Install
npx skills add growthxai/output --skill output-migrate
Runs the open skills CLI and installs the skill for your agent (Claude Code, Codex, Cursor …). Alternatively copy the skill folder into .claude/skills/ or upload it in Claude.ai under Settings → Skills.
As a Claude Code plugin
/plugin marketplace add anthropics/claude-plugins-official
/plugin install outputai@outputai
This skill is part of the plugin “outputai”. In Claude Code the plugin installs it together with the plugin’s other skills, commands and hooks.
What does the output-migrate skill do?
Description from the SKILL.md in the source repository.
Questions about output-migrate
What does the output-migrate skill do?
Upgrade a project between versions of the Output framework. Use when the user asks to upgrade, migrate, or move to a newer Output version. Detects the current @outputai/* version in the project, fetches the matching migration guide from docs.output.ai, applies the changes, and verifies the project still type-checks.
How do I install output-migrate?
In a terminal: npx skills add growthxai/output --skill output-migrate. The skills CLI asks which agents to install it for. In Claude.ai you can upload the skill folder as a ZIP under Settings → Skills.
Which agents can use output-migrate?
Skills use the open SKILL.md format, which Claude Code, Claude.ai, the Claude API, OpenAI Codex, Cursor, Gemini CLI and other agents support.
Is output-migrate safe to use?
Our automatic check found no risky patterns (piped shell scripts, hidden characters, secret access, prompt overrides). It is not a full audit – read the SKILL.md and any scripts before installing.
More skills from growthxai/output
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Pre-flight validation checks for Output SDK workflow operations. Ensures conventions are followed, requirements are gathered, and quality gates are passed before workflow execution.
Post-flight validation for Output SDK workflow operations. Systematic verification of step completion, convention compliance, quality validation, and deliverable verification.
Validate LLM judges against human labels using TPR/TNR metrics and train/dev/test splits. Use after writing a judge prompt to verify it agrees with human judgment.
Design effective LLM judge .prompt files for evaluators. Use when creating judgeVerdict/judgeScore/judgeLabel prompts, or when existing judges produce unreliable results.
Systematically review workflow traces to identify failure modes before building evaluators. Use when starting an eval project, after significant pipeline changes, or when production quality drops.
Handle errors caught in Output workflows without losing failure context. Use when adding workflow try/catch logic, fallbacks, partial-failure handling, custom Error classes, or typed checks for errors thrown by steps and evaluators.
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