent events

Configuration

Uses the same environment variables as sql-connector:

SQL_CLOUD_SERVER=sql5112.site4now.net
SQL_CLOUD_DATABASE=db_99ba1f_memory4oblio
SQL_CLOUD_USER=...
SQL_CLOUD_PASSWORD=...

SQL_LOCAL_SERVER=10.0.0.110
SQL_LOCAL_DATABASE=Oblio_Memories
SQL_LOCAL_USER=sa
SQL_LOCAL_PASSWORD=...

Architecture

┌──────────────────┐
│   Agents         │ ← OblioAgent subclasses
├──────────────────┤
│   SQLMemory      │ ← Semantic operations (remember/recall/queue/log)
├──────────────────┤
│   SQLConnector   │ ← Generic SQL execution (retry, parameterized, logging)
├──────────────────┤
│   pymssql (TDS)  │ ← Native SQL Server driver
└──────────────────┘

License

MIT

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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安装前确认

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