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talk-to

Agent Skill

talk-to 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

451

周安装

19

GitHub Stars

53

下载量

158
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:talk-to(与)
来源仓库:https://github.com/soul-brews-studio/oracle-skills-cli
仓库路径:skills/talk-to
安装命令:
npx skills add https://github.com/soul-brews-studio/oracle-skills-cli --skill talk-to
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/soul-brews-studio/oracle-skills-cli --skill talk-to

简介

talk-to 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围和维护状态,注意可能触发联网、命令执行或文件读写操作。
  • talk-to 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

/talk-to - Agent Messaging

Send messages to agents via Oracle threads. Each agent has a persistent channel thread.

Usage

/talk-to arthur "What's your status?"          # one-shot message
/talk-to arthur --new "Hey, starting fresh"    # skip lookup, create new thread
/talk-to arthur loop ask about their work      # autonomous conversation
/talk-to #42 "follow up on this"               # post to thread by ID
/talk-to --list                                # show channels

Mode 0: No arguments

If ARGUMENTS is empty, show usage help then run --list.

Routing

PatternUse
channel:{agent}Persistent per-agent channel
topic:{agent}:{slug}Topic-specific thread (with --topic)
#{id}Direct thread reference by ID

Mode 1: --list

  1. oracle_threads() (no status filter)
  2. Filter titles starting with channel: or topic:, exclude closed
  3. Display: channel:arthur (#42) pending — 12 msgs

Mode 2: --new (fast create)

Skip lookup. One MCP call.

  1. Compose message from intent
  2. oracle_thread({title: "channel:{agent}", message, role: "human"})
  3. Notify: Bash maw hey {agent}-oracle 'Thread #{id} from {self}: {preview}'

- If maw hey fails → warn only, don't error (thread already sent)

  1. Confirm: Created channel:{agent} (thread #{id})

Mode 3: One-shot (default)

  1. Compose message from intent
  2. If first arg is #{id} → post directly to that thread ID
  3. Otherwise: oracle_threads() → find channel:{agent}, create if missing
  4. Post message to thread
  5. Notify: Bash maw hey {agent}-oracle 'Thread #{id} from {self}: {preview}'

- If maw hey fails → warn only, don't error (thread already sent)

  1. oracle_thread_read({threadId}) → show any agent responses
  2. Confirm: Posted to channel:{agent} (thread #{id})

Mode 4: loop (autonomous conversation)

Like Ralph loop — AI drives the conversation autonomously. No user prompts between turns.

  1. Find or create thread (channel:{agent}, or --new to skip lookup)
  2. Compose opening message from user's intent and post it
  3. Autonomous loop (max 10 iterations): a. oracle_thread_read({threadId}) — check for new messages b. If agent responded: read their response, compose a thoughtful follow-up, post it c. If no new response: compose a follow-up question or probe deeper, post it d. After each exchange, briefly note what you learned e. Stop when: enough insight gathered, conversation circling, or 10 iterations hit
  4. Notify (once, after opening message): Bash maw hey {agent}-oracle 'Thread #{id} from {self}: {preview}'

- If maw hey fails → warn only, don't error

  1. Show summary: Conversation with {agent} (thread #{id}) — {n} messages, {iterations} turns Key insights: - [insight 1] - [insight 2]
  2. Leave thread open for future use

The goal is insight extraction. You are having a conversation on behalf of the human to learn something useful.

Parsing Rules

  • First arg = agent name (lowercase), #id (thread ref), or --list
  • --new = skip lookup, create fresh
  • loop = autonomous conversation (AI drives, no user prompts)
  • --topic "slug" = use topic:{agent}:{slug} instead of channel:{agent}
  • Everything else = the message/intent

Message Composition

CRITICAL: You are the composer. The user gives intent, you write the message.

  • Compose a clear, natural message from the user's intent
  • Post immediately — do NOT ask the user what to say
  • Do NOT use AskUserQuestion for message content
  • Show what you posted after sending

If the message already reads like a direct message (e.g. "What's your status?"), post as-is.

Auto Notification (maw hey)

After posting to a thread, notify the target agent via maw hey:

maw hey {agent}-oracle 'Thread #{id} from {self}: {first 60 chars of message}'
  • {self} = current Oracle's name (e.g. "Mother Oracle")
  • {agent} = target agent name (lowercase)
  • {preview} = first ~60 chars of the posted message
  • Runs once per /talk-to invocation (not per loop iteration)
  • Fail-safe: if maw hey errors, log warning and continue — the thread is the source of truth

Important Notes

  • Agent names are always lowercase
  • Thread titles are the routing key — never modify existing thread titles
  • One channel thread per agent (reuse, don't recreate)
  • #{id} lets users reference any thread directly — no lookup needed
  • All messages attributed with role: "human"

ARGUMENTS: $ARGUMENTS

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

Claude

32.73%
按下载量换算52

Codex

31.93%
按下载量换算50

Cursor

18.77%
按下载量换算30

Gemini CLI

9.23%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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

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