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Analyze Ci Config

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dykyi-roman
analyze-ci-config

Analyzes existing CI/CD configurations. Detects issues in GitHub Actions and GitLab CI files, checks for best practices, caching efficiency, and security concerns.

Overview

Publisherdykyi-roman
Repositoryawesome-claude-code
Skill nameanalyze-ci-config
Stars
98
Forks
25
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 dykyi-roman on GitHub. Read the source before you install it.

Installation

Install the Analyze Ci Config 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.
  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/dykyi-roman/awesome-claude-code.git /tmp/awesome-claude-code
mkdir -p .claude/skills
cp -r /tmp/awesome-claude-code/skills/analyze-ci-config .claude/skills/analyze-ci-config
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyze Ci Config 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 Analyze Ci Config 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 Analyze Ci Config 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 Configuration Analyzer

Analyzes CI/CD configurations for issues, optimizations, and best practices.

Analysis Categories

1. Structure Analysis

┌─────────────────────────────────────────────────────────────────┐
│                    CI CONFIG ANALYSIS                           │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ✓ Stages defined: install → lint → test → build → deploy      │
│  ✓ Jobs properly ordered                                        │
│  ✗ Missing concurrency control                                  │
│  ✗ No timeout configuration                                     │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

2. Caching Analysis

IssueSeverityLocationRecommendation
No Composer cache🟠 Majorlint jobAdd actions/cache for ~/.composer/cache
Invalid cache key🟡 MinorLine 23Use hashFiles('composer.lock')
Missing vendor cache🟠 MajorAll jobsShare vendor between jobs with artifacts

3. Security Analysis

IssueSeverityLocationRisk
pull_request_target misuse🔴 CriticalLine 5Code injection from forks
Secrets in logs🔴 CriticalLine 45echo ${{ secrets.API_KEY }} exposed
Outdated actions🟠 MajorLines 12, 18Using @v1 instead of @v4
No permissions defined🟡 Minor-Uses default (write-all)

GitHub Actions Analysis

Checklist

markdown
## GitHub Actions Analysis Report

### Configuration: `.github/workflows/ci.yml`

#### Structure ✓
- [x] Valid YAML syntax
- [x] Proper job dependencies (needs)
- [ ] Concurrency configuration
- [ ] Timeout defined for jobs
- [x] Workflow triggers appropriate

#### Caching ⚠️
- [ ] Composer dependencies cached
- [ ] Node modules cached (if applicable)
- [x] Docker layer caching
- [ ] Cache keys use file hashes

#### Security 🔴
- [ ] Permissions explicitly defined
- [ ] No secrets echoed
- [x] Actions pinned to SHA
- [ ] pull_request_target safe usage

#### Performance ⚠️
- [ ] Jobs run in parallel where possible
- [x] Matrix strategy for PHP versions
- [ ] Fail-fast disabled for matrix
- [ ] Artifacts shared between jobs

#### Best Practices ✓
- [x] Uses specific action versions
- [x] Environment variables centralized
- [ ] Reusable workflows
- [x] Clear job names

Common Issues

1. Missing Concurrency
yaml
# ❌ BAD: No concurrency control
name: CI
on: [push, pull_request]

# ✅ GOOD: Cancel redundant runs
name: CI
on: [push, pull_request]

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true
2. Inefficient Caching
yaml
# ❌ BAD: Cache key doesn't include lock file
- uses: actions/cache@v4
  with:
    path: vendor
    key: vendor-${{ github.sha }}

# ✅ GOOD: Cache key based on lock file
- uses: actions/cache@v4
  with:
    path: |
      ~/.composer/cache
      vendor
    key: composer-${{ hashFiles('composer.lock') }}
    restore-keys: composer-
3. Security Issues
yaml
# ❌ BAD: Dangerous with forks
on:
  pull_request_target:
    types: [opened]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          ref: ${{ github.event.pull_request.head.sha }}  # Runs untrusted code

# ✅ GOOD: Separate trusted/untrusted
on:
  pull_request:  # Safe: runs in context of base

GitLab CI Analysis

Checklist

markdown
## GitLab CI Analysis Report

### Configuration: `.gitlab-ci.yml`

#### Structure ✓
- [x] Valid YAML syntax
- [x] Stages defined
- [x] Jobs assigned to stages
- [ ] Global variables defined
- [x] Default image set

#### Caching ⚠️
- [ ] Cache key uses files hash
- [ ] Cache policy appropriate (pull/push)
- [x] Cache paths correct
- [ ] Artifacts used for job sharing

#### Security ⚠️
- [x] Secrets in CI/CD variables (not code)
- [ ] Protected branches configured
- [ ] No sensitive data in artifacts
- [x] Image from trusted registry

#### Performance ⚠️
- [ ] Jobs run in parallel
- [x] Needs keyword for dependencies
- [ ] Rules/only properly configured
- [ ] DAG mode enabled

#### Best Practices ✓
- [x] Uses extends for reuse
- [x] Clear job names
- [ ] Include for modular config
- [x] Appropriate timeouts

Common Issues

1. Cache Key Without Hash
yaml
# ❌ BAD: Cache never invalidates properly
cache:
  key: composer-cache
  paths:
    - vendor/

# ✅ GOOD: Cache invalidates on lock change
cache:
  key:
    files:
      - composer.lock
  paths:
    - vendor/
2. Missing Needs
yaml
# ❌ BAD: Sequential stages, no parallelism
stages:
  - lint
  - test

phpstan:
  stage: lint
  script: vendor/bin/phpstan

phpunit:
  stage: test  # Waits for ALL lint jobs

# ✅ GOOD: DAG with needs
phpunit:
  stage: test
  needs: [composer-install]  # Only waits for install

Analysis Output Format

markdown
# CI/CD Configuration Analysis

**File:** `.github/workflows/ci.yml`
**Platform:** GitHub Actions
**Date:** 2024-01-15

## Summary

| Category | Status | Issues |
|----------|--------|--------|
| Structure | ✅ Good | 0 |
| Caching | ⚠️ Warning | 3 |
| Security | 🔴 Critical | 2 |
| Performance | ⚠️ Warning | 4 |
| Best Practices | ✅ Good | 1 |

**Total Issues:** 10 (2 Critical, 4 Major, 4 Minor)

## Critical Issues

### SEC-001: Exposed Secret in Logs
**Location:** Line 45
**Code:**
```yaml
- run: echo "Deploying with ${{ secrets.DEPLOY_KEY }}"

Risk: Secret visible in workflow logs Fix:

yaml
- run: echo "Deploying..."
  env:
    DEPLOY_KEY: ${{ secrets.DEPLOY_KEY }}

SEC-002: pull_request_target with Checkout

Location: Lines 3, 15 Risk: Arbitrary code execution from forks Fix: Use pull_request event instead, or don't checkout PR code

Major Issues

CACHE-001: Missing Composer Cache

Location: lint job Impact: +2-3 minutes per run Fix:

yaml
- uses: actions/cache@v4
  with:
    path: ~/.composer/cache
    key: composer-${{ hashFiles('composer.lock') }}

PERF-001: Sequential Jobs Could Run Parallel

Location: test-unit, test-integration Impact: +5 minutes total Fix: Remove needs dependency between test jobs

Minor Issues

BP-001: Using Outdated Action Version

Location: Line 12 Current: actions/checkout@v2 Recommended: actions/checkout@v4

Recommendations

  1. Immediate: Fix security issues SEC-001 and SEC-002
  2. Short-term: Implement caching improvements
  3. Long-term: Restructure for parallel execution

Optimized Configuration

See Appendix A for complete optimized configuration.


## Analysis Instructions

1. **Parse configuration:**
   - Validate YAML syntax
   - Identify platform (GitHub/GitLab)
   - Extract jobs, stages, triggers

2. **Check structure:**
   - Proper job ordering
   - Dependencies (needs/stages)
   - Concurrency settings
   - Timeouts

3. **Analyze caching:**
   - Cache keys use file hashes
   - Appropriate cache paths
   - Cache policy (pull/push)
   - Artifacts for job sharing

4. **Security review:**
   - Secret exposure
   - Permissions
   - Unsafe triggers
   - Action versions

5. **Performance audit:**
   - Parallel execution opportunities
   - Unnecessary sequential jobs
   - Matrix optimization
   - Fail-fast settings

## Usage

Provide:
- Path to CI configuration file(s)
- Specific areas to focus on (optional)

The analyzer will:
1. Parse and validate configuration
2. Check against best practices
3. Identify issues by severity
4. Provide specific fixes
5. Generate optimized configuration

Frequently asked questions

What does the Analyze Ci Config AI skill do?

Analyzes existing CI/CD configurations. Detects issues in GitHub Actions and GitLab CI files, checks for best practices, caching efficiency, and security concerns.

Why use Analyze Ci Config on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dykyi-roman/awesome-claude-code/tree/master/skills/analyze-ci-config. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyze Ci Config?

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 Analyze Ci Config?

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

Is the Analyze Ci Config AI skill free?

Yes. It is published on GitHub by dykyi-roman 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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