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Code Standards

Community
tamdogood
code-standards

Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and type-checker clean, keep the build and tests green, make interface changes additive, protect security invariants).

Overview

Publishertamdogood
Repositorybuilder-essential-skills
Skill namecode-standards
Stars
225
Forks
8
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 tamdogood on GitHub. Read the source before you install it.

Installation

Install the Code Standards 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/tamdogood/builder-essential-skills/tree/main/skills/code-standards
  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/tamdogood/builder-essential-skills.git /tmp/builder-essential-skills
mkdir -p .claude/skills
cp -r /tmp/builder-essential-skills/skills/code-standards .claude/skills/code-standards
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Standards 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 Code Standards 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 Code Standards 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.

Code standards — the change workflow

You are making a code change. This skill is the execution order; it is stack-agnostic. Adapt every command to the project's actual toolchain: read the project's own docs (README, CONTRIBUTING, AGENTS.md/CLAUDE.md, a Makefile or package scripts) once to learn its build, test, lint, and format commands before you start.

Steps

  1. Orient. Read any project lessons/gotchas file (e.g. tasks/lessons.md) and obey it. Find and read the one doc for the subsystem you're changing, not the whole tree.
  2. Plan if non-trivial (3+ steps or an architectural choice): short plan in tasks/todo.md — files touched, tests to add, risks. Going sideways → stop and re-plan.
  3. Baseline: get the project's build + test command green before touching anything. A pre-existing red is the first task; never build on top of a broken baseline.
  4. Implement the smallest root-cause change. Hand-match the surrounding style. Don't run a mass auto-formatter on files you touch; it buries your real diff in noise.
  5. Test: new behavior gets a test that fails without the change; security gates get negative-assertion tests (wrong token → denied, state unchanged, no data leak).
  6. Verify: run the full check suite (build + lint + type-check + tests) until green before you call it "done".
  7. Self-review your own diff as if it were someone else's PR (or invoke a review skill). Fix findings before presenting.
  8. Close out: update docs in the same change; append to a lessons file if you were corrected or surprised; write a conventional commit message (feat|fix|docs(scope): …).

Tripwires — stop and re-check the project's guidelines when you're about to…

  • change a public or shared interface (API, wire format, schema) → check every caller; prefer additive changes
  • add a dependency → weigh transitive weight and licenses, and whether the stdlib already covers it
  • touch auth, tokens, or permissions → audit every writer and reader path, add negative tests
  • write a secret to disk → use the platform's atomic create-private API, never write-then-chmod
  • hold a lock across an await/blocking call, or read env inside logic → extract a pure decision function

No-progress guard

The same failure surviving two fix attempts means stop: write the finding to your lessons file, mark the item [BLOCKED], and surface it. Don't thrash.

Frequently asked questions

What does the Code Standards AI skill do?

Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and type-checker clean, keep the build and tests green, make interface changes additive, protect security invariants).

Why use Code Standards on TypingMind?

Because you install it once and use it with any model. Code Standards 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 Code Standards in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tamdogood/builder-essential-skills/tree/main/skills/code-standards. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code Standards?

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 Code Standards?

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

Is the Code Standards AI skill free?

Yes. It is published on GitHub by tamdogood 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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