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Alfred Rolling Summarization

作者 lJokerl · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install alfred-rolling-summarization
功能描述
Proactively updates a concise session summary every 15 turns or on tool loops to manage context size and preserve key decisions and progress.
使用说明 (SKILL.md)

Auto-Summarization Skill

Proactive context management to prevent overflow and improve session quality.

Problem

  • OpenClaw compaction triggers on context overflow (reactive)
  • Compaction timeout (60s internal) causes fallback to pre-compaction state
  • Tool loops (research with web_fetch) generate massive context without stopping
  • No control over WHAT is preserved during compaction

Solution: Rolling Summarization

How It Works

Every N turns, create a concise summary of recent work and update SESSION-STATE.md. This keeps the session lean while preserving important context.

Trigger Thresholds

Condition Action
Every 15 turns Create rolling summary
After 10 consecutive tool calls Force summary (tool loop guard)
Context >70% estimated Proactive summary + flush to memory

Rolling Summary Format

Update SESSION-STATE.md with a condensed view:

## Rolling Summary (as of HH:MM)
- Completed: [what was done]
- In progress: [what's being worked on]
- Decisions: [key decisions made]
- Blockers: [anything blocking]
- Next: [what to do next]

Integration with Existing Stack

  1. Hindsight auto-retain captures important facts before summary
  2. SESSION-STATE.md stores the rolling summary (always in context via bootstrap)
  3. Daily notes get the detailed version at end of session
  4. Working buffer becomes unnecessary if rolling summary works well

Anti-Patterns

  • ❌ Don't summarize every turn (adds latency, wastes LLM calls)
  • ❌ Don't duplicate Hindsight content (it already retains facts)
  • ❌ Don't include routine operations (heartbeat checks, status pings)
  • ✅ DO summarize: decisions, blockers, task progress, user preferences
  • ✅ DO keep it under 200 chars per section

Prompt Addition (add to agents that need it)

After completing a task or every 15 turns:
1. Read SESSION-STATE.md
2. Update the Rolling Summary section with current state
3. Keep it concise (under 500 chars total)
4. This prevents context overflow and preserves continuity

Metrics

Metric Before After (target)
Context overflow/week 1-2 ~0
Compaction timeout rate ~50% \x3C20%
Context lost per session High Low
Additional LLM cost $0 ~$0.02/week

Version

1.0.0 — Initial implementation

安全使用建议
This instruction-only skill appears coherent and low-risk, but check where SESSION-STATE.md will be stored and who/what can read it: the rolling summary may contain sensitive task details and will be kept in context. If you install it, confirm the file path and retention policy (where the file is saved, rotation/expiration), ensure you consent to the agent writing to that workspace, and verify the 'Hindsight' integration behavior. No credentials or external installs are required. If you need stronger privacy, limit what is summarized or redact sensitive lines before writing the file.
功能分析
Type: OpenClaw Skill Name: alfred-rolling-summarization Version: 1.0.0 The skill is a documentation-based instruction set designed to help the OpenClaw agent manage its context window through periodic summarization. It instructs the agent to update a local file (SESSION-STATE.md) with progress updates to prevent context overflow. There is no executable code, no network activity, and no instructions to access sensitive system information or bypass security controls.
能力评估
Purpose & Capability
Name/description (periodic rolling summaries to manage context) matches the instructions which explicitly create and update a SESSION-STATE.md rolling summary and integrate with an existing 'Hindsight' retention mechanism. No unrelated binaries, env vars, or cloud credentials are requested.
Instruction Scope
SKILL.md instructs the agent to read and update SESSION-STATE.md every N turns or on tool-loop detection and to keep summaries concise. All referenced actions are directly tied to the stated goal. The instructions do not call external endpoints or request unrelated files or secrets. They do assume the agent can read/write a session-state file and that 'Hindsight auto-retain' is available.
Install Mechanism
No install spec and no code files are present (instruction-only). This is low risk and consistent with an orchestration/prompting skill.
Credentials
The skill declares no environment variables, credentials, or config path requirements, and the SKILL.md does not ask for secrets or external tokens. Requested access (file read/write for SESSION-STATE.md and use of Hindsight) is proportionate to the purpose.
Persistence & Privilege
The skill writes persistent state (SESSION-STATE.md) and expects that file to be kept 'always in context via bootstrap'. It does not request always:true or modify other skills. Persisting summaries across runs is expected for this feature but is a persistence decision the user should be aware of.
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 alfred-rolling-summarization
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /alfred-rolling-summarization 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
Proactive context management: rolling summaries, tool loop guard, SESSION-STATE integration
元数据
Slug alfred-rolling-summarization
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Alfred Rolling Summarization 是什么?

Proactively updates a concise session summary every 15 turns or on tool loops to manage context size and preserve key decisions and progress. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 82 次。

如何安装 Alfred Rolling Summarization?

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

Alfred Rolling Summarization 是免费的吗?

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

Alfred Rolling Summarization 支持哪些平台?

Alfred Rolling Summarization 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 Alfred Rolling Summarization?

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

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