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Oss Debug Ci

Community
chiruu12
oss-debug-ci

Debug CI failures in unfamiliar repo pipelines. Reads CI configs, fetches failure logs, classifies the failure type, and guides the user through diagnosing and fixing the issue. Use when CI fails on your PR in an OSS repo, when you don't understand a CI error, or when debugging GitHub Actions/CircleCI/other CI systems. Not for setting up CI from scratch — this is for debugging existing pipelines.

Overview

Publisherchiruu12
RepositoryOSS-Skills
Skill nameoss-debug-ci
Stars
62
Forks
6
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 chiruu12 on GitHub. Read the source before you install it.

Installation

Install the Oss Debug Ci 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/chiruu12/OSS-Skills/tree/main/skills/oss-debug-ci
  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/chiruu12/OSS-Skills.git /tmp/OSS-Skills
mkdir -p .claude/skills
cp -r /tmp/OSS-Skills/skills/oss-debug-ci .claude/skills/oss-debug-ci
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oss Debug Ci 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 Oss Debug Ci 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 Oss Debug Ci 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.

Debug CI

CI failed on someone else's repo and the error makes no sense. This skill teaches you to read CI pipelines, classify failures, and fix them — instead of pushing blind patches and hoping.

Purpose

CI failures in unfamiliar repos are disorienting. The error might come from a linter you've never configured, a test runner with custom settings, or a platform-specific incompatibility. Most contributors respond by pushing random fixes until green. This skill teaches you to read the pipeline, understand what failed and why, determine if it's your fault, and fix it systematically.

When to Use

  • CI fails on your PR and you don't understand the error
  • CI failure is in a tool/system you haven't used before
  • You suspect the failure is pre-existing (flaky test, broken main)
  • You need to reproduce a CI failure locally
  • NOT when setting up CI for a new project — that's infrastructure work
  • NOT when CI passes but the code is wrong — that's oss-contribute

Prerequisites

  • A PR submitted to an OSS repo (from oss-submit-pr)
  • gh CLI authenticated
  • Access to the CI logs (public repo or you have permissions)

Process

1. Identify the CI system and read the config

Before looking at logs, understand the pipeline structure.

bash
# Find CI configuration files
ls -la .github/workflows/ 2>/dev/null
ls -la .circleci/ 2>/dev/null
ls -la .travis.yml Jenkinsfile .gitlab-ci.yml .buildkite/ azure-pipelines.yml 2>/dev/null

# Read the relevant workflow
cat .github/workflows/ci.yml  # or whatever the CI config is

Map the pipeline:

  • What jobs exist? (build, test, lint, typecheck, etc.)
  • What order do they run in? (parallel or sequential?)
  • What OS/container do they run on? (ubuntu, macos, windows, custom image?)
  • What language/tool versions are pinned? (node 20, python 3.12, go 1.22?)
  • What caching is used? (dependency caching, build caching?)

2. Fetch the failure logs

Your PR's CI runs on the upstream repo, not on your fork. Name the repo on every command. Without -R, gh guesses from your remotes, and in a fork clone it can guess your fork, which has no runs for this PR.

bash
# Start here. This maps each check to the run and job that produced it.
gh pr checks {pr-number} -R {owner}/{repo}

# Or list runs by head branch. Your fork's branch name is the head branch upstream.
gh run list -R {owner}/{repo} --branch {your-branch} --limit 5

# View the failed run
gh run view {run-id} -R {owner}/{repo}

# Get the specific failure logs
gh run view {run-id} -R {owner}/{repo} --log-failed

# If you need the full log for a specific job
gh run view {run-id} -R {owner}/{repo} --log --job {job-id}

Isolate the actual error from surrounding noise. CI logs are verbose — most of the output is setup and teardown. The failure is usually in the last 20-50 lines of a failed step.

3. Classify the failure

Determine what kind of failure you're dealing with. This determines your fix strategy.

Failure TypeSignalsFix Strategy
Build errorCompilation failed, import not found, type errorYour code has a syntax or type issue. Fix locally.
Test failureAssertion failed, expected X got YYour changes broke a test. Read the test to understand what it expects.
Lint/format failureStyle violation, unused import, formatting diffRun the repo's formatter/linter locally. Often auto-fixable.
Dependency resolutionPackage not found, version conflict, lockfile mismatchYou changed deps or the lockfile is stale. Regenerate it.
Environment mismatchWorks locally, fails in CIDifferent OS, different tool version, missing env var.
Flaky testTest passes sometimes, fails sometimes. Same code.Not your fault. Check if this test fails on main too.
Pre-existing failureMain branch has the same failureNot your fault. Comment on PR noting pre-existing failure.
TimeoutJob exceeded time limitYour changes might be slow, or CI is under load. Check if it's reproducible.
Permission/secret issueAuth failed, secret not foundFork PRs can't access repo secrets. Check if the job needs secrets.

4. Determine if the failure is yours

This is the critical question. Don't fix what isn't broken.

bash
# Check if the same job fails on main
gh run list -R {owner}/{repo} --branch main --limit 5 --workflow {workflow-name}
gh run view {latest-main-run-id} -R {owner}/{repo} --log-failed

# Check if this test is known to be flaky
gh issue list -R {owner}/{repo} --search "flaky {test-name}" --state open
gh issue list -R {owner}/{repo} --label "flaky" --state open

# Try rebasing onto latest main — maybe the failure was already fixed
git fetch origin main
git rebase origin/main

If the failure exists on main: it's not your problem. Comment on your PR:

CI failure in `{job-name}` appears to be pre-existing — the same test fails on main ([run link]). Happy to help fix separately if useful.

5. Reproduce locally

If the failure IS from your changes, reproduce it before fixing blindly.

bash
# Run the exact command CI runs (read it from the CI config)
# Don't guess — copy the exact command from the workflow file

# Common examples:
npm run test          # not just "npm test" if the script is different
pytest -x             # might need specific flags
make lint             # might include multiple linters
cargo clippy -- -D warnings  # might treat warnings as errors

If it passes locally but fails in CI:

  • Check tool versions: node --version, python --version, etc. vs CI config
  • Check for OS-specific behavior (path separators, line endings, case sensitivity)
  • Check for missing environment variables (CI might set CI=true which changes behavior)
  • Check for timing issues (CI machines are slower, timeouts may differ)

6. Thinking gate — user explains the failure

"Now that you've seen the logs and classified the failure:

  1. What type of failure is this? (Use the classification table above)
  2. Is it caused by your changes or pre-existing?
  3. What do you think the fix is? (Be specific — what file, what change)"

Wait for their answer. If they say "I don't know," point them to the specific log line and CI config step that failed. Don't hand them the answer.

7. Fix and verify

The user fixes the issue based on their diagnosis. The LLM helps with:

  • Explaining unfamiliar CI tools or configurations
  • Finding the right commands to reproduce locally
  • Pointing to CI documentation when needed
bash
# After fixing, run the failing command locally
# {exact command from CI config}

# Push the fix
git add {changed-files}
git commit -m "{descriptive message about the CI fix}"
git push

8. Monitor the re-run

bash
# Watch the new CI run
gh run list -R {owner}/{repo} --branch {your-branch} --limit 1
gh run watch {new-run-id} -R {owner}/{repo}

If it fails again with a DIFFERENT error: go back to step 2. CI pipelines are sequential — fixing one failure may reveal the next.

If it fails with the SAME error: your fix didn't work. Re-read the logs more carefully. The error message might be misleading — look at the lines above and below it for context.

Related Skills

  • Previous step: ← oss-submit-pr — CI runs after you submit
  • Previous step: ← oss-post-pr — reviewer may ask you to fix CI
  • If code changes needed: → oss-contribute — if the fix requires significant code changes
  • Return to: → oss-post-pr — after CI is green, continue with review

Common Rationalizations

ShortcutWhy It Fails
"I'll just push a fix and see if CI goes green"Blind pushing wastes CI minutes (a shared resource), clutters your commit history, and tells maintainers you don't understand the codebase. Diagnose first.
"It passes locally so CI must be wrong"CI is usually right. It uses pinned versions, clean state, and different OS. If it fails in CI and passes locally, YOU have the wrong environment.
"I'll just skip this CI check"You can't skip CI on someone else's repo. Even if you could, the maintainer will notice and reject your PR.
"I'll ask the maintainer to re-run CI, it's probably flaky"Check if it's flaky first (step 4). Asking maintainers to re-run without evidence wastes their time and makes you look like you didn't try.
"I'll copy the fix from another PR that had the same error"Same error message doesn't mean same root cause. Understand YOUR failure before applying someone else's fix.

Red Flags

  • User pushes 3+ "fix CI" commits without reading the logs — they're guessing, not debugging
  • Failure is in a job unrelated to the user's changes (e.g., docs build fails on a code PR) — likely pre-existing
  • User wants to modify CI config files — that's almost never the right fix for a contributor PR
  • Same failure type keeps recurring across multiple pushes — the diagnosis is wrong

Verification Checklist

  • CI system and pipeline structure understood (step 1)
  • Failure classified by type (step 3)
  • Determined whether failure is from user's changes or pre-existing (step 4)
  • Failure reproduced locally (step 5, if applicable)
  • User explained the failure type, cause, and fix (step 6)
  • Fix pushed and CI passes (step 7-8)
  • No unnecessary "fix CI" commits cluttering the history

Anti-patterns

  • DO NOT push blind fixes without reading the logs — diagnose first, fix second
  • DO NOT modify CI configuration files in a contributor PR — fix your code, not the pipeline
  • DO NOT assume "works locally" means CI is wrong — CI has the authoritative environment
  • DO NOT ask maintainers to re-run without checking if the failure is pre-existing first
  • DO NOT let the user skip classification — knowing the failure TYPE determines the fix strategy

Frequently asked questions

What does the Oss Debug Ci AI skill do?

Debug CI failures in unfamiliar repo pipelines. Reads CI configs, fetches failure logs, classifies the failure type, and guides the user through diagnosing and fixing the issue. Use when CI fails on your PR in an OSS repo, when you don't understand a CI error, or when debugging GitHub Actions/CircleCI/other CI systems. Not for setting up CI from scratch — this is for debugging existing pipelines.

Why use Oss Debug Ci on TypingMind?

Because you install it once and use it with any model. Oss Debug Ci 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 Oss Debug Ci in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chiruu12/OSS-Skills/tree/main/skills/oss-debug-ci. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Oss Debug Ci?

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 Oss Debug Ci?

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

Is the Oss Debug Ci AI skill free?

Yes. It is published on GitHub by chiruu12 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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