xint
X Intelligence CLI — search, monitor, analyze, and engage on X/Twitter. TypeScript + Bun. AI agent skill.
Context window health monitoring for OpenClaw agents — threshold warnings via Telegram, pre-compaction snapshots, and memory rotation.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install assistantheinrich-prog/session-health-monitor打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Monitor your OpenClaw agent context window health, get warnings via Telegram when usage is high, save critical facts before compaction, and keep memory directories clean.
Four capabilities for OpenClaw agent sessions:
Add to your shared/INDEX.md:
| Context window health, compaction detection, pre-compaction snapshots | `skill-session-health.md` |
Create shared/skill-session-health.md:
# Session Health Monitor
## Context Health Thresholds
| Level | Condition | Action |
|--------|------------------------------------|-------------------------------|
| GREEN | <50% used AND 0 compactions | Normal operation |
| YELLOW | >=50% used OR >=1 compaction | Save key facts via snapshot |
| RED | >=75% used OR >=2 compactions | Save facts NOW, session ending|
## Behavioral Rules
1. When context reaches YELLOW+, extract 3-5 key facts (decisions, files changed, blockers)
2. Run: `bash scripts/snapshot.sh "fact1" "fact2"`
3. Append footer to Telegram messages at YELLOW+: `X% Context Window | Nx compacted`
4. Do this BEFORE session ends or context gets compacted
5. After any detected compaction, immediately snapshot what you remember
Add to your agent heartbeat/loop:
**Context health check**: Run `session_status` → always append context % to Telegram messages
as footer: `📊 X% Context Window`. If Context >50%...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# Session Health Monitor Monitor your OpenClaw agent context window health, get warnings via Telegram when usage is high, save critical facts before compaction, and keep memory directories clean. ## Overview Four capabilities for OpenClaw agent sessions: 1. **Context Threshold Warnings** — Agents append usage footer to Telegram messages and warn at configurable thresholds 2. **Compaction Detection** — Track usage drops to infer when context was compacted 3. **Pre-Compaction Snapshots** — Save key facts and decisions to daily memory files before they're lost 4. **Memory Rotation** — Archive old daily memory files to prevent clutter ## Quick Setup (OpenClaw) ### 1. Add shared skill reference Add to your `shared/INDEX.md`: ```markdown | Context window health, compaction detection, pre-compaction snapshots | `skill-session-health.md` | ``` ### 2. Create shared skill doc Create `shared/skill-session-health.md`: ```markdown # Session Health Monitor ## Context Health Thresholds | Level | Condition | Action | |--------|------------------------------------|-------------------------------| | GREEN | <50% used AND 0 compactions | Normal operation | | YELLOW | >=50% used OR >=1 compaction | Save key facts via snapshot | | RED | >=75% used OR >=2 compactions | Save facts NOW, session ending| ## Behavioral Rules 1. When context reaches YELLOW+, extract 3-5 key facts (decisions, files changed, blockers) 2. Run: `bash scripts/snapshot.sh "fact1" "fact2"` 3. Append footer to Telegram messages at YELLOW+: `X% Context Window | Nx compacted` 4. Do this BEFORE session ends or context gets compacted 5. After any detected compaction, immediately snapshot what you remember ``` ### 3. Add heartbeat step Add to your agent heartbeat/loop: ```markdown **Context health check**: Run `session_status` → always append context % to Telegram messages as footer: `📊 X% Context Window`. If Context >50%...
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