Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问许可证需确认审计提醒

searching-messages搜索消息

Agent Skill

searching-messages 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

1,317

周安装

56

GitHub Stars

2,401

下载量

461
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

unknown

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:searching-messages(搜索消息)
来源仓库:https://github.com/letta-ai/letta-code
仓库路径:skills/searching-messages
安装命令:
npx skills add https://github.com/letta-ai/letta-code --skill searching-messages
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。

skills.shnpx skills
npx skills add https://github.com/letta-ai/letta-code --skill searching-messages

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 支持基于关键词、任务场景或来源线索进行信息检索与筛选。
  • 可通过 npx skills add 命令从 GitHub 仓库安装使用。
  • 建议确认权限范围和维护状态,注意是否触发联网或文件读写操作。
  • searching-messages 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Searching Messages

This skill helps you search through past conversations to recall context that may have fallen out of your context window.

When to Use This Skill

  • User asks "do you remember when we discussed X?"
  • You need context from an earlier conversation
  • User references something from the past that you don't have in context
  • You want to verify what was said before about a topic
  • You need to find which agent discussed a specific topic (use with finding-agents skill)

CLI Usage

letta messages search --query <text> [options]

Options

OptionDescription
--query <text>Search query (required)
--mode <mode>Search mode: vector, fts, hybrid (default: hybrid)
--start-date <date>Filter messages after this date (ISO format)
--end-date <date>Filter messages before this date (ISO format)
--limit <n>Max results (default: 10)
--all-agentsSearch all agents, not just current agent
--agent <id>Explicit agent ID (overrides LETTA_AGENT_ID)
--agent-id <id>Alias for --agent

Search Modes

  • hybrid (default): Combines vector similarity + full-text search with RRF scoring
  • vector: Semantic similarity search (good for conceptual matches)
  • fts: Full-text search (good for exact phrases)

Companion Command: messages list

Use this to expand around a found needle by message ID cursor:

letta messages list [options]
OptionDescription
--after <message-id>Get messages after this ID (cursor)
--before <message-id>Get messages before this ID (cursor)
`--order <asc\desc>`Sort order (default: desc = newest first)
--limit <n>Max results (default: 20)
--agent <id>Explicit agent ID (overrides LETTA_AGENT_ID)
--agent-id <id>Alias for --agent

Search Strategies

Strategy 1: Needle + Expand (Recommended)

Use when you need full conversation context around a specific topic:

  1. Find the needle - Search with keywords to discover relevant messages: letta messages search --query "flicker inline approval" --limit 5
  2. Note the message_id - Find the most relevant result and copy its message_id
  3. Expand before - Get messages leading up to the needle: letta messages list --before "message-xyz" --limit 10
  4. Expand after - Get messages following the needle (use --order asc for chronological): letta messages list --after "message-xyz" --order asc --limit 10

Strategy 2: Date-Bounded Search

Use when you know approximately when something was discussed:

letta messages search --query "topic" --start-date "2025-12-31T00:00:00Z" --end-date "2025-12-31T23:59:59Z" --limit 15

Results are sorted by relevance within the date window.

Strategy 3: Broad Discovery

Use when you're not sure what you're looking for:

letta messages search --query "vague topic" --mode vector --limit 10

Vector mode finds semantically similar messages even without exact keyword matches.

Strategy 4: Find Which Agent Discussed Something

Use with --all-agents to search across all agents and identify which one discussed a topic:

letta messages search --query "authentication refactor" --all-agents --limit 10

Results include agent_id for each message. Use this to:

  1. Find the agent that worked on a specific feature
  2. Identify the right agent to ask follow-up questions
  3. Cross-reference with the finding-agents skill to get agent details

Tip: Load both searching-messages and finding-agents skills together when you need to find and identify agents by topic.

Search Output

Returns search results with:

  • message_id - Use this for cursor-based expansion
  • message_type - user_message, assistant_message, reasoning_message
  • content or reasoning - The actual message text
  • created_at - When the message was sent (ISO format)
  • agent_id - Which agent the message belongs to

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

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

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。

平台分布

Codex

35.31%
按下载量换算163

Claude

30.02%
按下载量换算138

Cursor

19.61%
按下载量换算90

Gemini CLI

9.09%
按下载量换算42

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

继续浏览同类 Skills