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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Guide authors through a structured self-review before requesting peer review.
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See how to installReview the instructions the agent will receive before you install.
I help you review your own pull request through a structured five-category checklist before you request external review:
By catching issues before external review, you save reviewers' time and reduce review cycles.
Supporting files in this skill:
assets/self-review-checklist.template — The five-category checklist as a markdown file you can copy into a PR commentUse when you want to:
Stage: Author pre-review checklist (after implementation, before external review).
pr-create when you need to draft the PR title/body before opening it.pr-review when reviewing someone else's PR as a reviewer.pr-split when the core question is whether to split a large PR."Review my own PR before I request review" "Walk me through a self-review checklist" "Help me check my PR for debug code and missing tests"
Here's how I guide you through self-review:
I'll ask for your PR diff or a link to the open PR if not provided in context.
I'll ask: Does the PR description accurately describe every file in the diff?
I'll ask: Are all files in the diff intentionally included?
I'll ask: Are there any obvious leftover debug artifacts? I'll flag obvious issues like:
console.log, print, println, println! (debug output)debugger, pdb, debug! (breakpoints).only, .skip on test cases (ensures tests run)For TODO/FIXME comments, I'll ask: did you intend to resolve these before requesting review?
Note: Thorough artifact detection is your reviewers' job—this catches the obvious ones you might have missed.
I'll ask: Does every changed function/method have test coverage?
I'll ask: Is this PR focused on a single concern, or does it mix unrelated changes?
/pr-split skillI'll summarize the findings:
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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.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.