Debugging Runtime Errors logo

Debugging Runtime Errors

Organization
friday-platform
debugging-runtime-errors

Loads when a session ends with `status: failed` AND a structured error message. Recognize: `did not call complete` (agent never called the injected complete tool); `emitted empty output` (called complete with `{}`); `emitted an empty response` (called complete with empty string); `LLM step failed:` (agent called failStep); `output does not match schema` (outputType validation failed); `Invalid job config` / `Invalid signal config` (Zod schema rejection on upsert). Each pattern has a one-line fix in the table below.

Overview

Publisherfriday-platform
Repositoryfriday-studio
Skill namedebugging-runtime-errors
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 Runtime Errors 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-runtime-errors
  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-runtime-errors .claude/skills/debugging-runtime-errors
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Runtime Errors 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 Runtime Errors 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 Runtime Errors 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 runtime errors

When a session emits status: failed with a specific error message, the message itself names the contract violation. This skill maps each error pattern to its fix.

Error → fix table

LLM action with outputTo '<doc>' did not call complete

The action set outputTo so the runtime injected a complete tool. The LLM finished without calling it.

Fix: update the agent's prompt to explicitly call complete:

"When you have the final result, call the `complete` tool with
`{response: '<your final text>'}`. The runtime captures your output
from this call."

See @friday/agent-action-handshake for the full contract.

LLM action with outputTo '<doc>' emitted empty output

The LLM called complete({}). Same fix — the prompt needs to specify what the args should contain.

LLM action with outputTo '<doc>' emitted an empty response

The LLM called complete({response: ""}). Same fix — the prompt needs to populate response with actual content.

LLM step failed: <reason>

The agent called failStep deliberately. Read the reason; it's usually accurate. Either:

  • The task was impossible given the agent's tools/inputs → adjust the inputs or the agent's tool surface.
  • The agent gave up too easily → tighten the prompt to handle the edge case.

Agent '<agentId>' output does not match <type> schema: ...

The action declared outputType: <type> and the agent's output doesn't match the JSON Schema in documentTypes.<type>. The error message names the specific path that failed validation.

Fix:

  • If the schema is wrong → update documentTypes.<type>.
  • If the agent's output is wrong → update the agent's prompt to produce data matching the schema, or add an example in the prompt.

Invalid job config: ... (on upsert_job)

Zod rejected the job config on upsert. Common causes:

  • execution.agents[] contained inline objects when only string IDs are accepted in Pattern A.
  • fsm.states.<state>.entry[] action type was not one of llm, agent, emit, notification.
  • outputTo placed on an inline type: llm action without the outer FSM wrapper.

Fix: load @friday/writing-workspace-jobs for the canonical Pattern A vs Pattern B shapes. The error message names the exact path that failed.

Invalid signal config: ... (on upsert_signal)

Zod rejected the signal config. Common cause: missed the inner config: {path: ...} and put path directly on the outer config.

Fix: the shape is

{
  id: "<signalId>",
  config: {
    provider: "http",
    description: "...",
    config: { path: "/your-path" }
  }
}

See @friday/writing-workspace-signals for templates.

Signal payload validation failed for '<signalId>': ...

The signal declared a JSON Schema and the trigger payload doesn't match.

Fix: match the schema, OR loosen the schema if the new payload shape is correct.

What NOT to do

  • Do NOT wrap the failing job in a try/catch and silently retry. These errors are contract violations; retrying without fixing them produces the same error.
  • Do NOT migrate Pattern A → Pattern B "to fix" a did not call complete error. That error means the agent prompt is wrong, not the FSM shape.

Cross-references

  • @friday/debugging-broken-jobs — the triage entry point.
  • @friday/agent-action-handshake — the cross-boundary contract most of these errors violate.
  • @friday/writing-workspace-jobs — FSM authoring.
  • @friday/writing-workspace-signals — signal config templates.

Frequently asked questions

What does the Debugging Runtime Errors AI skill do?

Loads when a session ends with `status: failed` AND a structured error message. Recognize: `did not call complete` (agent never called the injected complete tool); `emitted empty output` (called complete with `{}`); `emitted an empty response` (called complete with empty string); `LLM step failed:` (agent called failStep); `output does not match schema` (outputType validation failed); `Invalid job config` / `Invalid signal config` (Zod schema rejection on upsert). Each pattern has a one-line fix in the table below.

Why use Debugging Runtime Errors on TypingMind?

Because you install it once and use it with any model. Debugging Runtime Errors 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 Runtime Errors 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-runtime-errors. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging Runtime Errors?

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 Runtime Errors?

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

Is the Debugging Runtime Errors 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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