Agent Watchdog logo

Agent Watchdog

OrganizationPopular
BuilderIO
agent-watchdog

Use when asked to watch, babysit, audit, review, compare, or fix another agent's work from a Codex session ID, Claude Code session/transcript, chat/thread link, PR, branch, log, or pasted run summary. Monitor until the other agent is done or blocked, reconstruct what the user asked, independently investigate the same problem to form your own hypotheses and approach, inspect what the agent actually changed and verified, compare the two investigations, report gaps and add-on directions, and optionally make scoped fixes when the user authorizes repair.

Overview

PublisherBuilderIO
Repositoryskills
Skill nameagent-watchdog
Stars
4.3K
Forks
211
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 BuilderIO on GitHub. Read the source before you install it.

Installation

Install the Agent 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.
  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/BuilderIO/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/agent-watchdog .claude/skills/agent-watchdog
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent 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 Agent 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 Agent 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.

Agent Watchdog

Watch another agent's work like a reviewer with a pager who is also a second investigator: wait for completion when needed, reconstruct the request, run your own independent investigation of the same problem, verify the evidence, and close the gap between what was asked and what actually happened. You are not just grading their homework — you are a second solver whose findings get diffed against theirs so nothing is missed.

Choose The Mode

Infer the mode from the user's wording:

  • Watch only: monitor a session, PR, branch, CI run, or transcript until it reaches a terminal state. Do not edit files.
  • Audit: read the prompt, transcript, diff, tests, CI, comments, screenshots, or final claims, run the independent investigation below, and return a gap report plus add-on directions. Do not edit files.
  • Audit and fix: audit first, then make narrow fixes for clear gaps. Avoid broad rewrites, branch movement, or speculative changes.
  • Compare: when given multiple sessions or agents, compare their work against the same original request and reconcile the important differences.

If authority is unclear, default to audit-only and say what you would fix.

Resolve The Target

  1. Identify every artifact the user supplied: session ID, transcript path, thread URL, PR, branch, commit, CI run, issue, Slack link, or pasted summary.
  2. Use the host's native thread/history tools, local transcript files, repo logs, GitHub tools, or pasted content to resolve the artifact. Prefer the most direct source over summaries.
  3. If the artifact is still running and the user asked to watch, poll at a reasonable interval until it is done, blocked, stale, or clearly waiting on a human/external system.
  4. If the artifact cannot be resolved, ask for the missing identifier or path.

Reconstruct The Contract

Build a compact contract before judging the work:

  • Original user request and any later changes in scope.
  • Explicit constraints: branch rules, no-edit requests, deadlines, package versions, validation expectations, design requirements, or security/privacy limits.
  • Implied acceptance criteria: user-visible behavior, tests, CI, docs, deploys, screenshots, review replies, or status updates.
  • The other agent's final claims and any "could not do" caveats.

Treat the user's request as the source of truth, not the other agent's summary.

Investigate Independently

Act as if the original prompt had been given to you, in parallel with auditing the watched agent. Anchoring is the failure mode: reading their work first and nodding along. Your value comes from a genuinely separate second pass.

  1. Form your own hypotheses about root causes and the approach you would take — ideally before reading the watched agent's conclusions, and if you have already seen them, still reason from first principles rather than from their framing.
  2. Explore the code, data, logs, production state, and docs yourself, directly or via subagents. While the watched agent is still running, use the wait to pre-map the problem domains so hypotheses are ready before their diff lands.
  3. Prioritize evidence the watched agent may not have looked at: production run ledgers or databases, session replays, error trackers, user-supplied screenshots, deploy/version state, other worktrees, memory of past incidents in the same area.
  4. Verify the watched agent's key claims against primary sources, and verify your own leads the same way — reopen the cited files and line refs before asserting either side is right. Subagent reports are leads, not facts.
  5. Diff the two investigations: what they found that you missed, what you found that they missed, where the approaches diverge, and any product or design fork they took silently. Convert the diff into concrete, actionable add-on directions (exact files, guards, test cases) — not vague concerns — and offer them as a paste-ready note the user can relay.

Relay Sparingly

Finding something is not a reason to send it yet. A watched agent that is still building re-plans around every note it receives, so the cost of a relay is their attention and their sequencing, not your tokens.

  • Interrupt a live run only for what changes their current step: a defect in code they are touching now, a correction to something you told them earlier, or an answer they are blocked on.
  • Everything else — coverage gaps, reference material, conventions, polish — waits for their next checkpoint and travels as one batched note.
  • Never hand a mid-run agent an unranked list of what is missing. Ranked, and labelled as a map rather than a to-do list, or not at all: an unranked backlog reliably produces several half-finished surfaces instead of a few complete ones.
  • Say what you verified as correct, not only what is wrong. It stops them re-litigating settled ground and keeps the relationship peer-to-peer.
  • If the watched agent has not yet acted on your previous note, do not send another one.

Audit The Evidence

Inspect evidence, not vibes:

  • Read changed files and relevant unchanged files around the touched paths.
  • Check git status/diff without reverting unrelated work.
  • Compare commands the agent claimed to run with actual output when available.
  • Inspect failed or skipped tests, CI logs, browser screenshots, review comments, deploy output, and error traces.
  • For PR/review work, verify unresolved threads and CI state from the source system when tools are available.
  • For UI work, prefer screenshots or browser checks over prose claims.

Classify each issue as:

  • Gap: requested behavior is missing or incomplete.
  • Bug: the implementation likely fails or regresses behavior.
  • Verification miss: the work may be right but the evidence is weak.
  • Scope drift: the agent changed something unrelated or skipped a constraint.
  • No issue: the concern is already handled, with evidence.

Fix Narrowly

When the user authorized repair:

  1. Fix only gaps with clear evidence.
  2. Preserve unrelated local changes and do not move branches unless explicitly asked for that branch operation.
  3. Use existing repo patterns and targeted tests.
  4. Re-run the smallest useful validation after each meaningful fix.
  5. If a fix would require a product decision, credential, destructive action, or broad rewrite, stop and report the decision instead of guessing.

Report

Lead with the outcome. Keep the report short enough to scan:

md
Status
- Done, blocked, stale, or still running.

Requested
- What the user asked the watched agent to do.

Observed
- What the watched agent changed, claimed, and verified.

Gaps
- Missing behavior, bugs, weak verification, or scope drift.

Independent findings
- What your own investigation surfaced that the watched agent missed, where
  your approach would have differed, and concrete add-on directions to relay.

Fixes made
- Files changed and validation run. Omit this section for audit-only work.

Remaining risk
- Anything still unverified or waiting on CI/review/deploy/human input.

Name exact files, commands, PRs, or thread IDs when they matter.

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 Agent Watchdog AI skill do?

Use when asked to watch, babysit, audit, review, compare, or fix another agent's work from a Codex session ID, Claude Code session/transcript, chat/thread link, PR, branch, log, or pasted run summary. Monitor until the other agent is done or blocked, reconstruct what the user asked, independently investigate the same problem to form your own hypotheses and approach, inspect what the agent actually changed and verified, compare the two investigations, report gaps and add-on directions, and optionally make scoped fixes when the user authorizes repair.

Why use Agent Watchdog on TypingMind?

Because you install it once and use it with any model. Agent 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 Agent Watchdog in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BuilderIO/skills/tree/main/skills/agent-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 Agent 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 Agent Watchdog?

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

Is the Agent Watchdog AI skill free?

Yes. It is published on GitHub by BuilderIO 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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