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Issue Pipeline

Community
fagemx
issue-pipeline

End-to-end issue pipeline: plan → implement → review → merge. Dispatches parallel sub-agents with worktree isolation. Usage: /issue-pipeline 33 34 35 [--skip-plan] [--skip-review] [--no-merge]

Overview

Publisherfagemx
Repositorygstack-game
Skill nameissue-pipeline
Stars
81
Forks
10
Bundled files
Instructions only
LicenseMIT
Links
  • 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 fagemx on GitHub. Read the source before you install it.

Installation

Install the Issue Pipeline 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.
    https://github.com/fagemx/gstack-game/tree/main/.claude/skills/issue-pipeline
  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/fagemx/gstack-game.git /tmp/gstack-game
mkdir -p .claude/skills
cp -r /tmp/gstack-game/.claude/skills/issue-pipeline .claude/skills/issue-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Issue Pipeline 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 Issue Pipeline 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 Issue Pipeline 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.

/issue-pipeline: End-to-End Issue Pipeline

You orchestrate the full lifecycle of GitHub issues through parallel sub-agents. Each phase runs all issues concurrently in isolated worktrees.

Arguments

Parse $ARGUMENTS to get issue numbers and flags.

Examples:

  • /issue-pipeline 33 34 35 — full pipeline (plan → implement → review → merge)
  • /issue-pipeline 33 34 --skip-plan — issues already have plans, skip to implement
  • /issue-pipeline 33 34 --skip-review --no-merge — implement only, no review or merge
  • /issue-pipeline 33 — single issue, full pipeline

If no issue numbers provided, ask: "Which issues? Provide issue numbers separated by spaces."

Flags:

  • --skip-plan — Skip plan phase (issues already have planned label + plan comments)
  • --skip-review — Skip review phase, merge after implement
  • --no-merge — Stop after review, don't auto-merge
  • --dry-run — Show what would happen without executing

Pre-Flight Check

Before starting, verify all issues exist and check their state:

bash
for num in {issue_numbers}; do
  gh issue view $num --json number,title,labels,state --jq '"\(.number) [\(.labels | map(.name) | join(","))] \(.state) — \(.title)"'
done

Report findings:

  • Issues with planned label → can skip plan phase
  • Closed issues → skip entirely with warning
  • Issues without planned label → need plan phase

Pipeline Phases

Phase 1: Plan (parallel)

Skip if: --skip-plan flag OR issue already has planned label.

For each issue needing a plan, launch a sub-agent:

Agent tool parameters:
  description: "Plan issue #{number}"
  isolation: "worktree"
  run_in_background: true
  prompt: |
    You are working on GitHub issue #{number} for gstack-game at {repo_root}.

    Execute the /issue-plan workflow:
    1. Read the skill at {repo_root}/.claude/skills/issue-plan/SKILL.md
    2. Follow its instructions for issue #{number}

    This means: Research → Innovate → Plan, posting all 3 phases to the issue.

    IMPORTANT:
    - You are in a worktree — do NOT modify source code, planning only
    - Post all findings to the GitHub issue as comments
    - Add the "planned" label when done

Wait for ALL plan agents to complete. Report status table.

Phase 2: Implement (parallel)

For each issue with a plan, launch a sub-agent:

Agent tool parameters:
  description: "Implement issue #{number}"
  isolation: "worktree"
  run_in_background: true
  prompt: |
    You are implementing GitHub issue #{number} for gstack-game at {repo_root}.

    Execute the /issue-action workflow:
    1. Read the plan from the issue comments: `gh issue view {number} --json comments`
    2. Also check for local artifacts: `.tmp/deep-dive/issue-{number}/plan.md`
    3. Read the skill at {repo_root}/.claude/skills/issue-action/SKILL.md
    4. Follow its instructions for issue #{number}

    IMPORTANT:
    - You are in a worktree — create a feature branch, make changes, commit
    - Edit .tmpl files, never generated .md directly
    - Run `bun run build` after template changes
    - Run `bun test` — all tests must pass before creating PR
    - Create a PR with `gh pr create` — include "Closes #{number}" in the body
    - Do NOT merge the PR

Wait for ALL implement agents to complete. Collect PR URLs/numbers. Report status table.

Phase 3: Review (parallel)

Skip if: --skip-review flag.

For each PR created in Phase 2, launch a sub-agent:

Agent tool parameters:
  description: "Review PR #{pr_number}"
  isolation: "worktree"
  run_in_background: true
  prompt: |
    You are reviewing PR #{pr_number} for gstack-game at {repo_root}.

    Execute the /pr-review-loop workflow:
    1. Read the skill at {repo_root}/.claude/skills/pr-review-loop/SKILL.md
    2. Follow its instructions for PR #{pr_number}

    IMPORTANT:
    - You are in a worktree
    - If fixes are needed, check out the PR branch, fix, commit, and push
    - The review loop continues until LGTM or max 3 iterations

Wait for ALL review agents to complete. Report verdicts.

Phase 4: Merge

Skip if: --no-merge flag.

For each PR with LGTM verdict:

bash
gh pr merge {pr_number} --merge --delete-branch

For PRs without LGTM: report them as needing manual review.

After all merges, sync main:

bash
git checkout main && git pull

Report final status table.

Execution Rules

  1. All sub-agents use isolation: "worktree" — never work on main directly
  2. All sub-agents use run_in_background: true — maximize parallelism
  3. Launch ALL agents for a phase in a SINGLE message (one Agent tool call per issue, all in parallel)
  4. Wait for ALL agents in a phase before starting the next phase
  5. Report a status table after each phase — issue number, status, key details
  6. If any agent fails, report it but continue with the rest — don't block the pipeline
  7. Between phases, sync main — git checkout main && git pull to pick up merged changes

gstack-game Specific Rules

  • Template workflow: Agents edit .tmpl files, run bun run build to regenerate .md, run bun test (14 tests)
  • Preamble changes: If an issue changes skills/shared/preamble-*.md, ALL 28 SKILL.md files are regenerated
  • Commit style: Conventional Commits (feat: / fix: / test: / docs:)
  • PR merges use --merge (not squash) to preserve commit history

Status Table Format

After each phase:

| Issue | Phase     | Status | Details |
|-------|-----------|--------|---------|
| #33   | plan      | ✅     | Plan posted to issue |
| #34   | plan      | ⏭️     | Already planned |
| #35   | implement | ✅     | PR #38 created |
| #36   | implement | ❌     | bun test failed |

After merge:

| Issue | PR  | Plan | Implement | Review | Merge | Result |
|-------|-----|------|-----------|--------|-------|--------|
| #33   | #38 | ✅   | ✅        | ✅     | ✅    | DONE   |
| #34   | #39 | ⏭️   | ✅        | ✅     | ✅    | DONE   |
| #35   | #40 | ✅   | ✅        | ⚠️     | ❌    | NEEDS REVIEW |

Error Handling

  • Agent fails: Mark as ❌, continue with other issues. Report at end.
  • PR creation fails: Try to identify branch, suggest manual fix.
  • Review finds issues: pr-review-loop handles fix + re-review automatically (max 3 rounds).
  • Merge conflict: Report conflict, skip merge for that PR, suggest manual resolution.
  • bun test fails: Agent should NOT create PR. Report test failures.

Anti-Sycophancy

  • ❌ "All issues completed successfully!" (unless they actually all did)
  • ❌ "Easy batch!" (some issues may have unexpected complexity)
  • ✅ Report exact counts: "4/5 issues completed. #36 failed at implement phase: bun test returned 2 failures."
  • ✅ If all agents fail, say so plainly: "0/3 issues completed. All agents encountered errors."

Frequently asked questions

What does the Issue Pipeline AI skill do?

End-to-end issue pipeline: plan → implement → review → merge. Dispatches parallel sub-agents with worktree isolation. Usage: /issue-pipeline 33 34 35 [--skip-plan] [--skip-review] [--no-merge]

Why use Issue Pipeline on TypingMind?

Because you install it once and use it with any model. Issue Pipeline 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 Issue Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fagemx/gstack-game/tree/main/.claude/skills/issue-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Issue Pipeline?

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 Issue Pipeline?

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

Is the Issue Pipeline AI skill free?

Yes. It is published on GitHub by fagemx 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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