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Watchdog

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mvschwarz
watchdog

Use when configuring `rig watchdog` policies, authoring wake/refocus/alignment-checkpoint messages, or choosing the right intervention level for a stale-owner situation. The 3-level continuity-check stack (wake / refocus / alignment-checkpoint), the discipline that prevents cadence pollution and bureaucracy theater, and the artifact-pool loop-edge pattern for evidence-aware waking.

Overview

Publishermvschwarz
Repositoryopenrig
Skill namewatchdog
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mvschwarz 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/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/specs/agents/shared/skills/core/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

Continuity checks are timed or evidence-triggered interventions that keep an agent workflow from going idle, drifting, or entering needless bureaucracy. They operationalize the refocus doctrine.

Use this when

  • Authoring a watchdog policy for a long-running rig (artifact-pool edges, stale-owner detection, periodic reminders).
  • Choosing the intervention level for a stale-owner situation: is this a wake, a refocus, or an alignment checkpoint?
  • Drafting wake/refocus/alignment-checkpoint message text that prevents cadence pollution and bureaucracy theater.
  • Deciding cadence — scan vs wake intervals, conservative vs aggressive nudging.

Don't use this when

  • The agent is actively working and closing artifacts. Prefer no-op.
  • The intervention is masking bad startup or workflow design — fix the underlying cause, don't compensate via watchdog.
  • The work has a clear next-action handoff already in queue. Use queue nudges instead of a watchdog policy.

The 3-level intervention stack

LevelGoalWhenWhat it does
WakeRestart motionOwner appears idle, stale, blocked, or missing a next handoffSmall liveness nudge — does NOT reframe the work
RefocusCorrect driftOutput shows mode drift, approval regression, weak stop-condition reasoningMedium alignment nudge — re-centers on role, north star, current approved workstream, coordination mode, stop conditions; does NOT interrupt valid work
Alignment checkpointRebuild shared mapPhase boundary, lifecycle mutation, product-intent decision, confusing contradictionLarger deliberate pause — agent runs full refocus before proceeding

cron, timers, and rig watchdog are scheduling substrates. They should NOT imply that every tick means the same semantic action. The intervention level is chosen by evidence, not cadence.

Current best practice for refocus message text

A well-shaped refocus message:

  • Starts with: finish current action first.
  • Names the intervention kind: wake, refocus, or alignment checkpoint.
  • Names the current approved workstream or says none is known.
  • Says what continuity means: continue, verify, hand off, or explicitly park.
  • Names stop conditions: scope/risk/posture changes, failed gate, contradictory evidence, or no continuity chain naming a sensible next step.
  • Does NOT convert itself into a fresh approval gate.
  • Does NOT wake delivery seats unless an approved workflow exists and the owner is stale.

Failure modes (7; verbatim)

  1. Cadence is too frequent and pollutes the workstream.
  2. Watchdog wakes the wrong seat instead of the stale owner.
  3. Refocus text is too rule-shaped and creates brittle behavior.
  4. Watchdog compensates for bad startup or workflow design instead of revealing it.
  5. Refocus is misread as a new top-priority task and interrupts the current action.
  6. Refocus becomes bureaucracy theater: an already-approved workflow stops for re-approval.
  7. Repeated static nudges teach agents to answer the reminder instead of progressing the artifact.

Artifact-pool loop edges (the first evidence-aware pattern)

The first evidence-aware watchdog use that should remain small and explicit:

  • Consumer-pool wake: if a ready artifact exists, wake the consumer loop head.
  • Producer-edge repair: if upstream completion exists but the downstream artifact is missing, wake the producer loop head.

Scan cadence and wake cadence are separate. Worked example (the numbers are illustrative; the scan-vs-wake separation is the durable rule): scan every 30 seconds, wake at most every 600 seconds while work remains actionable.

This is not a generic workflow engine. It's a guardrail against cold pools and missing edge artifacts.

Active surface

Daemon rig watchdog is the active host coordination surface for new work. History records only loud evaluations (sent / terminal); quiet skip reasons are NOT recorded — POC parity so agents are not woken about scheduler polls.

Future shape (current limit)

The mature primitive should be evidence-aware: inspect durable queue/workflow state, pane activity, transcript/context growth, and known workstream frontier; classify the intervention as no-op / wake / refocus / alignment-checkpoint; prefer no-op for active owners; render message text from the active orientation graph instead of hard-coding static policy.

Until queue substrate, activity state, and lifecycle evidence are fully productized at this level of granularity: keep config-layer use small and explicit, prefer one workflow watchdog plus targeted exception handling over many per-seat nag loops.

See also

  • refocus skill — the doctrine this primitive operationalizes; full and light trace templates
  • queue-handoff skill — durable handoff via queue items; watchdog is complementary (watchdog wakes; queue routes)
  • looping-workflows skill — operating discipline for self-driving rig-shaped loops; uses watchdog policies for loop edges

Frequently asked questions

What does the Watchdog AI skill do?

Use when configuring `rig watchdog` policies, authoring wake/refocus/alignment-checkpoint messages, or choosing the right intervention level for a stale-owner situation. The 3-level continuity-check stack (wake / refocus / alignment-checkpoint), the discipline that prevents cadence pollution and bureaucracy theater, and the artifact-pool loop-edge pattern for evidence-aware waking.

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/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/watchdog. TypingMind reads its SKILL.md 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 mvschwarz under the Apache-2.0 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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