Twg Jira Resolve Merged Work logo

Twg Jira Resolve Merged Work

Organization
zenobi-us
twg-jira-resolve-merged-work

Clean up stale or unresolved Jira workitems. Dry-run single items, lists, boards, sprints, epics, or projects by matching Jira keys/titles to merged PRs, repos, Rovo/search-code hits, and assignee activity.

Overview

Publisherzenobi-us
Repositorydotfiles
Skill nametwg-jira-resolve-merged-work
Stars
67
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zenobi-us on GitHub. Read the source before you install it.

Installation

Install the Twg Jira Resolve Merged Work AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zenobi-us/dotfiles.git /tmp/dotfiles
mkdir -p .claude/skills
cp -r /tmp/dotfiles/files/devtools/agent/bundles/developer/skills/atlassian/twg-jira-resolve-merged-work .claude/skills/twg-jira-resolve-merged-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Twg Jira Resolve Merged Work in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Twg Jira Resolve Merged Work on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Twg Jira Resolve Merged Work is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

twg-jira-resolve-merged-work

Resolve stale Jira workitems only when merged PR evidence is strong. Plan first; mutate only after approval.

Use exact command grammar from live twg help / twg help describe; do not guess board, sprint, transition, workspace, repo, PR, or field syntax.

CLI launcher fallback

Run twg <command>. On shell command not found, use $HOME/.local/bin/twg (macOS/Linux) / $env:LOCALAPPDATA\Programs\twg\bin\twg.exe (PowerShell), then tell user to add that directory to PATH. Do not treat auth or command errors as PATH failures.

Scope

Accept one Jira scope:

  • One Jira workitem key/URL.
  • A small explicit list of Jira workitem keys/URLs.
  • Board or sprint ID/URL.
  • Epic key.
  • Jira space/project key.

Default to dry run. Ask before mutating unless the user requests reviewed execution. Use a bounded window; otherwise inspect recent active/completed sprint work and nearby PR merge dates.

Workflow

  1. Load Jira candidates.

    • Single/list: hydrate each provided workitem directly.
    • Sprint: jira sprint workitems query.
    • Board: board/backlog/sprint commands advertised by live help.
    • Epic or space: JQL-backed jira workitem query.
    • Hydrate key, title, status/category, assignee, updated time, parent, subtasks/blockers, and URL.

  2. Split the set.

    • Candidates are not done/resolved. Peers are done in the same board, sprint, epic, or space.
    • Exclude epics/parents. Skip unresolved blockers or incomplete subtasks unless the user explicitly allows them.

  3. Discover shared repo context set-wise.

    • Even for one item or a short list, infer context for the set together.
    • Extract repos/workspaces from linked PRs, commits, branches, project links, Compass/components, context queries, and resolved peers.
    • Use completed peers to infer common repos, assignees, Jira-key title/branch conventions, PR links, and merge-to-transition lag.

  4. Check optional discovery signals.

    • If rovo list-apps shows Bitbucket or GitHub connected, use Rovo search for keys, titles, PR URLs, branches, and repos. Treat it as discovery until hydrated through TWG/provider/Jira evidence.
    • If search-code is available, search exact keys first, then distinctive title terms in discovered repos. Strong hits are branch names, commit text, changed files, or symbols; broad fuzzy hits stay weak.

  5. Expand likely implementers.

    • Prefer assignees from completed peer items and candidates. Treat reporters and commenters as weak hints.
    • Query merged PRs authored by these assignees in the window, scoped by discovered repos first; if unknown, infer repos from author/date results.

  6. Search merged PRs in batches.

    • Prefer one merged-PR query per repo/window, then match locally across all candidate keys. One PR may satisfy multiple exact keys.
    • Match exact keys in PR title, description, branch, commits, and linked issue metadata.
    • Fall back to per-workitem lookup only for no evidence, priority items, or ambiguity.

  7. Score confidence.

    • High: exact key in merged PR or linked PR metadata, plausible timing, no open same-key PR, no blockers/subtasks, and target transition exists.
    • Medium: same assignee/repo with strong title similarity, or exact key only in commit/search-code evidence.
    • Low: fuzzy similarity, same author only, unknown repo, or ambiguity.

  8. Produce the dry-run plan.

    • Table columns: workitem key/title, current status, proposed transition, confidence, brief rationale, PR title/URL, merge date, last Jira update, link/repo proposal, PR title-fix proposal, skipped reason, and verification.
    • Include exact commands only after live-help verification. Never present low-confidence rows as executable.

Mutations

Only mutate after explicit approval of the exact rows or plan.

  • Transition: list transitions first, then use only the approved done/resolved target.
  • Link enrichment: if links are missing, offer jira workitem link weblink for PR/repo URLs after querying links and verifying help. Do not claim this creates native development-panel links.
  • PR title fix: for Bitbucket, update title only when twg bitbucket can read the PR, write access is verified, the PR is confidently tied to the item, and the user approved it. For GitHub, update only when an authenticated write tool is available and access is verified; otherwise skip with the reason.
  • Verification: re-read each changed workitem and PR. Report changed keys, statuses, URLs, and failed rows.

Do not transition medium-confidence items automatically. Do not transition declined/superseded PRs or rows supported only by same author, same repo, or fuzzy similarity.

Output

Lead with the decision summary:

  • executable high-confidence rows,
  • review-needed medium-confidence rows,
  • skipped unsafe rows,
  • missing repo/auth/permission coverage.

Then provide the dry-run table and exact approval question. Include URLs or stable IDs for every workitem and PR used as evidence.

Frequently asked questions

What does the Twg Jira Resolve Merged Work AI skill do?

Clean up stale or unresolved Jira workitems. Dry-run single items, lists, boards, sprints, epics, or projects by matching Jira keys/titles to merged PRs, repos, Rovo/search-code hits, and assignee activity.

Why use Twg Jira Resolve Merged Work on TypingMind?

Because you install it once and use it with any model. Twg Jira Resolve Merged Work is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Twg Jira Resolve Merged Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zenobi-us/dotfiles/tree/master/files/devtools/agent/bundles/developer/skills/atlassian/twg-jira-resolve-merged-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Twg Jira Resolve Merged Work?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Twg Jira Resolve Merged Work?

As many as you like. As long as a model supports skills, you can use Twg Jira Resolve Merged Work with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Twg Jira Resolve Merged Work AI skill free?

Yes. It is published on GitHub by zenobi-us under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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