Implement comprehensive error handling for Python code paths to keep services resilient and user-friendly. Use when failures are currently silent or exceptions leak through.
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Backport fixes from main onto release/YYYY.WW in the ai repo with cherry-pick and rebase merge so release-please changelogs stay correct. Use when hotfixing a release branch, cherry-picking to release/*, or preparing a release backport PR.
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See how to installReview the instructions the agent will receive before you install.
release/*main to backport (merge commits or PR numbers).git fetch origin main release/YYYY.WW
git checkout -b hotfix/UN-XXXXX origin/release/YYYY.WW
git cherry-pick <sha1> <sha2> ...
release/YYYY.WW with a list of backported commits/PRs in the description..github/scripts/check-release-lineage.sh — each PR commit must be on main or patch-equivalent (git cherry - prefix). Cherry-picks get new SHAs; identical hashes are not required. No commit-metadata bypass (author/subject are forgeable).release-please--* PRs skip lineage in ci.yaml (automation-owned version/changelog bumps).release-please opens chore: hotfix release/YYYY.WW to YYYY.WW.N. Approve and rebase-merge that PR too. PyPI publish follows the GitHub Release event.
release-process — versioning and changelog ownershipgit-conventional-commits — commit subjects on cherry-picked commits must stay conventionalmodel-deployment LESSONS-LEARNED — monorepo pins after AI packages shipNo reviews yet. Tell others what you thought.
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Implement comprehensive error handling for Python code paths to keep services resilient and user-friendly. Use when failures are currently silent or exceptions leak through.
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Tabular and numerical data analysis with descriptive statistics and insights. Use when the user provides data, tables, CSVs, or numbers and wants analysis.
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Financial factsheet analysis with key metrics extraction and investment rationale
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Diagnose and fix CI failures without leaving your editor.
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Reproduce ai-repo PR checks locally with Poe and CI scripts, including per-package typecheck and coverage behavior. Use when validating changes before push or when user asks which local commands match CI.
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Ask clarifying questions before implementing to ensure Python requirements are understood. Use when a task lacks detail, dependencies are unclear, or multiple interpretations are possible.
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