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session-bridge会话桥

Agent Skill

session-bridge 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

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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:session-bridge(会话桥)
来源仓库:https://github.com/codeinonenight/session-bridge
安装命令:
openclaw skills install session-bridge
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 session-bridge

简介

会话桥用于在切换界面或代理间移交任务时恢复上下文。

  • 适合解决 Telegram 到 WhatsApp 或 Jon 到 Eddie 等场景下的信息丢失问题。
  • 可根据关键词快速定位候选结果,提升跨环境协作连续性。
  • 使用前请确认来源仓库维护状态,避免依赖不稳定组件。
  • session-bridge 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
session-bridge
description
Use when context is lost after switching surfaces (Telegram to WhatsApp, TUI to Telegram), when handing off tasks between agents (Jon → Eddie, Cipher → Eddie), when an agent needs to know what was discussed in another session, or when you want to resume a conversation on a new surface. Triggers on "catch me up", "what were we working on", "pass this to Eddie", "context switch", "session handoff", "I'm on TUI now", "continue from where we left off".

Session Bridge

Keep context coherent across surfaces (Telegram / WhatsApp / TUI) and agents (Eddie ↔ Jon ↔ Cipher ↔ Sage ↔ Picasso) using lightweight topic capsules — without syncing full transcripts or adding load to the memory system.

Core Concept

Each active work thread gets a capsule — a small JSON file keyed by topic:

tasks/bridges/
  looking-glass.json        ← Meta Ray-Ban project
  session-bridge-design.json ← This skill's design thread
  clawhub-skill-build.json  ← Active publishing task

Capsules hold only what matters: goal, status, decisions, open questions, next action. They are transient working state, not long-term memory. They do not replace MEMORY.md, daily logs, or the ontology graph — they sit on top as a coordination layer.


Script

SCRIPT=~/.openclaw/workspace/skills/session-bridge/scripts/bridge.py
python3 $SCRIPT <command> [options]

Commands

Create a capsule

python3 $SCRIPT create \
  --topic "looking-glass" \
  --goal "Wire Meta Ray-Bans as Eddie's physical presence" \
  --source "agent:main:telegram:direct:7550791652" \
  --agent main

Update a capsule (after decisions, handoffs, progress)

python3 $SCRIPT refresh \
  --topic "looking-glass" \
  --status "active" \
  --next-action "Mike to share Ray-Ban SDK access" \
  --add-decision "Use Bluetooth audio bridge, not USB" \
  --add-question "Does Ray-Ban SDK expose camera feed?" \
  --add-fact "Mike's Ray-Bans model: Meta Ray-Ban v2"

Get a briefing for session start (hydrate)

python3 $SCRIPT hydrate --topic "looking-glass"

Output (~150–350 tokens):

[Session Bridge] Topic: looking-glass
Status: active
Goal: Wire Meta Ray-Bans as Eddie's physical presence
Decisions: Use Bluetooth audio bridge, not USB
Open: Does Ray-Ban SDK expose camera feed?
Next: Mike to share Ray-Ban SDK access
Sources: agent:main:telegram:direct:7550791652
Updated: 2026-03-20T06:45Z

Cross-agent handoff

python3 $SCRIPT handoff \
  --topic "session-bridge-design" \
  --to "agent:main:telegram:direct:7550791652"

Then pass the output to sessions_send so Eddie wakes up informed.

See all active capsules

python3 $SCRIPT list
python3 $SCRIPT status --topic "looking-glass"

Clean up stale capsules

python3 $SCRIPT expire --max-age-hours 48

Bridging Surfaces (Telegram ↔ WhatsApp ↔ TUI)

Option A — Config routing (recommended first step)

Add identityLinks to openclaw.json to collapse the same human across surfaces into one canonical session per agent:

{
  "session": {
    "dmScope": "per-channel-peer",
    "identityLinks": [
      {
        "canonical": "mike",
        "peers": [
          "telegram:7550791652",
          "whatsapp:+15555550123"
        ]
      }
    ]
  }
}

This makes Telegram-Eddie and WhatsApp-Eddie share the same session — no bridging needed. TUI uses a different mechanism (main session key).

Option B — Capsule hydration on surface switch

When the same canonical session is not possible (TUI ↔ Telegram, or different agent entirely):

  1. On the outgoing surface, refresh the relevant capsule:
   python3 $SCRIPT refresh --topic <topic> --next-action "..."
  1. On the incoming surface, hydrate at session start:
   python3 $SCRIPT hydrate --topic <topic>
  1. Inject the briefing as context before responding.

Bridging Agents (Jon → Eddie, Cipher → Eddie)

When finishing a delegation task, the completing agent should:

  1. Refresh the capsule with results:
   python3 $SCRIPT refresh \
     --topic "<task-topic>" \
     --status "done" \
     --add-decision "Research complete: X is the right approach" \
     --next-action "Eddie to implement"
  1. Generate a handoff and deliver via sessions_send:
   python3 $SCRIPT handoff --topic "<task-topic>" --to "agent:main:telegram:direct:7550791652"
   # Copy output → sessions_send(sessionKey="agent:main:...", message=<output>)

Eddie reads the brief and continues — no re-explaining needed.


What Goes Where

Information typeWhere it lives
Current topic, status, next actionCapsule (transient, expires)
Decisions worth keeping long-termMEMORY.md (promote manually)
Structured facts (people, devices, projects)Ontology graph
Narrative context / observationsDaily memory log
Team operating rulesSHARED_CONTEXT.md

Capsules are not a replacement for memory — they are short-lived working state that gets discarded when a topic concludes.


Token Cost

OperationApprox. tokensWhen
Create/refresh capsule~150On handoff or decision
Hydrate (session start briefing)~200–350Once per session
Handoff message~300Cross-agent delegation
Status/list~100On demand

Hydration adds <0.2% of a 200k context window per session. Stale capsules add zero cost (they're just files).


Agent Protocol (Team Rules)

  • On delegation: create or refresh capsule before sessions_send-ing a task
  • On completion: refresh capsule with outcome + next action, then handoff
  • On surface switch: hydrate the relevant capsule at session start
  • On topic close: set --status done, let expire cleanup handle it
  • Never: dump full session transcripts into capsules (defeats the point)

Example: Full Cross-Agent Flow

Mike asks Eddie about the Looking Glass project.
Eddie doesn't remember details from last session.

1. Eddie runs: bridge.py hydrate --topic looking-glass
   → Gets: "Status: active | Next: Mike to share SDK access"
   → Continues conversation without re-explaining from scratch

Mike tells Eddie: "Let Jon research the Ray-Ban SDK."
Eddie creates a task capsule and delegates to Jon via sessions_send.

Jon finishes research.
2. Jon runs: bridge.py refresh --topic looking-glass \
     --add-decision "SDK exposes camera feed via BLE" \
     --next-action "Eddie to prototype BLE bridge"
3. Jon runs: bridge.py handoff --topic looking-glass \
     --to agent:main:telegram:direct:7550791652
4. Jon sends handoff output to Eddie via sessions_send.

Eddie receives handoff.
5. Eddie reads capsule brief, continues work without Jon re-explaining.

Setup

No dependencies beyond Python 3.8+. Works immediately after install:

openclaw skills install session-bridge
python3 ~/.openclaw/workspace/skills/session-bridge/scripts/bridge.py list

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

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

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。

平台分布

OpenClaw

85.69%
按下载量换算1,140

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安装前确认

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