Debugging Tool Loops logo

Debugging Tool Loops

Organization
friday-platform
debugging-tool-loops

Loads when a session shows many tool calls (often the same name repeated) with no terminal text, no `complete` call, and either hits `stopWhen: stepCountIs(...)` (default 10) OR exhausts max output tokens. The LLM is in a tool loop without producing a final message. Distinguish from `debugging-empty-output` — that's about contract exits being absent; this is about the LLM never deciding to stop. Required: the agent prompt needs an explicit termination criterion.

Overview

Publisherfriday-platform
Repositoryfriday-studio
Skill namedebugging-tool-loops
Stars
102
Forks
5
Bundled files
Instructions only
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 friday-platform on GitHub. Read the source before you install it.

Installation

Install the Debugging Tool Loops 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/friday-platform/friday-studio/tree/main/packages/system/skills/debugging-tool-loops
  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/friday-platform/friday-studio.git /tmp/friday-studio
mkdir -p .claude/skills
cp -r /tmp/friday-studio/packages/system/skills/debugging-tool-loops .claude/skills/debugging-tool-loops
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Tool Loops 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 Debugging Tool Loops 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 Debugging Tool Loops 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.

Debugging tool loops

Symptom: agent makes 8+ tool calls (often repeating searches with slight variations), eventually hits stepCountIs(10) cap or runs out of output tokens, ends with output: {response: ""} or empty complete args.

Diagnostic checklist

1. Confirm the loop signature

describe_session(sessionId) → session.agentBlocks[].toolCalls

Look for:

  • 6+ tool calls in a single agent block.
  • Repeated calls to similar tools (search_issues × 3, search_pull_requests × 3, then more search variations).
  • No complete or failStep toolName in the list.
  • Final assistant text is empty.

If the agent IS calling complete but with empty args, that's @friday/debugging-empty-output step 2, not this skill.

2. Check the agent prompt's termination criterion

A loop usually means the agent doesn't know when to stop searching. Look for prompts like:

"Search for all stale issues across all repos."

The LLM keeps searching because "all" is open-ended. Add an explicit stop condition:

"Search exactly these 6 queries (3 issue queries × 3 PR queries).
After all 6 return, stop and call `complete` with the report. Do not
search again."

3. Check whether the agent has the right terminal tool

If outputTo is set, the runtime auto-injects complete and stops the LLM when complete is called (stopWhen: hasToolCall("complete")). If outputTo is NOT set, the LLM stops only on stepCountIs(...) or max output tokens. Without an outputTo, you're at the mercy of the LLM's own sense of when to stop.

Consider adding outputTo: response to the FSM action to give the LLM a clear exit hatch.

4. Bump max_steps only as a last resort

The default max_steps: 10 is generous for most agents. If you genuinely need more (e.g., a job that fans out to 20 repos), bump it in the action's config.max_steps. But: if you're hitting 20 steps and still not done, the prompt is wrong, not the budget.

What NOT to do

  • Do NOT bump max_output_tokens to "fix" the loop. Token budget isn't the issue — the agent isn't producing tokens, it's producing tool calls. More budget = more loops.
  • Do NOT swap to a more capable model. Tool loops aren't a model capability problem; they're a prompt instruction problem.

Cross-references

  • @friday/debugging-broken-jobs — the triage entry point.
  • @friday/debugging-empty-output — sibling for "complete present but empty" cases.
  • @friday/agent-action-handshake — the outputTo + complete injection contract that gives a terminal exit.

Frequently asked questions

What does the Debugging Tool Loops AI skill do?

Loads when a session shows many tool calls (often the same name repeated) with no terminal text, no `complete` call, and either hits `stopWhen: stepCountIs(...)` (default 10) OR exhausts max output tokens. The LLM is in a tool loop without producing a final message. Distinguish from `debugging-empty-output` — that's about contract exits being absent; this is about the LLM never deciding to stop. Required: the agent prompt needs an explicit termination criterion.

Why use Debugging Tool Loops on TypingMind?

Because you install it once and use it with any model. Debugging Tool Loops 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 Debugging Tool Loops in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/friday-platform/friday-studio/tree/main/packages/system/skills/debugging-tool-loops. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging Tool Loops?

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 Debugging Tool Loops?

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

Is the Debugging Tool Loops AI skill free?

It is published on GitHub by friday-platform. Check the repository for licensing terms. 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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