Ci Test Optimization logo

Ci Test Optimization

Community
naodeng
ci-test-optimization

Use this skill when you need evidence-bounded pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; triggers include CI 测试流水线 and CI test pipeline.

Overview

Publishernaodeng
Repositoryawesome-qa-skills
Skill nameci-test-optimization
Stars
279
Forks
36
Bundled files
10
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.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by naodeng on GitHub. Read the source before you install it.

Installation

Install the Ci Test Optimization 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/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ci-test-optimization
  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/naodeng/awesome-qa-skills.git /tmp/awesome-qa-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-qa-skills/skills/en/testing-types/ci-test-optimization .claude/skills/ci-test-optimization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ci Test Optimization 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 Test Optimization 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 Test Optimization 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 Test Pipeline Optimization

When to Use

  • Use this skill when you need evidence-bounded analysis, design, or validation preparation for pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries.
  • Use it to review a plan, result, or metric and turn the review into executable improvements.
  • Use it when input is incomplete but a bounded first pass with assumptions, gaps, and human-decision boundaries is still useful.

Output Format Options

  • Default to Markdown organized by domain risk, evidence state, priority, and boundary.
  • When the user requests tables, CSV, JSON, or ticket fields, preserve the same finding fields, evidence, and decision boundaries.
  • Before machine consumption, confirm the schema, enums, required fields, and evidence sources.
  • In Markdown tables, escape literal | characters inside cells as \| (including inline code), or rephrase them so they do not create extra columns.

How to Use

  1. Read and follow prompts/ci-test-optimization.md, including its input audit, domain coverage, and output order.
  2. Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to stage dependency, feedback latency, resource use, flakiness, caching and sharding.
  3. Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength.
  4. Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method.
  5. When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence.

Reference Files

  • When calibrating domain outcomes, valid controls, or evidence gaps, read references/domain-decision-example.md for sanitized input, per-item oracles, and reproducible calculations.

  • Always read prompts/ci-test-optimization.md; it is the complete execution specification for this skill.

  • For evaluation, read evals/eval.yaml and the matching cases under evals/cases/.

  • Load references/, examples/, scripts/, or output-formats.md only when those directories exist and the task needs them.

Domain Execution Essentials

  1. Fix commit/run/runner/config/test-set identities. Decompose events into queue, checkout/install, setup, execute, retry, and report; separate end-to-end waiting from runner busy time.
  2. Compute the actual DAG critical path without summing overlapping jobs. Preserve retry counts and original failures; retry green cannot erase first failure.
  3. Cache keys include relevant lockfile/toolchain/config identity. Compare cold/warm runs, hits, and invalidation evidence; mismatched cache cannot bypass dependency validation.
  4. Match before/after protection obligations, tests, fault detection, resources, and windows. Reject speedups through dropped tests/swallowed failures; propose measurable gains and rollback signals.

Core Constraints

  • Keep the analysis focused on pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; do not replace business owners or Human risk acceptance, exception approval, or release decisions.
  • Never invent system behavior, fields, metrics, thresholds, data, root causes, execution records, or pass claims.
  • Static design, plans, file presence, or a dry run retain their evidence state and cannot become proof of real execution.
  • When evidence is insufficient, use pending confirmation, blocked, unassessed, or NOT_SCORED and give the smallest validation method.
  • For production, privacy, or security work, use least privilege, masked data, mocks, dry runs, or isolation.

Delivery Checklist

  • Covered stage dependency, feedback latency, resource use, flakiness, caching and sharding, with source, evidence state, and validation method for each.
  • Separated facts, inferences, candidate recommendations, gaps, and Human decisions.
  • Gave high-risk items P0/P1/P2/P3 or an equivalent priority, owner role, and close condition.
  • Did not turn plans, static checks, or dry runs into test execution, all-passed, or release-approved claims.
  • Stated residual risk, stop/escalation conditions, and next actions.

Common Pitfalls

  • Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence.
  • Treating adjacent metrics or tool names as a complete CI test pipeline judgment.
  • Using unexplained numbers for false precision or writing correlation as causation.
  • Refusing incomplete input, or pretending that incomplete evidence is conclusive.

Best Practices

  • Start with paths most likely to cause business loss, quality regression, or decision blockage.
  • Use the smallest verifiable experiment to reduce uncertainty and record conditions, versions, sources, and evidence.
  • Make the Skill independently installable, executable, and reviewable by another engineer.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ci Test Optimization AI skill do?

Use this skill when you need evidence-bounded pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; triggers include CI 测试流水线 and CI test pipeline.

Why use Ci Test Optimization on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ci-test-optimization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ci Test Optimization?

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 Test Optimization?

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

Is the Ci Test Optimization AI skill free?

It is published on GitHub by naodeng. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇