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termio-shtermio-sh/termio/skills/issue-creator/SKILL.md

issue-creator

Skill787 starsChanged 3 months agoMITView on GitHub

Turn vague customer or user feedback from pasted text, chat excerpts, or screenshots into a clear, evidence-backed GitHub issue without inventing requirements. Use when the user says 'create an issue for this', 'turn this user request into an issue', 'file this feedback', '用户反馈建 issue', '把这个模糊需求变成 issue', '把这段聊天/截图建成 GitHub issue', or invokes /issue-creator.

3 other projects use a version of this.

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The file

Show its rules and text

We split this skill into the rules it gives an agent

22 rules. 0 are common, 21 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 21

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Mixed: treat the pasted explanation as context and the screenshot as primary evidence unless they conflict.

    line 21
  2. Distinctive:

    Do not force the request into “As a user, I want…” language.

    line 36
  3. Distinctive:

    If the message is phrased as a question, identify the latent need but first check whether existing behavior or documentation already …

    line 39
  4. Distinctive:

    Use an explicitly named repository.

    line 45
  5. Distinctive:

    Read any relevant issue template or contributing guide.

    line 54
  6. Distinctive:

    Do light, targeted exploration using nouns, UI labels, errors, and capability names from the intake.

    line 60
  7. Distinctive:

    Stop when there is enough context to name the affected area, identify existing behavior, or determine that this is a product-level …

    line 68
  8. Distinctive:

    Ask at most three focused questions, and only when an answer would change one of:

    line 74
  9. Distinctive:

    When feedback is too broad for an implementation issue, create a bounded discovery issue such as “Define task-completion notification …

    line 96
  10. Distinctive:

    Run separate searches using:

    line 102
  11. Distinctive:

    If a strong duplicate exists, return it instead of creating another issue.

    line 114
  12. Distinctive:

    Read references/templates.md, choose the matching template, and omit empty sections.

    line 119
  13. Distinctive:

    Write a direct, scannable title under 72 characters.

    line 124
  14. Distinctive:

    Add file/component references only when verified by repository exploration.

    line 128
  15. Distinctive:

    Write acceptance criteria only for observable behavior supported by the request or established product conventions.

    line 129
  16. Very specific:

    Put unresolved product decisions under Open questions.

    line 131
  17. Distinctive:

    Keep one issue focused on one outcome.

    line 132
  18. Distinctive:

    If the input includes one or more images, read references/images.md and follow its privacy, upload, and verification workflow.

    line 136
  19. Distinctive:

    Before any GitHub write, state the exact repository, proposed title, and any image asset path.

    line 142
  20. Distinctive:

    Use an existing issue type or label only after confirming it exists.

    line 151
  21. Distinctive:

    Do not create the issue until all applicable checks pass:

    line 164
  22. Distinctive:

    No behavior, scope, or acceptance criterion was invented.

    line 169

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Vague user request → actionable GitHub issue

Preserve the user's voice, identify the underlying problem, add only grounded repository context, and create the smallest honest issue that moves the work forward. Never turn missing information into confident requirements.

Workflow

1. Normalize the raw input

Handle all three input modes:

  • Pasted text: preserve the most useful original sentence verbatim.
  • Screenshot: inspect both visible text and UI context. Transcribe only what is legible and mark uncertainty. Keep the original image for Evidence.
  • Mixed: treat the pasted explanation as context and the screenshot as primary evidence unless they conflict; surface any conflict.

Build a private scratch intake before drafting:

FieldExtract
ActorWho experiences this?
SituationWhen and where does it happen?
FrictionWhat is hard, broken, or missing now?
Desired outcomeWhat should the user be able to achieve?
ImpactWhy does it matter?
Explicit constraintsWhat did the user actually require?
UnknownsWhat remains unclear?

Do not force the request into “As a user, I want…” language. Preserve a direct quote when it expresses the pain better than a summary.

If the message is phrased as a question, identify the latent need but first check whether existing behavior or documentation already answers it. Do not file a feature request for something the product already supports.

2. Resolve repository conventions

Use an explicitly named repository; otherwise resolve the current checkout:

gh auth status
gh repo view --json nameWithOwner,defaultBranchRef,visibility,hasIssuesEnabled
gh issue list --state all --limit 10 --json number,title,state,url
rg --files -g 'CONTRIBUTING*' -g '.github/ISSUE_TEMPLATE/**'

Read any relevant issue template or contributing guide. Match the repository's language, headings, title style, and metadata conventions. Do not invent labels, types, milestones, or assignees.

3. Ground the request in the product

Do light, targeted exploration using nouns, UI labels, errors, and capability names from the intake. Search README/docs first, then likely code:

rg -n -i 'user phrase|normalized capability|visible UI label' \
  README.md docs Sources Shared ios web 2>/dev/null

Stop when there is enough context to name the affected area, identify existing behavior, or determine that this is a product-level discovery request. Code exploration is context gathering, not implementation planning.

4. Apply the clarification gate

Ask at most three focused questions, and only when an answer would change one of:

  • whether an issue should exist;
  • bug vs feature/discovery classification;
  • the core problem or desired outcome;
  • reproduction of a bug;
  • a material scope boundary;
  • whether evidence is safe to publish.

First try to answer gaps from the screenshot, repository, docs, and related issues. Do not ask the user to design the solution or manufacture acceptance criteria. Put non-blocking uncertainty in Open questions and continue.

5. Classify honestly

TypeUse when
BugExisting behavior contradicts expected or documented behavior
Feature/usabilityA clear user outcome is unsupported or unnecessarily hard
DiscoveryThe need is real but the right behavior or scope still requires investigation
No new issueExisting behavior/docs solve it, or a strong duplicate already tracks it

When feedback is too broad for an implementation issue, create a bounded discovery issue such as “Define task-completion notification behavior” instead of inventing a complete solution.

6. Search for duplicates

Run separate searches using:

  1. the user's exact phrase;
  2. the normalized product capability;
  3. the symptom or desired outcome.
gh issue list --repo OWNER/REPO --state all \
  --search 'query in:title,body' --limit 20 \
  --json number,title,state,url

If a strong duplicate exists, return it instead of creating another issue. If the overlap is partial, create the new issue and link the related one.

7. Draft a problem-first issue

Read references/templates.md, choose the matching template, and omit empty sections.

Follow these rules:

  • Write a direct, scannable title under 72 characters.
  • Separate original evidence, verified facts, and inference.
  • Describe the problem and desired outcome before any possible solution.
  • Include the user's original words as a short quote when useful.
  • Add file/component references only when verified by repository exploration.
  • Write acceptance criteria only for observable behavior supported by the request or established product conventions.
  • Put unresolved product decisions under Open questions.
  • Keep one issue focused on one outcome; split independent requests.

8. Attach evidence when present

If the input includes one or more images, read references/images.md and follow its privacy, upload, and verification workflow. Do not silently drop an image.

9. Create and verify

Before any GitHub write, state the exact repository, proposed title, and any image asset path. Prepare the final Markdown body outside the repository, then:

gh issue create --repo OWNER/REPO \
  --title 'Concise problem or outcome' \
  --body-file /absolute/path/to/temporary-issue-body.md

Use an existing issue type or label only after confirming it exists. Then verify the returned issue:

gh issue view ISSUE_NUMBER --repo OWNER/REPO \
  --json number,title,state,url,body,labels

Confirm the intended repository, title, body, evidence links, and metadata. Return the issue URL plus any assumptions or open questions retained in it.

Quality gate

Do not create the issue until all applicable checks pass:

  • The original request is represented faithfully.
  • The core problem and desired outcome are understandable.
  • Facts and inference are distinguishable.
  • No behavior, scope, or acceptance criterion was invented.
  • Existing docs/code and likely duplicate issues were checked.
  • The issue is actionable, or explicitly scoped as discovery.
  • Supplied evidence is embedded and safe for the repository's visibility.
  • The issue follows repository templates and conventions.

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