Skill

Monitor the Claude API for outages and latency spikes with rich Telegram alerts. Status monitoring, latency probes, and automatic recovery notifications.

Claude Watchdog 🐕

Monitor the Anthropic/Claude API for outages and latency spikes. Sends rich alerts to Telegram — no agent tokens consumed for status checks.

What It Does

Status Monitor (status-check.py)

• Polls status.claude.com every 15 minutes via cron
• Alerts with incident name, latest update text, per-component status
• Tags incidents as "(not our model)" if e.g. Haiku is affected but you use Sonnet
• Sends all-clear on recovery
• Zero token cost

Latency Probe (latency-probe.py)

• Sends a minimal request through OpenClaw's local gateway every 15 minutes
• Measures real end-to-end latency to Anthropic API
• Maintains rolling baseline (median of last 20 samples)
• Alerts with 🟡/🟠/🔴 severity based on spike magnitude
• Sends all-clear when latency recovers
• ~$0.000001 per probe

Setup

Run the interactive setup script:

bash /path/to/skills/claude-watchdog/scripts/setup.sh

You'll need:

1. Telegram Bot Token — from @BotFather
2. Telegram Chat ID — send a message to your bot, then check https://api.telegram.org/bot<TOKEN>/getUpdates
3. OpenClaw Gateway Token — run:

   python3 -c "from pathlib import Path; import json; print(json.load(open(Path.home() / '.openclaw/openclaw.json'))['gateway']['auth']['token'])"
   

4. Gateway Port — default 18789

The setup script writes config, installs cron jobs, and runs an initial check.

To uninstall (removes cron jobs, optionally config/state):

bash /path/to/skills/claude-watchdog/scripts/setup.sh --uninstall

Config

Stored in ~/.openclaw/skills/claude-watchdog/claude-watchdog.env. To reconfigure, either re-run setup.sh or edit this file directly — changes take effect on the next cron run (within 15 minutes).

TELEGRAM_BOT_TOKEN=...
TELEGRAM_CHAT_ID=...
OPENCLAW_GATEWAY_TOKEN=...
OPENCLAW_GATEWAY_PORT=18789
MONITOR_MODEL=sonnet
PROBE_MODEL=openclaw
PROBE_AGENT_ID=main

Variable	Default	Description
TELEGRAM_BOT_TOKEN	(required)	Telegram bot token from @BotFather
TELEGRAM_CHAT_ID	(required)	Target chat for alerts
OPENCLAW_GATEWAY_TOKEN	(required)	Auth token for the local OpenClaw gateway
OPENCLAW_GATEWAY_PORT	18789	Port the OpenClaw gateway listens on
MONITOR_MODEL	sonnet	Model name to match in status incidents (e.g. "sonnet", "haiku")
PROBE_MODEL	openclaw	Model alias sent to the gateway for latency probes. openclaw uses the gateway's default model routing
PROBE_AGENT_ID	main	Value of the x-openclaw-agent-id header sent with probes
FILTER_KEYWORDS	(none)	Comma-separated keywords to filter out of status alerts (e.g. "skills,Artifacts,Memory"). Empty = receive all alerts

Scripts also accept these as environment variables (env file takes priority).

Security Note

The env file contains sensitive tokens (Telegram bot token, gateway token). The setup script sets permissions to 600 (owner-only read/write). If you create or edit the file manually, ensure restricted permissions:

chmod 600 ~/.openclaw/skills/claude-watchdog/claude-watchdog.env

Alert Examples

Status incident:

🟠 Anthropic Status: Partially Degraded Service

📌 Elevated error rates on Claude 3.5 Haiku (not our model)
Status: Investigating
Update: "We are investigating increased error rates..."

Components:
  🟠 API: partial outage

🔗 https://status.claude.com

Latency spike:

🟡 Anthropic API — High Latency Detected

Current: 12.3s
Baseline: 3.1s (median of last 19 samples)
Ratio: 4.0×

Slow responses are expected right now.

Recovery:

✅ Anthropic API — Latency Back to Normal

Current: 2.8s
Baseline: 3.1s
Was: 12.3s when alert fired

State & Logs

All state and log files are stored in ~/.openclaw/skills/claude-watchdog/:

File	Purpose
claude-watchdog-status.json	Status check state
claude-watchdog-latency.json	Latency probe state & samples
claude-watchdog-status.log	Status check log
claude-watchdog-latency.log	Latency probe log

Tuning Thresholds

Edit constants at the top of latency-probe.py:

Constant	Default	Meaning
ALERT_MULTIPLIER	2.5	Alert if latency > N× baseline median
ALERT_HARD_FLOOR	10.0s	Always alert above this absolute threshold
RECOVER_MULTIPLIER	1.5	Clear alert when below N× baseline
BASELINE_WINDOW	20	Rolling sample window size
BASELINE_MIN_SAMPLES	5	Minimum samples before alerting starts
PROBE_TIMEOUT	45s	Give up on probe after this long

Requirements

• Python 3.10+ (stdlib only, no pip dependencies)
• OpenClaw gateway running locally
• Telegram bot with access to the target chat