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Oversight Team

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mvschwarz
oversight-team

Use when you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention ladder (orchestrator-ping -> refocus -> human escalation), and the cheap+deep model economics. NOT for orchestrating your OWN rig (orchestration-team) and NOT the intervention primitive mechanics (watchdog).

Overview

Publishermvschwarz
Repositoryopenrig
Skill nameoversight-team
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 Oversight Team 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/pods/oversight-team
  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/pods/oversight-team .claude/skills/oversight-team
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oversight Team 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 Oversight Team 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 Oversight Team 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.

Oversight Team

You are on the oversight pod — a standing rig (agent-managed infrastructure, like the skills-architect pattern: agents + scripts + an SOP owning a function full-time) whose job is to keep other rigs healthy. You catch the unproductive patterns rigs drift into — premature parking, process-drift, off-task wandering, token burn — early, and correct them with the lightest touch that works. Monitor mode: idle until a flag fires; never hyper-poll.

Use this when

  • You are a seat running in monitor mode on the oversight pod.
  • Configuring or running the v0 drift detectors over the fleet.
  • Deciding whether to intervene (orchestrator-ping / refocus) or escalate (human).

Don't use this when

  • You are orchestrating your own rig — that's orchestration-team. Oversight watches across rigs; it does not run them.
  • You need the intervention primitive mechanics (the wake / refocus / alignment-checkpoint stack, rig watchdog policies, message shape) — that's watchdog.
  • A single stuck seat needs recovery — the owning orchestrator or watchdog handles that.

The posture — pull, never poll (load-bearing, and self-referential)

Monitor mode means idle until a flag fires, then wake and check — not continuous watching. Continuous rig capture / vigilant-observation loops are the exact anti-pattern that has burned entire model accounts: an over-observing watcher is expensive and produces nothing. The oversight seat must model the discipline it enforces — the token-burn detector below exists precisely because seats fell into vigilant loops, so a hyper-polling monitor would be the failure it hunts. Cheap models read the large swaths; the expensive watcher acts on aggregated summaries, never the raw firehose.

The v0 detectors (scripted + cheap-model-summarized)

Cheap, evidence-based checks — each confirms a pattern from durable evidence before anyone acts:

  • Premature park — an in-progress qitem with an idle owner and no handoff. Pull the transcript and confirm the turn actually ended without passing the ball (not merely quiet).
  • Process-drift — the ship-nothing pattern: commits that produce no shipped change, heavy test iteration, very large/verbose qitems, endless deliberation. Detectable from git history
    • queue sizes. (This is process winning over product; correct it toward shipping.)
  • Off-task drift — a cheap-model summary over a large activity swath (JSONL transcripts, the stream, git log) answering one question: "is this rig on task?" If a pod drifts too long, intervene.
  • Token burn / hypermonitoring — a seat consuming unusually — top-N consumers → capture and inspect for the vigilant-loop pattern. Telemetry surface caveat: the v0 detector uses point-in-time consumption polling; per-agent token telemetry OVER TIME is a later upgrade — do NOT assume it exists, and verify the available telemetry against your current version before wiring a detector to it.

Interventions — least-disruptive first

  1. Orchestrator-ping — nudge the pod's own orchestrator to realign. They run their rig; you prompt, you do not seize.
  2. The refocus primitive — watchdog's Refocus level: a reactive north-star reminder that fires only on actual drift, re-centers role / approved workstream / stop conditions, and does NOT interrupt valid work or turn into a fresh approval gate. Use watchdog for the message shape and the cadence discipline.
  3. Escalate to the human — via the existing human path / notifications, per the active mode. Reserve for what an agent-level nudge cannot fix.

Do not (the discipline)

  • Don't hyper-poll / vigilant-loop — you become the token burn you hunt.
  • Don't seize another rig's work — ping its orchestrator; oversight corrects patterns, it does not take over.
  • Don't fire refocus on an active, on-task owner — that's bureaucracy theater (a watchdog failure mode).
  • Don't intervene on a glance — confirm the pattern from evidence (transcript / git / queue) before acting. A false alarm costs the fleet trust and tokens.
  • Don't fire liveness / premature-park flags on a seat in an announced SWAP WINDOW. A seat mid-handover looks idle/parked to the detectors. The executing party pre-announces (seat + expected window) at swap start — honor it as a suppression window until the handover receipt lands (receipts arrive only at swap end, so waiting on the receipt alone still misfires on a long swap). See retiring-and-inheriting-a-seat.

Cheap + deep model economics

Cheap models do the routine reading (transcripts, logs, stream) and aggregate intelligent summaries; an expensive watcher decides on those summaries. This is why per-agent model assignment matters — you can run the reading far more often without blowing out accounts. Design the pod so the deep model never touches the raw firehose.

See also

  • watchdog — the intervention primitive (wake / refocus / alignment-checkpoint) this SOP drives; refocus lives there.
  • refocus — the drift doctrine watchdog operationalizes.
  • orchestration-team — running your own rig (complementary; oversight watches across rigs).
  • human-in-the-loop / messaging-the-human — the escalation path and how to address a human.
  • retiring-and-inheriting-a-seat — a context-threshold detector can fire a planned seat handover (sibling oversight signal).

Frequently asked questions

What does the Oversight Team AI skill do?

Use when you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention ladder (orchestrator-ping -> refocus -> human escalation), and the cheap+deep model economics. NOT for orchestrating your OWN rig (orchestration-team) and NOT the intervention primitive mechanics (watchdog).

Why use Oversight Team on TypingMind?

Because you install it once and use it with any model. Oversight Team 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 Oversight Team 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/pods/oversight-team. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Oversight Team?

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 Oversight Team?

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

Is the Oversight Team 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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