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Parallel Ci Triage

Community
spencerpauly
parallel-ci-triage

When GitHub Actions fails, fetch failing job logs and assign each failing job to a separate subagent that fixes its slice of the problem in parallel. Use for multi-job CI failures where jobs are independent.

Overview

Publisherspencerpauly
Repositoryawesome-cursor-skills
Skill nameparallel-ci-triage
Stars
848
Forks
169
Bundled files
Instructions only
LicenseCC0-1.0
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 spencerpauly on GitHub. Read the source before you install it.

Installation

Install the Parallel Ci Triage 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/spencerpauly/awesome-cursor-skills/tree/main/resources/parallel-ci-triage
  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/spencerpauly/awesome-cursor-skills.git /tmp/awesome-cursor-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-cursor-skills/resources/parallel-ci-triage .claude/skills/parallel-ci-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Ci Triage 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 Parallel Ci Triage 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 Parallel Ci Triage 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.

Parallel CI Triage

Speed up fixing broken CI by splitting failing jobs (or independent failure clusters) across parallel subagents. Each subagent owns one vertical slice: logs, root cause, code fix, and local verification for that slice.

Prerequisites

  • GitHub CLI (gh) installed and authenticated (gh auth login), or use the GitHub web UI / API to copy logs manually.
  • Push access to the repo so fixes can be pushed and CI re-run.

Workflow

1. Identify the failing run

From the repo root:

bash
gh run list --limit 5
gh run view <RUN_ID> --log-failed

Or open the Actions tab, open the failed workflow run, and note which jobs failed (not just which step — group by job name).

If gh is unavailable, download logs from the GitHub UI and paste them into the conversation.

2. Split by job (or by failure cluster)

  • One subagent per failed job when jobs test different things (e.g. lint, test-node-18, e2e).
  • One subagent per independent failure cluster when a single job logs multiple unrelated errors — but prefer one job per agent to avoid conflicting edits in the same files.

If two failures share the same root cause in the same file, assign one subagent to fix both.

3. Launch parallel subagents

For each failing job, launch a generalPurpose subagent in a single message so they run concurrently:

Task: Fix CI failure for job "<JOB_NAME>"

Context:
- Workflow run: <RUN_URL or RUN_ID>
- Branch: <branch>
- Relevant log excerpt (failed steps only):
<paste gh run view --job <JOB_ID> --log or the failed section>

Instructions:
1. Infer the root cause from the log (command, stack trace, file:line).
2. Open and edit only what this job requires.
3. Run the same commands locally that failed in CI (or the narrowest equivalent, e.g. `npm run lint`, `pytest tests/foo`, `pnpm test --filter pkg`).
4. Report: what failed, what you changed, and confirmation that the local command passes.

Include the exact failing command and error lines so the subagent does not guess.

4. Merge and verify

  • Collect each subagent’s changed files. Resolve overlaps manually if two agents touched the same file.

  • Run the full CI-equivalent locally when possible:

    bash
    # Example: match your repo
    npm run lint && npm test
  • Commit with a conventional message, push, and re-check the workflow:

    bash
    gh run watch

When to use

  • Multiple GitHub Actions jobs failed and the failures look independent.
  • A long workflow log is easier to split by job than to fix sequentially.

When not to use

  • A single job with one clear error — fix it in the main agent.
  • Failures that are purely flaky infrastructure — retry or fix workflow config first.

Notes

  • Redact secrets if pasting logs into chat.
  • If agents conflict on shared files, merge sequentially after the parallel pass.

Frequently asked questions

What does the Parallel Ci Triage AI skill do?

When GitHub Actions fails, fetch failing job logs and assign each failing job to a separate subagent that fixes its slice of the problem in parallel. Use for multi-job CI failures where jobs are independent.

Why use Parallel Ci Triage on TypingMind?

Because you install it once and use it with any model. Parallel Ci Triage 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 Parallel Ci Triage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/spencerpauly/awesome-cursor-skills/tree/main/resources/parallel-ci-triage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Parallel Ci Triage?

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 Parallel Ci Triage?

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

Is the Parallel Ci Triage AI skill free?

Yes. It is published on GitHub by spencerpauly under the CC0-1.0 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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