Debugging Job Invocation logo

Debugging Job Invocation

Organization
friday-platform
debugging-job-invocation

Loads when a workspace job tool (called from chat) returns `output-error` with no meaningful error text — typically because the tool was invoked with bare `{}`. Job tools require `{prompt: "..."}` — the prompt becomes the signal payload. Do NOT conclude "the tool isn't bound to this chat session"; retry with a prompt arg first. Also covers Pattern A vs Pattern B job shapes and the manual / signal-name distinction.

Overview

Publisherfriday-platform
Repositoryfriday-studio
Skill namedebugging-job-invocation
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 Job Invocation 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-job-invocation
  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-job-invocation .claude/skills/debugging-job-invocation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Job Invocation 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 Job Invocation 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 Job Invocation 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 job invocation

Symptom: a workspace job tool was called from chat and returned state=output-error with little or no error text. The chat agent's instinct is to declare "the tool isn't bound to this chat session" and abandon the job. That instinct is wrong.

Quick fix: pass a prompt arg

Job tools accept {prompt: "..."} — the prompt becomes the signal payload. Calling with bare {} errors out before the FSM even fires.

Wrong:

job_tool_name({})  → output-error

Right:

job_tool_name({prompt: "Run the report."})  → success / structured failure

Always include prompt. If the job's signal has a more specific schema (e.g. {message_id: string, tone: string}), pass that instead — but prompt is the universal fallback.

Pattern A vs Pattern B distinction

Workspaces have two job shapes:

  • Pattern A (execution.strategy: sequential): a list of agents that run in order. The final agent's assistant text becomes the session's natural output. Captured by the chat-tool wrapper without explicit outputTo.
  • Pattern B (fsm:): a state machine with named states, entry actions, and explicit outputTo documents. Output flows through the FSM document store; needs outputTo for the caller to see anything.

The chat trajectory that motivated this skill migrated a Pattern A job to Pattern B "to get outputTo" — that was the wrong fix. The original Pattern A would have worked with a small prompt change.

Rule of thumb: if the existing job is Pattern A and producing empty output, the fix is in the agent's prompt, not in the FSM shape. Migrating Pattern A → Pattern B introduces the complete-injection contract that is itself a common failure source — see @friday/debugging-empty-output.

manual is a string, not a magic value

Some Pattern A jobs use triggers: [{signal: "manual"}]. This means "this job is invoked directly via the chat-side tool wrapper, not via an HTTP signal." It is NOT the FSM event name and there's no signal called manual registered.

If you migrate this job to Pattern B FSM and try to use manual as the FSM event name in idle.on.manual, that's syntactically valid but semantically nonsensical — there's no signal firing manual events.

For Pattern B, register a real HTTP signal (e.g. stale-signal with path: /stale) and reference it in both triggers and fsm.idle.on.<signal>.

When the job tool genuinely isn't bound

Distinct from the bare-{} case above: here the tool name does not exist in your tool set at all — calling it returns Model tried to call unavailable tool '<name>'.

The usual cause: you created the job this session. Job tools are bound from a config snapshot taken when the chat session started, so a job you just made via upsert_job has no dedicated tool until the next session. This is expected, not a bug — and you do NOT need to tell the user to send another message.

Fix: run it with trigger_signal. Call trigger_signal({ signalId, payload }) with the job's trigger signal id. That fires the job through the signal endpoint and blocks until it completes — the in-session equivalent of the bound tool.

Other real causes (rare):

  • Jobs whose triggers list is empty — no signal, nothing to fire.
  • Jobs whose signal is unknown to the runtime.

Diagnose by calling list_jobs(workspaceId). If the job appears with a trigger signal, invoke it via trigger_signal with that signal id. If it has no trigger, fix the workspace config.

Cross-references

  • @friday/debugging-broken-jobs — the triage entry point.
  • @friday/debugging-empty-output — Pattern A → B migration is rarely the right fix for empty output.
  • @friday/writing-workspace-jobs — Pattern A vs B authoring.
  • @friday/writing-workspace-signals — signal authoring.

Frequently asked questions

What does the Debugging Job Invocation AI skill do?

Loads when a workspace job tool (called from chat) returns `output-error` with no meaningful error text — typically because the tool was invoked with bare `{}`. Job tools require `{prompt: "..."}` — the prompt becomes the signal payload. Do NOT conclude "the tool isn't bound to this chat session"; retry with a prompt arg first. Also covers Pattern A vs Pattern B job shapes and the manual / signal-name distinction.

Why use Debugging Job Invocation on TypingMind?

Because you install it once and use it with any model. Debugging Job Invocation 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 Job Invocation 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-job-invocation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging Job Invocation?

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 Job Invocation?

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

Is the Debugging Job Invocation 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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