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Debugging Broken Jobs

Organization
friday-platform
debugging-broken-jobs

Triage entry-point for any "the job ran but something's wrong" situation in a Friday workspace. Loads when a job tool returns `output-error`, `success: false`, `summary: ""`, `artifactIds: []`, or any session with `status: failed`. Routes to the right sibling debug skill. Required reading before declaring "platform bug" / "FSM wiring is broken" / "this is a known issue" — those claims are FORBIDDEN without first walking this triage table.

Overview

Publisherfriday-platform
Repositoryfriday-studio
Skill namedebugging-broken-jobs
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 Broken Jobs 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-broken-jobs
  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-broken-jobs .claude/skills/debugging-broken-jobs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Broken Jobs 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 Broken Jobs 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 Broken Jobs 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 broken jobs

Stop. Before you form a hypothesis, walk this triage. The job tool's return shape and the session's agentBlocks[].toolCalls carry the diagnosis 95% of the time. The remaining 5% is rarely a platform bug and almost always a contract violation by the agent or the FSM.

Diagnostic discipline (non-negotiable)

You may NOT claim "platform bug" / "FSM wiring is broken" / "this is a known issue" until you have:

  1. Called describe_session(sessionId) on the failing session.
  2. Read agentBlocks[].toolCalls to see what tools the agent actually invoked.
  3. Verified the contracted exit (complete for outputTo actions, terminal text otherwise) was attempted.

If you skipped any of those, you don't know enough to claim a bug. Run the steps first.

Run a control before blaming your own change. When something you just created or edited fails, fire an equivalent request at something you have not touched this session. If the untouched thing fails the same way, your edit is not the cause — you're looking at a shared layer (request envelope, auth, a stale cache, the runtime), so stop re-editing your change. If only your edited thing fails, then it is your edit. Either branch halves the search space in one call; the expensive mistake is looping on your change on the assumption it must be the culprit.

Symptom triage table

Match the job tool's return shape OR the session's status, then load the matching sibling skill.

Symptom signatureLoad
success: true, status: completed, summary: "", artifactIds: []debugging-empty-output
success: true, status: completed, summary mentions "Session completed without producing a summarizable document"debugging-empty-output
Session shows many tool calls of the same name, no terminal text, hit stopWhen: stepCountIs(...)debugging-tool-loops
Session status: failed, error contains LLM action with outputTo '...' did not call completedebugging-runtime-errors
Session status: failed, error contains emitted empty output or emitted an empty responsedebugging-runtime-errors
Session status: failed, error contains LLM step failed: ... (agent called failStep)debugging-runtime-errors
Session status: failed, error contains Agent '<id>' output does not match <type> schemadebugging-runtime-errors
Session status: failed, error contains Invalid job config or Invalid signal config (Zod rejection)debugging-runtime-errors
Job tool returned output-error with no meaningful error text (typically called with bare {})debugging-job-invocation
Job tool returned output-error mentioning "tool isn't bound" — this is wrong; you forgot a prompt argdebugging-job-invocation
Model tried to call unavailable tool '<name>' — you created the job this session; it has no bound tool yetdebugging-job-invocation
External MCP tool returned 422 / 401 / 403 with provider-side errorcheck the workspace's MCP server credentials; not a platform bug

If no row matches, gather more evidence — don't escalate.

What "diagnostic discipline" looks like in practice

text
USER: run stale
ASSISTANT [calls workspace job tool]
JOB TOOL: { success: true, status: completed, summary: "", artifactIds: [] }
ASSISTANT [recognizes empty-output signature]
ASSISTANT [load_skill: debugging-empty-output]
ASSISTANT [calls describe_session(sessionId)]
ASSISTANT [reads agentBlocks[0].toolCalls]
ASSISTANT [observes complete was not called; agent emitted text only]
ASSISTANT: The job ran successfully but the agent emitted prose without
calling `complete`. Per the `@friday/agent-action-handshake`
contract, an `outputTo` action requires the agent to call complete
with the final text. Fix the agent's prompt to instruct it explicitly.

What you must NOT do:

text
USER: run stale
ASSISTANT [calls job tool]
JOB TOOL: { summary: "" }
ASSISTANT: This looks like a platform bug. Let me try migrating from
Pattern A to Pattern B FSM…

That's a contract violation by you. Load this skill, walk the table, diagnose properly.

Cross-references

  • @friday/agent-action-handshake — the FSM action ↔ agent prompt contract that most empty-output failures violate.
  • @friday/writing-workspace-jobs — FSM authoring once you know what to fix.
  • @friday/workspace-api — agent CRUD once you know the agent prompt is the broken part.

Frequently asked questions

What does the Debugging Broken Jobs AI skill do?

Triage entry-point for any "the job ran but something's wrong" situation in a Friday workspace. Loads when a job tool returns `output-error`, `success: false`, `summary: ""`, `artifactIds: []`, or any session with `status: failed`. Routes to the right sibling debug skill. Required reading before declaring "platform bug" / "FSM wiring is broken" / "this is a known issue" — those claims are FORBIDDEN without first walking this triage table.

Why use Debugging Broken Jobs on TypingMind?

Because you install it once and use it with any model. Debugging Broken Jobs 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 Broken Jobs 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-broken-jobs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging Broken Jobs?

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 Broken Jobs?

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

Is the Debugging Broken Jobs 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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