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Ci Debugging

Community
citypaul
ci-debugging

Systematic CI/CD failure diagnosis using hypothesis-first investigation, local reproduction, and environment delta analysis. Use when a CI pipeline, GitHub Actions workflow, or build job fails; when tests pass locally but fail in CI; when diagnosing flaky tests, timeouts, or red pipelines; or when the user says "CI is failing", "the build is broken", or "works on my machine".

Overview

Publishercitypaul
Repository.dotfiles
Skill nameci-debugging
Stars
740
Forks
96
Bundled files
Instructions only
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 citypaul on GitHub. Read the source before you install it.

Installation

Install the Ci Debugging 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/citypaul/.dotfiles/tree/main/claude/.claude/skills/ci-debugging
  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/citypaul/.dotfiles.git /tmp/.dotfiles
mkdir -p .claude/skills
cp -r /tmp/.dotfiles/claude/.claude/skills/ci-debugging .claude/skills/ci-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ci Debugging 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 Ci Debugging 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 Ci Debugging 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.

CI Debugging

Every CI failure is real until proven otherwise. Never assume flakiness.

This skill owns pipeline-specific evidence and environment deltas. For a local or runtime failure outside CI, use debugging.

Getting the Data

Identify the CI provider from the repository configuration, then pull the actual failure output with that provider's connected tool, CLI, or UI before forming hypotheses. Preserve the run, job, attempt, commit SHA, and timestamps so later comparisons refer to the same execution.

For GitHub Actions:

bash
gh run list --branch <branch> --limit 5        # find the failing run
gh run view <run-id> --log-failed              # only the failed steps' logs
gh run view <run-id> --job <job-id> --log      # one job's full log
gh run download <run-id>                       # artifacts (coverage, reports, screenshots)

Rerunning a workflow or changing a CI variable mutates external state and may repeat deploys, migrations, notifications, or other effects. Do that only with explicit authority after inspecting the workflow and failed job for side effects. When authorized, rerun only the failed jobs with gh run rerun <run-id> --failed, or use gh run rerun <run-id> --debug for step-level logging; remove any temporary debug variable afterwards. For another provider, use its equivalent controls without translating commands by guesswork. Compare the failing run against the last green run on the same branch — the diff in commits, dependency lockfiles, and pipeline files between those two runs is the primary suspect list.

If the system under test emits structured telemetry (canonical events, traces, in-memory exporter output from test runs), pull it as evidence alongside the logs — see the observability skill.

Hypothesis-First Diagnosis

Before editing, list plausible causes, rank them by evidence, and test one falsifiable hypothesis at a time. Do not invent a fixed minimum merely to fill a list.

Example hypotheses for a test timeout:

  1. Test relies on network access unavailable in CI
  2. Parallel test execution causes resource contention
  3. CI runner has less memory/CPU than local machine

Local Reproduction

Reproduce locally when a faithful environment is available before pushing fixes.

  • Run the exact failing command, not a close equivalent
  • Match the CI environment as closely as possible (Node version, env vars)
  • If it passes locally, treat the environment delta as evidence to localize; the cause may still be timing, state, or an external dependency

Environment Delta Analysis

Compare CI vs local:

FactorCheck
Node/runtime versionCI config vs node -v locally
OSLinux CI vs macOS local
Dependency resolutionRepository's locked clean-install command and package cache (for npm, npm ci vs cached node_modules)
Env varsCI secrets/config vs local .env
ParallelismCI may run tests in parallel differently
Memory/CPUCI runners often have less resources
NetworkCI may block external network access
File systemCase sensitivity (Linux) vs insensitive (macOS)

Read the Full Error

  • Read the complete error output, not just the last line
  • Check preceding log lines and warnings — they often contain the real cause
  • Look at stack traces to identify the actual failure point
  • Check for earlier failures that may cascade into the visible error

Fix Verification

If the request is diagnosis-only, report the supported root cause, evidence, affected jobs, uncertainty, and recommended fix, then stop. Diagnosis does not authorize workflow, production, dependency, test, or configuration changes.

When the user requested a fix, after identifying it:

  1. Explain why it addresses the root cause (not just the symptom)
  2. Run the exact failing command locally
  3. Verify the fix doesn't mask the real issue (e.g., adding a retry hides a race condition)

Anti-Patterns

Anti-PatternWhy It's WrongInstead
"It's flaky, re-run it"Masks real issuesInvestigate the failure
Adding blind retries/sleepsCan hide timing bugsIdentify the cause; add bounded retry only when the external contract requires it
Pushing speculative fixesWastes CI cyclesReproduce and verify locally
Reading only the last error lineMisses root causeRead full output from the top
Fixing symptomsProblem will recurTrace to root cause

Proving Flakiness

Different outcomes across independent runs with the same controlled inputs are evidence that the outcome is nondeterministic. That proves flakiness even when the source is not yet diagnosed. Record which inputs and environment facts were actually controlled, then localize the cause: race, time, shared state, resource pressure, or an external dependency. A single unexplained failure remains a real failure, not permission to re-run until green.

Handoff

Once the root cause is identified, a diagnosis-only handoff ends with the evidence and recommended guard. For an authorized fix, leave the smallest durable regression guard that fits it. For production behavior, write the failing behavior test before the fix using tdd (or characterisation-tests when legacy behavior first needs capture). For provider configuration, permissions, or an external outage, use a config assertion, dry-run, contract check, or monitored recovery proof as appropriate. If no executable guard is feasible, record the evidence and remaining detection path instead of fabricating a test.

Frequently asked questions

What does the Ci Debugging AI skill do?

Systematic CI/CD failure diagnosis using hypothesis-first investigation, local reproduction, and environment delta analysis. Use when a CI pipeline, GitHub Actions workflow, or build job fails; when tests pass locally but fail in CI; when diagnosing flaky tests, timeouts, or red pipelines; or when the user says "CI is failing", "the build is broken", or "works on my machine".

Why use Ci Debugging on TypingMind?

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

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

Which AI models can use Ci Debugging?

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 Ci Debugging?

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

Is the Ci Debugging AI skill free?

It is published on GitHub by citypaul. Check the repository for licensing terms. 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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