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Install in OpenClaw
/install context-compress
Description
Incrementally summarizes long conversations by pruning, preserving key segments, and using AI to maintain context coherence.
README (SKILL.md)
Context Compress Skill
🛡️ OpenClaw 混合进化方案 — 将 Hermes-agent（100K ⭐）+ Claude Code 核心能力移植到 OpenClaw
防止长对话中思维链断裂的增量摘要工具。
🚀 一键安装
mkdir -p ~/.openclaw/skills && cd ~/.openclaw/skills && curl -fsSL https://github.com/olveww-dot/openclaw-hermes-claude/archive/main.tar.gz | tar xz && cp -r openclaw-hermes-claude-main/skills/context-compress . && rm -rf openclaw-hermes-claude-main && echo "✅ context-compress 安装成功"
触发方式
- 手动触发: 对我说 "压缩上下文" 或 "compact"
- 自动触发: 当上下文超过模型 context window 的 50% 时自动压缩
五步算法
- Prune — 裁剪旧工具输出（无 LLM 调用，廉价预检）
- Head — 保护开头的系统提示和前几轮对话
- Tail — 按 token 预算保护最近几轮（~20K tokens）
- LLM Summarize — 中间部分调用 DeepSeek-V3 压缩
- Iterative — 后续压缩迭代更新摘要
摘要格式
保留以下结构化字段：
- Active Task — 当前任务（最重要）
- Goal — 总体目标
- Completed Actions — 已完成操作（含工具、目标、结果）
- Active State — 当前工作状态
- Blocked — 阻塞问题
- Key Decisions — 关键决策
- Pending User Asks — 未完成请求
- Remaining Work — 剩余工作
使用 SiliconFlow API
- 模型:
deepseek-ai/DeepSeek-V3 - API Base:
https://api.siliconflow.cn/v1 - 通过中转商调用，API Key 存储在环境变量
🧩 配套技能
本 skill 是 OpenClaw 混合进化方案 的一部分：
将 Hermesagent（100K ⭐）+ Claude Code 核心能力移植到 OpenClaw
将 Hermes-agent（100K ⭐）+ Claude Code 核心能力移植到 OpenClaw
🔗 GitHub 项目：olveww-dot/openclaw-hermes-claude
完整技能套件（6个）：
- 🛡️ crash-snapshots — 崩溃防护
- 🧠 auto-distill — T1 自动记忆蒸馏
- 🎯 coordinator — 指挥官模式
- 💡 context-compress — 思维链连续性（本文）
- 🔍 lsp-client — LSP 代码智能
- 🔄 auto-reflection — 自动反思
输出文件
src/compressor.ts— 核心压缩逻辑（TypeScript）
Usage Guidance
This skill will read your local OpenClaw conversation/session data and (if enabled) send the middle portion of conversations to a third‑party LLM service (SiliconFlow / DeepSeek‑V3) for summarization. Before installing: (1) assume SILICONFLOW_API_KEY is required even though the registry metadata omitted it — do not provide any key you don't trust the recipient with; (2) inspect compressor.ts and the import readSecret to confirm where API keys are read from (env vs secret manager) and that no other unexpected endpoints are contacted; (3) be aware summaries may include secrets found in chat history (the README shows such examples) — redact or remove sensitive messages before compression or disable the remote LLM step; (4) prefer installing only after verifying the upstream GitHub repo and repository owner, and avoid the one‑liner curl|tar unless you trust that repo; (5) if you cannot verify the code path that sends data to SiliconFlow, do not install on systems containing confidential data. Providing the full, untruncated compressor.ts and any network-calling code paths would increase confidence and could change this assessment to benign if they prove limited and transparent.
Capability Analysis
Type: OpenClaw Skill
Name: context-compress
Version: 1.0.2
The context-compress skill is a utility designed to manage long conversation histories by summarizing intermediate turns using the SiliconFlow API (DeepSeek-V3). The core logic in `src/compressor.ts` implements a multi-step process to prune verbose tool outputs, protect the system prompt and recent messages, and generate a structured summary to maintain task continuity. The prompt instructions in `SKILL.md` and the compressor logic are well-structured and align with the stated purpose of preventing context window overflow. No evidence of malicious intent, unauthorized data exfiltration, or harmful prompt injection was found; the code includes defensive measures to ensure the agent treats summarized content as reference rather than active instructions.
Capability Tags
Capability Assessment
Purpose & Capability
The skill claims incremental summarization using a third‑party LLM (DeepSeek‑V3 / SiliconFlow). The package and runtime invoke a remote summarizer, which makes sense for the stated purpose, but the registry metadata declares no required environment variables or primary credential while the README and scripts expect SILICONFLOW_API_KEY and the code uses a readSecret helper. This mismatch (no declared secret despite needing an API key) is an incoherence that reduces transparency.
Instruction Scope
Runtime instructions and scripts will read local conversation/session data and send content to an external API for summarization. The README shows examples where secrets found in context (e.g., SECRET_KEY) might be included in summaries — meaning potentially sensitive conversation contents could be transmitted to SiliconFlow. The SKILL.md also suggests copying the skill into a GitHub sync directory, which risks persisting sensitive summaries to a remote git repo. These behaviors are within the claimed purpose but increase privacy/exfiltration risk and are not called out in registry metadata.
Install Mechanism
There is no formal install spec; an install.sh is provided that copies files into ~/.openclaw/skills (local, low-risk). SKILL.md suggests a one‑liner that downloads a tar.gz from a GitHub repo (a common pattern). The remote download is from GitHub (well-known host) but the repository is third‑party; downloading and executing anything from an untrusted repo has inherent risk. Overall installation is not highly suspicious but lacks provenance/verification.
Credentials
The skill effectively requires at least one secret (SILICONFLOW_API_KEY) to enable LLM summarization, and the code imports a readSecret helper (which could access environment or secret stores). However the registry metadata lists no required env vars or primary credential. That omission is a transparency problem. Also the skill will send conversation contents (which may include credentials, tokens, config snippets) to a third party — this is proportionate to using an external LLM but should be explicitly declared and limited.
Persistence & Privilege
The skill does not request 'always: true' and its install.sh only writes its own files into ~/.openclaw/skills. It does not appear to change other skills' configs or system-wide settings. Autonomous invocation is allowed (default) which increases blast radius if the skill exfiltrates data, but that is platform default rather than a unique misconfiguration of this skill.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install context-compress - After installation, invoke the skill by name or use
/context-compress - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
v1.0.2: 添加一键安装命令，ClaWHub安装后可直接使用
v1.0.1
v1.0.1: 详情页加入Hermes-agent+Claude Code融合卖点
v1.0.0
Initial release: 思维链连续性，防止长对话中断层
Metadata
Frequently Asked Questions
What is Context Compress?
Incrementally summarizes long conversations by pruning, preserving key segments, and using AI to maintain context coherence. It is an AI Agent Skill for Claude Code / OpenClaw, with 161 downloads so far.
How do I install Context Compress?
Run "/install context-compress" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Context Compress free?
Yes, Context Compress is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Context Compress support?
Context Compress is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Context Compress?
It is built and maintained by ECsss (@olveww-dot); the current version is v1.0.2.
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