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ralph-memories拉尔夫回忆

Agent Skill

ralph-memories 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

824

周安装

33

GitHub Stars

公开资料未说明

下载量

267
CodexClaudeCursorGemini CLI

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

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:ralph-memories(拉尔夫回忆)
来源仓库:https://github.com/mikeyobrien/ralph-orchestrator
仓库路径:skills/ralph-memories
安装命令:
npx skills add mikeyobrien/ralph-orchestrator --skill "ralph-memories"
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 npx skills 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。

AgentSkills.tonpx skills
npx skills add mikeyobrien/ralph-orchestrator --skill "ralph-memories"

简介

Ralph Memories 技能负责存储和检索代理的历史交互记录与经验数据。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中实现个性化响应优化。
  • 通过 GitHub 安装,使用 npx 命令添加 ralph-orchestrator 仓库中的对应模块。
  • 数据存储格式需与宿主系统兼容,定期清理过期记录以节省资源开销。
  • ralph-memories 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Ralph Memories

Persistent learning system for accumulated wisdom across sessions. Storage: .agent/memories.md.

When to Search Memories

Search BEFORE starting work when:

  • Entering unfamiliar code area → ralph tools memory search "area-name"
  • Encountering an error → ralph tools memory search -t fix "error message"
  • Making architectural decisions → ralph tools memory search -t decision "topic"
  • Something feels familiar → there might be a memory about it

Search strategies:

  • Start broad, narrow with filters: search "api"search -t pattern --tags api
  • Check fixes first for errors: search -t fix "ECONNREFUSED"
  • Review decisions before changing architecture: search -t decision

When to Create Memories

Create a memory when:

  • You discover how this codebase does things (pattern)
  • You make or learn why an architectural choice was made (decision)
  • You solve a problem that might recur (fix)
  • You learn project-specific knowledge others need (context)

Do NOT create memories for:

  • Session-specific state (use tasks instead)
  • Obvious/universal practices
  • Temporary workarounds

Memory Types

TypeFlagUse For
pattern-t pattern"Uses barrel exports", "API routes use kebab-case"
decision-t decision"Chose Postgres over SQLite for concurrent writes"
fix-t fix"ECONNREFUSED on:5432 means run docker-compose up"
context-t context"ralph-core is shared lib, ralph-cli is binary"

Discover Available Tags

Before searching or adding, check what tags already exist:

# See all memories with their tags
ralph tools memory list

# Extract unique tags (grep the file directly)
grep -o 'tags: [^|]*' .agent/memories.md | sort -u

Reuse existing tags for consistency. Common tag patterns:

  • Component names: api, auth, database, cli
  • Concerns: testing, performance, error-handling
  • Tools: docker, postgres, redis

Quick Reference

# Add memory (creates file if needed)
ralph tools memory add "content" -t pattern --tags tag1,tag2

# Search (start broad, narrow with filters)
ralph tools memory search "query"
ralph tools memory search -t fix "error message"
ralph tools memory search --tags api,auth

# List and show
ralph tools memory list
ralph tools memory list -t fix --last 10
ralph tools memory show mem-1737372000-a1b2

# Delete
ralph tools memory delete mem-1737372000-a1b2

# Prime for context injection
ralph tools memory prime --budget 2000
ralph tools memory prime --tags api,auth    # Prime specific tags only
ralph tools memory prime --recent 7         # Only last 7 days

Best Practices

  1. Be specific: "Uses barrel exports in each module" not "Has good patterns"
  2. Include why: "Chose X because Y" not just "Uses X"
  3. One concept per memory: Split complex learnings
  4. Tag consistently: Reuse existing tags when possible

Examples

# Pattern: discovered codebase convention
ralph tools memory add "All API handlers return Result<Json<T>, AppError>" -t pattern --tags api,error-handling

# Decision: learned why something was chosen
ralph tools memory add "Chose JSONL over SQLite: simpler, git-friendly, append-only" -t decision --tags storage,architecture

# Fix: solved a recurring problem
ralph tools memory add "cargo test hangs: kill orphan postgres from previous run" -t fix --tags testing,postgres

# Context: project-specific knowledge
ralph tools memory add "The /legacy folder is deprecated, use /v2 endpoints" -t context --tags api,migration

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

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。

平台分布

Claude Code

27.97%
按下载量换算75

trae

22.17%
按下载量换算59

Antigravity

16.98%
按下载量换算45

windsurf

13.74%
按下载量换算37

Codex

8.66%
按下载量换算23

Gemini CLI

3.89%
按下载量换算10

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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