跳到正文
cccskills
免费在 GitHub 公开

oversight-team

Monitor other agent teams for abandoned work, process drift, off-task activity, and excessive token use. It guides evidence checks after alerts and chooses a proportionate response, from notifying the team’s coordinator to escalating to a human.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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).

查看安装方法

适用场景

  • Check whether unfinished work was abandoned
  • Detect process drift or off-task activity
  • Investigate excessive token use
  • Choose between a coordinator nudge and human escalation

包含的文件(1)

  • SKILL.md6.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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).

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Automate website interactions through a command-line browser tool. Navigate pages, fill forms, extract text, save screenshots or PDFs, reuse login sessions, and compare page states to check the effects of actions or website changes.

中文简介正在准备中。当前显示英语简介。

  • Automating form entry and submission
  • Extracting page text and saving screenshots
  • Comparing page appearance before and after changes
mvschwarz/openrig80152026年10月21日 更新

Design and maintain applications where OpenRig agents help run the system. Organize instructions, shared state, and artifact checks, then trace unexpected behavior to the code, skill, or schema that needs repair.

中文简介正在准备中。当前显示英语简介。

  • Designing an application operated by OpenRig agents
  • Organizing instructions and shared application state
  • Tracing unexpected behavior to its source
mvschwarz/openrig80152026年10月21日 更新

Design and run bounded procedures in environments where current conditions are uncertain. The skill separates agent judgment from precise tool actions, verifies each effect, and records checkpoints so interrupted work can resume from observed evidence.

中文简介正在准备中。当前显示英语简介。

  • Designing procedures for environments with varying conditions
  • Resuming interrupted operations from verified evidence
  • Deciding whether an agent or ordinary code should control a procedure
mvschwarz/openrig80152026年10月21日 更新

Create reusable starting configurations that combine an agent’s role, startup context, and source history. The skill guides registry entries, checks whether their sources are current, and manages their progression from initial capture to recommendation or retirement.

中文简介正在准备中。当前显示英语简介。

  • Create reusable startup context for an agent role
  • Refresh an agent workspace from an existing starter
  • Check a starter’s source history and freshness
mvschwarz/openrig80152026年10月21日 更新

Guides the design and review of agent startup context, including role files and links to authoritative records. It helps identify stale guidance, missing updates, overloaded startup files, and instructions that lose the purpose of the work.

中文简介正在准备中。当前显示英语简介。

  • Designing startup files for a new agent
  • Reviewing stale guidance used by running agents
  • Separating role information from reusable procedures
mvschwarz/openrig80152026年10月21日 更新

Configure selected OpenRig command permissions in Claude Code or Codex at the agreed project or user scope. Preserve existing restrictions, verify that settings load and behave as intended, and record additions so they can be removed later.

中文简介正在准备中。当前显示英语简介。

  • Configuring command permissions before launching an agent team
  • Reducing repeated approvals for selected rig commands
  • Adding permission rules within a chosen project scope
mvschwarz/openrig80152026年10月21日 更新

查看 mvschwarz 的全部技能

报告此技能的问题