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Watchdog

Organization
Rune-kit
watchdog

Post-deploy monitoring. Use when verifying a deployed app is healthy after deploy or launch — auto-fires from launch/deploy. Checks HTTP status, response times, error detection, and smoke test report.

Overview

PublisherRune-kit
Repositoryrune
Skill namewatchdog
Stars
88
Forks
26
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Rune-kit on GitHub. Read the source before you install it.

Installation

Install the Watchdog AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Rune-kit/rune/tree/master/skills/watchdog
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Rune-kit/rune.git /tmp/rune
mkdir -p .claude/skills
cp -r /tmp/rune/skills/watchdog .claude/skills/watchdog
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Watchdog in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Watchdog on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Watchdog is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

watchdog

Purpose

Post-deploy monitoring. Receives a deployed URL and list of expected endpoints, runs health checks, measures response times, detects errors, and returns a structured smoke test report.

Called By (inbound)

  • deploy (L2): post-deploy monitoring setup
  • launch (L1): monitoring as part of launch pipeline
  • incident (L2): current system state check during incident triage

Calls (outbound)

None — pure L3 utility.

Executable Instructions

Step 1: Receive Target

Accept input from calling skill:

  • base_url — deployed application URL (e.g. https://myapp.com)
  • endpoints — list of paths to check (e.g. ["/", "/health", "/api/status"])

If no endpoints provided, default to: ["/", "/health", "/ready"]

Step 2: Health Check

For each endpoint, run an HTTP status check using Bash:

bash
curl -s -o /dev/null -w "%{http_code}" https://myapp.com/health
  • 2xx → HEALTHY
  • 3xx → REDIRECT (note final destination)
  • 4xx → CLIENT_ERROR (flag as alert)
  • 5xx → SERVER_ERROR (flag as critical alert)
  • Connection refused / timeout → UNREACHABLE (flag as critical)

Step 3: Response Time

For each endpoint, measure latency using Bash:

bash
curl -s -o /dev/null -w "%{time_total}" https://myapp.com/health

Thresholds:

  • < 500ms → FAST
  • 500ms–2000ms → ACCEPTABLE
  • 2000ms → SLOW (flag as alert)

Step 4: Performance Signal Analysis

After collecting response times from Step 3, analyze for patterns that indicate root causes:

  • Consistently 2x+ slower than baseline (or > 2000ms with no apparent load): flag with PERF_WARN — investigate N+1 query or missing DB index
  • Endpoint cluster degradation: if 3+ endpoints share a pattern (all auth endpoints slow, all /api/* slow): flag PERF_WARN — connection pool saturation likely
  • Spike after deploy: compare with previous watchdog run if available — if an endpoint that was FAST is now SLOW, flag PERF_REGRESSION — correlate with recent git diff

If no previous baseline exists, skip spike detection and note INFO: no baseline — first run.

Output performance signals into a perf_signals list (separate from alerts).

Step 5: Error Detection

Scan responses for problems:

  • 4xx/5xx HTTP codes → log endpoint + status code
  • Response time > 2s → log endpoint + measured time
  • Connection timeout (curl exits non-zero) → UNREACHABLE
  • Empty response body on non-204 endpoints → flag as WARNING

Collect all flagged issues into an alerts list.

Step 6: Report

Output the following report structure:

## Watchdog Report: [base_url]

### Smoke Test Results
- [endpoint] — [HTTP status] ([response_time]s) — [HEALTHY|REDIRECT|CLIENT_ERROR|SERVER_ERROR|UNREACHABLE]

### Alert Rules Applied
- Response time > 2s → alert
- Any 4xx on non-auth endpoint → alert
- Any 5xx → critical alert
- Unreachable → critical alert

### Alerts
- [CRITICAL|WARNING] [endpoint] — [reason]

### Performance Signals
- [PERF_WARN|PERF_REGRESSION|INFO] [endpoint] — [diagnosis]

### Summary
- Total endpoints checked: [n]
- Healthy: [n]
- Alerts: [n]
- Perf Signals: [n]
- Overall status: ALL_HEALTHY | DEGRADED | DOWN

If no alerts and no perf signals: output Overall status: ALL_HEALTHY.

Output Format

## Watchdog Report: [base_url]
### Smoke Test Results
- / — 200 (0.231s) — HEALTHY
- /health — 200 (0.089s) — HEALTHY
- /api/status — 500 (1.203s) — SERVER_ERROR

### Alerts
- CRITICAL /api/status — HTTP 500

### Summary
- Total: 3 | Healthy: 2 | Alerts: 1
- Overall status: DEGRADED

Constraints

  1. MUST report with specific metrics — not vague "performance seems slow"
  2. MUST include baseline comparison when available
  3. MUST NOT generate false alarms — precision over recall
  4. MUST separate perf signals from error alerts — they are different severity channels
  5. MUST NOT call perf skill — watchdog is a detector, not a diagnoser

Sharp Edges

Known failure modes for this skill. Check these before declaring done.

Failure ModeSeverityMitigation
curl timeout treated as slow (not unreachable)HIGHNon-zero curl exit code = UNREACHABLE, not a response time measurement
PERF_REGRESSION reported without baselineMEDIUMOnly flag regression if a previous run exists — otherwise INFO: first run
All endpoints flagged SLOW because test env is slowMEDIUMNote environment context — add ENV: non-production detected if URL contains dev/staging/localhost
Perf signal without actionable diagnosisLOWEvery PERF_WARN must include a hypothesis (N+1, pool saturation, etc.)

Done When

  • All specified endpoints checked (HTTP status + response time measured)
  • All 4xx/5xx → alerts list, all SLOW → alerts list
  • Performance patterns analyzed → perf_signals list (or INFO: first run)
  • Structured Watchdog Report emitted with Alerts + Performance Signals + Summary
  • Overall status is ALL_HEALTHY, DEGRADED, or DOWN (never ambiguous)

Cost Profile

~500-1500 tokens input, ~300-800 tokens output. Sonnet for configuration quality.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Watchdog AI skill do?

Post-deploy monitoring. Use when verifying a deployed app is healthy after deploy or launch — auto-fires from launch/deploy. Checks HTTP status, response times, error detection, and smoke test report.

Why use Watchdog on TypingMind?

Because you install it once and use it with any model. Watchdog is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Watchdog in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Rune-kit/rune/tree/master/skills/watchdog. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Watchdog?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Watchdog?

As many as you like. As long as a model supports skills, you can use Watchdog with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Watchdog AI skill free?

Yes. It is published on GitHub by Rune-kit under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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