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Debug a workflow YAML — validate, trace execution, simulate state flow, and catch issues before runtime.
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SOC 職業分類に基づく
Deep analysis of current changes before committing. Goes beyond lint — catches logic bugs, architectural violations, security, and performance issues.
Create a pull request from the current branch, matching the repo's PR conventions (What/Why/Testing for features, Summary/Test-plan for small PRs).
Review staged or unstaged changes before committing. Catches bugs, style drift, and missing coverage.
Cut a release — promote Unreleased → versioned in CHANGELOG, bump version, commit. CI auto-tags and publishes from the version-bump commit.
Create a release-tracking issue that organizes open issues into a phased roadmap. Pass a target version (e.g., `0.5.0`) to scope the plan.
Run the full quality gate — format, lint, tests, types. Use after changes to verify nothing is broken.
| name | debug-workflow |
| description | Debug a workflow YAML — validate, trace execution, simulate state flow, and catch issues before runtime. |
Debug the workflow at the path given in $ARGUMENTS (or ask for one).
uv run agentloom validate $ARGUMENTSuv run agentloom visualize $ARGUMENTSRead the YAML and trace execution mentally:
output: field. Does each step's prompt: reference variables that will exist at that point? Walk through the state dict step by step.state.x is a dict, not a string)?tool_name a registered tool? Are tool_args valid for that tool's schema? Read tools/builtins.py to check.budget_usd is set, estimate cost per step (model + estimated tokens). Will it exceed?For each step in execution order, write out:
Step: <id> (<type>)
Input state: {key: expected_value, ...}
Expected output: <what this step produces>
State after: {key: new_value, ...}
Risk: <what could go wrong>