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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.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install assistantheinrich-prog/session-health-monitorInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
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%...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# 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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