Fetching Circleci Logs logo

Fetching Circleci Logs

Community
NTCoding
fetching-circleci-logs

Fetches CircleCI job logs via the v1.1 API and displays step-level output. Focuses on failed steps. Use when: CI checks fail on a PR, user shares a CircleCI job URL, user asks to check build logs, 'circleci', 'build failed', 'CI failed', 'check the logs'.

Overview

PublisherNTCoding
Repositoryclaude-skillz
Skill namefetching-circleci-logs
Stars
356
Forks
47
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 NTCoding on GitHub. Read the source before you install it.

Installation

Install the Fetching Circleci Logs 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/NTCoding/claude-skillz/tree/main/fetching-circleci-logs
  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/NTCoding/claude-skillz.git /tmp/claude-skillz
mkdir -p .claude/skills
cp -r /tmp/claude-skillz/fetching-circleci-logs .claude/skills/fetching-circleci-logs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fetching Circleci Logs 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 Fetching Circleci Logs 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 Fetching Circleci Logs 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.

Fetching CircleCI Logs

Fetch step-level build logs from CircleCI jobs using the v1.1 API.

When to use

  • PR checks failed and you need to see why
  • User shares a CircleCI URL (e.g. https://circleci.com/gh/org/repo/169955)
  • User asks to investigate a CI failure

Extract job details from URL

CircleCI URLs follow this pattern:

https://circleci.com/gh/{org}/{repo}/{job_number}

Extract org, repo, and job_number from the URL. If the user provides only a job number, ask for the org/repo or check the current git remote:

bash
git remote get-url origin | sed -E 's|.*github\.com[:/]([^/]+)/([^/.]+).*|\1/\2|'

Fetch logs

Step 1: Get the CircleCI token

bash
grep 'token' ~/.circleci/cli.yml | awk '{print $2}'

If the file doesn't exist or has no token, tell the user to run circleci setup or set the token manually.

Step 2: Fetch job data and display step output

Run this as a single command, substituting {org}, {repo}, and {job_number}:

bash
TOKEN=$(grep 'token' ~/.circleci/cli.yml | awk '{print $2}') && \
curl -s -H "Circle-Token: $TOKEN" \
  "https://circleci.com/api/v1.1/project/gh/{org}/{repo}/{job_number}" | \
python3 -c "
import sys, json, urllib.request
data = json.load(sys.stdin)
for step in data.get('steps', []):
    for action in step.get('actions', []):
        status = action.get('status', '')
        name = action.get('name', '')
        exit_code = action.get('exit_code')
        failed = action.get('failed', False)
        print(f'STEP: {name} | status: {status} | exit: {exit_code}')
        if failed and action.get('output_url'):
            with urllib.request.urlopen(action['output_url']) as r:
                for msg in json.load(r):
                    print(msg.get('message', '')[:2000])
"

Step 3: Analyze the output

After fetching, summarize:

  1. Which steps failed and why
  2. The relevant error messages (not the full log)
  3. Suggested fix if the cause is clear

Important notes

  • Use API v1.1 — v2 does not return step-level output with log URLs.
  • The output_url is a pre-signed S3 URL — no additional auth needed to fetch it.
  • Truncate log messages to 2000 chars per action to avoid context window bloat.
  • If the API returns 404, the job number is likely wrong or the token lacks access to that project.

Mandatory Checklist

When fetching CircleCI logs, complete this checklist:

  1. Verify the CircleCI token exists in ~/.circleci/cli.yml
  2. Verify org, repo, and job number are correctly extracted from the URL or git remote
  3. Verify you are using the v1.1 API (not v2)
  4. Verify you summarized the failure cause, not just dumped raw logs

Do not proceed to suggest fixes until all checks pass.

Frequently asked questions

What does the Fetching Circleci Logs AI skill do?

Fetches CircleCI job logs via the v1.1 API and displays step-level output. Focuses on failed steps. Use when: CI checks fail on a PR, user shares a CircleCI job URL, user asks to check build logs, 'circleci', 'build failed', 'CI failed', 'check the logs'.

Why use Fetching Circleci Logs on TypingMind?

Because you install it once and use it with any model. Fetching Circleci Logs 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 Fetching Circleci Logs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NTCoding/claude-skillz/tree/main/fetching-circleci-logs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fetching Circleci Logs?

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 Fetching Circleci Logs?

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

Is the Fetching Circleci Logs AI skill free?

It is published on GitHub by NTCoding. 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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