debugging-empty-output
Loads when a workspace job tool returns `success: true, status: completed` AND (`summary: ""` OR `artifactIds: []` OR summary equals "Session completed without producing a summarizable document"). Diagnostic checklist for sessions that ran successfully but emitted nothing useful. Most common cause: the agent's prompt didn't instruct the LLM to call `complete()` for an `outputTo` action. Do NOT migrate Pattern A → Pattern B FSM as a fix — that's almost always wrong.
Profile →npx skills add friday-platform/friday-studio --skill debugging-empty-output --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Debugging empty-output sessions Symptom: session `status: completed` but the chat-tool wrapper got back nothing useful — empty `summary`, empty `artifactIds`, or the sentinel "Session completed without producing a summarizable document." ## Diagnostic checklist Walk in order. Each step has a concrete fix. ### 1. Read `aiSummary.summary` from `describe_session` The daemon synthesizes a useful dia
What does the debugging-empty-output skill do?
Loads when a workspace job tool returns `success: true, status: completed` AND (`summary: ""` OR `artifactIds: []` OR summary equals "Session completed without producing a summarizable document"). Diagnostic checklist for sessions that ran successfully but emitted nothing useful. Most common cause: the agent's prompt didn't instruct the LLM to call `complete()` for an `outputTo` action. Do NOT migrate Pattern A → Pattern B FSM as a fix — that's almost always wrong.
How do I install it?
Run `npx skills add friday-platform/friday-studio --skill debugging-empty-output --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From friday-platform/friday-studio, a repository with 98 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.