SiinXu

draft-issue

Draft a high-quality English GitHub issue for this repository with dedupe and evidence-verified claims.

SiinXu 2 1 Updated 3w ago
GitHub

Install

npx skillscat add siinxu/stock-pulse-ai/claude-skills-draft-issue

Install via the SkillsCat registry.

SKILL.md

Draft a high-quality issue in this repository's style: dedupe first, then structure as "background evidence → proposal → boundaries → acceptance", producing an English body plus label suggestions. Produces a draft only by default; publishing requires confirmation.

Repository: https://github.com/SiinXu/stock-pulse-ai/issues

Source of truth: repository root AGENTS.md. Issue titles and bodies published to GitHub must be in English.

Usage

/draft-issue <one-line problem/idea description> [--type bug|feature|design|docs|chore]

Instructions

Issue titles and bodies published to GitHub must be in English.

Step 1: Dedupe and relate

gh issue list --repo SiinXu/stock-pulse-ai --state open --search "<keywords>" --limit 20
gh issue list --repo SiinXu/stock-pulse-ai --state closed --search "<keywords>" --limit 10
  • An open issue already covers the topic → recommend commenting there instead of opening a new issue; output the comment draft and stop.
  • Related but distinct → continue drafting, reference it as #<number> in the body, and state the difference explicitly.

Step 2: Verify the evidence

Every factual claim in the body needs a source:

  • Bug: reproduction steps and the actual code location (file:line or function name). No "seems broken / probably broken". If it cannot be reproduced, say so and state what the claim is based on.
  • Feature/design: describe the current state (where the existing implementation lives, what is missing), citing relevant docs (docs/*.md) or existing issues.
  • Never invent config names, filenames, or API fields — verify every name exists in the repository.

Step 3: Compose (English body skeleton)

## Summary
<one paragraph: what the problem/request is, who is affected>

## Background / Evidence
<current state and evidence: repro steps, code locations, audit references; for bugs, actual vs expected behavior>

## Proposal
<direction, in steps; state explicitly which existing modules are reused — no parallel implementations>

### Non-goals
<what is explicitly out of scope, to prevent scope creep>

## Scope / Boundaries
<directories/file domains involved; whether high-risk areas are touched (config semantics, API/Schema, data-source fallback, report structure, auth, scheduling, release, desktop startup)>

## Acceptance Criteria
<decidable items (commands, numbers, allow/deny lists), each individually checkable>

## Related
<#issue references and how they relate>
  • Title: English, describes the actual change (fix: / feat: / design: prefix style consistent with the repo), no tool/agent attribution.
  • Splitting: if the proposal contains more than two independent deliverables, recommend splitting into multiple issues with a split proposal instead of one mega-issue.

Step 4: Label suggestions

Check existing labels (gh label list --repo SiinXu/stock-pulse-ai --limit 100) and suggest a set: type (bug/enhancement/documentation) + domain (web/desktop/data-source/agent/i18n/security — only ones that actually exist) + priority (P0P3, only with explicit justification). Do not suggest creating new labels.

Step 5: Deliver

Output: English issue title + body + label suggestions + dedupe conclusion.

Division of labor vs existing skills

  • analyze-issue: evaluates an existing issue (validity, priority, fix entry point); this skill covers drafting and dedupe before creation. Both share the same quality bar: verifiable evidence, decidable acceptance criteria.

Allowed Auto-Actions (No Confirmation Needed)

  • Read-only queries such as gh issue list / gh label list
  • Reading code to verify evidence; writing local drafts

Actions Requiring Confirmation

  1. gh issue create (publishing the issue)
  2. Commenting on an existing issue