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Context Relay

作者 OpenLark · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install context-relay
功能描述
Solves the memory fragmentation problem for Agents during session restarts, sub-agent boundaries, and cron/heartbeat isolation.
使用说明 (SKILL.md)

Context Relay - Cross-Session Memory Continuity System

Files are the single source of truth. Each execution unit reads context from files at startup, without relying on session memory. Suitable for Agents that need to maintain task continuity across sessions.

Trigger Words

  • Memory fragmentation
  • Cross-session
  • Context passing
  • context relay
  • session restart
  • sub-agent communication
  • persistent context
  • project state management
  • cold start

Core Principle

Files are the single source of truth.

Agents lose session memory in the following scenarios:

  • Session restart (app restart, timeout disconnection)
  • Sub-agent boundaries (newly spawned agents lack parent session memory)
  • Cron/Heartbeat isolation (scheduled tasks and heartbeats are independent sessions)

Solution: Each execution unit reads context from files at startup and writes back to files when finished. Files are memory.

Three-Layer Context Architecture

project/
├── PROJECT.md        # Project metadata (long-term stable)
├── state.json        # Current state (frequently updated)
├── decisions.md      # Architecture decision records (append-only)
└── todos.json        # Self-managed todo list (cross-session tracking)

Layer 1: PROJECT.md (Project Identity)

Purpose: Defines "who I am," long-term stable, rarely modified.

Content Template:

# Project Name

## One-Line Description
[What this project is, what problem it solves]

## Tech Stack
- Frontend:
- Backend:
- Database:
- Deployment:

## Directory Structure
/src        - Source code
/docs       - Documentation
/tests      - Tests

## Key Constraints
[Rules that must be followed, such as API compatibility, performance requirements]

## Related Documents
- Architecture decisions: decisions.md
- Current state: state.json
- Todo items: todos.json

Layer 2: state.json (Current State)

Purpose: Defines "where I am," frequently updated, records current progress.

Content Template:

{
  "phase": "development",
  "current_task": "Implement user authentication module",
  "progress": {
    "completed": ["Database design", "API design"],
    "in_progress": "Login endpoint development",
    "blocked": [],
    "next_steps": ["Registration endpoint", "Password reset"]
  },
  "last_update": "2026-04-20T10:00:00+08:00",
  "session_id": "abc123",
  "notes": "Encountering CORS issues, need to configure proxy"
}

Layer 3: decisions.md (Decision Records)

Purpose: Defines "why," append-only, never deleted.

Content Template:

# Architecture Decision Records

## ADR-001: Use JWT for Authentication
- Date: 2026-04-15
- Status: Accepted
- Context: Need a stateless authentication scheme
- Decision: Use JWT + refresh tokens
- Consequences: Must handle token expiration and revocation

## ADR-002: Choose PostgreSQL as Primary Database
- Date: 2026-04-16
- Status: Accepted
- Context: Need support for complex queries and transactions
- Decision: Use PostgreSQL
- Consequences: Must learn PostgreSQL-specific syntax

todos.json (Self-Managed Todo System)

Purpose: Agent-managed todo list for cross-session tracking.

Content Template:

{
  "todos": [
    {
      "id": "TODO-001",
      "title": "Complete user authentication module",
      "priority": "high",
      "status": "in_progress",
      "created": "2026-04-20T09:00:00+08:00",
      "updated": "2026-04-20T10:00:00+08:00",
      "notes": "Implementing login endpoint"
    },
    {
      "id": "TODO-002",
      "title": "Write unit tests",
      "priority": "medium",
      "status": "pending",
      "created": "2026-04-20T09:00:00+08:00",
      "updated": null,
      "notes": ""
    }
  ],
  "last_review": "2026-04-20T10:00:00+08:00"
}

Operation Rules:

  • Start task: Change status to in_progress
  • Complete task: Change status to completed, record completed_at
  • Blocked task: Change status to blocked, explain reason in notes
  • Each session start: Check last_review; if over 24 hours, review all todos

Workflow

On Startup: Cold Start Procedure

Execute at the start of each new session/sub-agent/cron job:

1. Read PROJECT.md → Understand project identity
2. Read state.json → Understand current progress
3. Read todos.json → Understand pending tasks
4. If decision context is needed → Read decisions.md
5. Begin work

Critical: Do not assume any memory. All context must be read from files.

On Completion: State Synchronization

Execute before ending each work session:

1. Update state.json → Record current progress
2. If new decisions were made → Append to decisions.md
3. If todos changed → Update todos.json
4. Commit file changes

Critical: Files must be written before the session ends to ensure the next execution unit can read the latest state.

Sub-Agent Communication

Parent and child agents communicate context via files:

Parent agent:
1. Update state.json (current task, expected output)
2. Spawn child agent

Child agent:
1. Read state.json → Understand task
2. Execute task
3. Update state.json (results, progress)
4. End

Parent agent:
1. Read state.json → Retrieve results
2. Continue work

Note: Child agents have no memory of the parent session; they can only communicate via files.

Reference Resources

Script Tools

  • scripts/init_context.py - Initialize the context file structure in a project directory
安全使用建议
This skill is coherent and implements a file-backed context system using local files; the bundled Python script simply creates templates and does not perform network activity or access secrets. Before using it: (1) inspect the included init_context.py yourself (already provided) to confirm behavior, (2) run it in the intended project directory or a disposable/test directory to avoid accidental overwrites, and (3) back up any existing PROJECT.md/state.json/todos.json/decisions.md if you care about current contents (use the script without --force first so it won't overwrite existing files). If you need the agent to operate across directories or with sensitive repos, consider limiting its filesystem scope or running in an isolated workspace.
功能分析
Type: OpenClaw Skill Name: context-relay Version: 1.0.0 The 'context-relay' skill bundle is a project management framework designed to help AI agents maintain state across sessions using local files (PROJECT.md, state.json, etc.). The included Python script (scripts/init_context.py) safely initializes these templates without any network calls, obfuscation, or suspicious execution logic. The instructions in SKILL.md and the reference guides are focused on task continuity and do not contain malicious prompt injections or attempts to exfiltrate sensitive data.
能力评估
Purpose & Capability
The name/description promise (solve memory fragmentation via cross-session file-backed context) is implemented directly by the SKILL.md workflow and the init_context.py script. Required capabilities (reading/writing project files) align with the stated purpose; nothing unrelated (cloud credentials, network endpoints, extra binaries) is requested.
Instruction Scope
Instructions explicitly tell agents to read and write PROJECT.md, state.json, decisions.md, and todos.json in the project directory. This is within scope, but it grants the agent filesystem read/write access to the working directory — which is expected for this feature but worth noting because it can modify or overwrite local files (the script warns about existing files and offers --force to overwrite).
Install Mechanism
No install spec; this is an instruction-only skill with a small bundled Python script. Nothing is downloaded from external URLs and no archives are extracted, so installation risk is minimal.
Credentials
The skill requires no environment variables, credentials, or external config paths. The script uses only standard library modules and local filesystem operations — the requested scope is proportionate to the stated functionality.
Persistence & Privilege
always is false and the skill is user-invocable (normal defaults). The skill does not request persistent platform-wide privileges or attempt to modify other skills/config. Its only persistence is writing context files to the project directory, which is expected behaviour.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install context-relay
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /context-relay 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of context-relay, solving agent memory fragmentation during session restarts, sub-agent boundaries, and task isolation. - Introduces a three-layer context architecture: PROJECT.md (identity), state.json (current state), decisions.md (decision records), and todos.json (cross-session task management). - Files serve as the single source of truth; agents read all context from files at startup, ensuring persistent continuity across sessions. - Provides clear workflows for cold start initialization and end-of-session state synchronization. - Adds sub-agent communication protocol via state files and includes script to initialize file structure. - Comprehensive documentation and file templates included for immediate adoption.
元数据
Slug context-relay
版本 1.0.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 1
常见问题

Context Relay 是什么?

Solves the memory fragmentation problem for Agents during session restarts, sub-agent boundaries, and cron/heartbeat isolation. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 108 次。

如何安装 Context Relay?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install context-relay」即可一键安装,无需额外配置。

Context Relay 是免费的吗?

是的,Context Relay 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Context Relay 支持哪些平台?

Context Relay 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Context Relay?

由 OpenLark(@openlark)开发并维护,当前版本 v1.0.0。

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