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Coding Effectively

Organization
ed3dai
coding-effectively

ALWAYS use this skill when writing or refactoring code. Includes context-dependent sub-skills to empower different coding styles across languages and runtimes.

Overview

Publishered3dai
Repositoryed3d-plugins
Skill namecoding-effectively
Stars
249
Forks
33
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 ed3dai on GitHub. Read the source before you install it.

Installation

Install the Coding Effectively 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/ed3dai/ed3d-plugins.git /tmp/ed3d-plugins
mkdir -p .claude/skills
cp -r /tmp/ed3d-plugins/plugins/ed3d-house-style/skills/coding-effectively .claude/skills/coding-effectively
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coding Effectively 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 Coding Effectively 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 Coding Effectively 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.

Coding Effectively

Required Sub-Skills

ALWAYS REQUIRED:

  • howto-functional-vs-imperative - Separate pure logic from side effects
  • defense-in-depth - Validate at every layer data passes through

CONDITIONAL: Use these sub-skills when applicable:

  • howto-code-in-typescript - TypeScript code
  • howto-code-in-rust - Rust code
  • howto-develop-with-postgres - PostgreSQL database code
  • programming-in-react - React frontend code
  • writing-good-tests - Writing or reviewing tests
  • property-based-testing - Tests for serialization, validation, normalization, pure functions

Property-Driven Design

When designing features, think about properties upfront. This surfaces design gaps early.

Discovery questions:

QuestionProperty TypeExample
Does it have an inverse operation?Roundtripdecode(encode(x)) == x
Is applying it twice the same as once?Idempotencef(f(x)) == f(x)
What quantities are preserved?InvariantsLength, sum, count unchanged
Is order of arguments irrelevant?Commutativityf(a, b) == f(b, a)
Can operations be regrouped?Associativityf(f(a,b), c) == f(a, f(b,c))
Is there a neutral element?Identityf(x, 0) == x
Is there a reference implementation?Oraclenew(x) == old(x)
Can output be easily verified?Easy to verifyis_sorted(sort(x))

Common design questions these reveal:

  • "What about deleted/deactivated entities?"
  • "Case-sensitive or not?"
  • "Stable sort or not? Tie-breaking rules?"
  • "Which algorithm? Configurable?"

Surface these during design, not during debugging.

Core Engineering Principles

Correctness Over Convenience

Model the full error space. No shortcuts.

  • Handle all edge cases: race conditions, timing issues, partial failures
  • Use the type system to encode correctness constraints
  • Prefer compile-time guarantees over runtime checks where possible
  • When uncertain, explore and iterate rather than assume

Don't:

  • Simplify error handling to save time
  • Ignore edge cases because "they probably won't happen"
  • Use any or equivalent to bypass type checking

Error Handling Philosophy

Two-tier model:

  1. User-facing errors: Semantic exit codes, rich diagnostics, actionable messages
  2. Internal errors: Programming errors that may panic or use internal types

Error message format: Lowercase sentence fragments for "failed to {message}".

Good: failed to connect to database: connection refused
Bad:  Failed to Connect to Database: Connection Refused

Good: invalid configuration: missing required field 'apiKey'
Bad:  Invalid Configuration: Missing Required Field 'apiKey'

Lowercase fragments compose naturally: "operation failed: " + error.message reads correctly.

Pragmatic Incrementalism

  • Prefer specific, composable logic over abstract frameworks
  • Evolve design incrementally rather than perfect upfront architecture
  • Don't build for hypothetical future requirements
  • Document design decisions and trade-offs when making non-obvious choices

The rule of three applies to abstraction: Don't abstract until you've seen the pattern three times. Three similar lines of code is better than a premature abstraction.

File Organization

Descriptive File Names Over Catch-All Files

Name files by what they contain, not by generic categories.

Don't create:

  • utils.ts - Becomes a dumping ground for unrelated functions
  • helpers.ts - Same problem
  • common.ts - What isn't common?
  • misc.ts - Actively unhelpful

Do create:

  • string-formatting.ts - String manipulation utilities
  • date-arithmetic.ts - Date calculations
  • api-error-handling.ts - API error utilities
  • user-validation.ts - User input validation

Why this matters:

  • Discoverability: Developers find code by scanning file names
  • Cohesion: Related code stays together
  • Prevents bloat: Hard to add unrelated code to string-formatting.ts
  • Import clarity: import { formatDate } from './date-arithmetic' is self-documenting

When you're tempted to create utils.ts: Stop. Ask what the functions have in common. Name the file after that commonality.

Module Organization

  • Keep module boundaries strict with restricted visibility
  • Platform-specific code in separate files: unix.ts, windows.ts, posix.ts
  • Use conditional compilation or runtime checks for platform branching
  • Test helpers in dedicated modules/files, not mixed with production code

Cross-Platform Principles

Use OS-Native Logic

Don't emulate Unix on Windows or vice versa. Use each platform's native patterns.

Bad: Trying to make Windows paths behave like Unix paths everywhere.

Good: Accept platform differences, handle them explicitly.

typescript
// Platform-specific behavior
if (process.platform === 'win32') {
  // Windows-native approach
} else {
  // POSIX approach
}

Platform-Specific Files

When platform differences are significant, use separate files:

process-spawn.ts        // Shared interface and logic
process-spawn-unix.ts   // Unix-specific implementation
process-spawn-windows.ts // Windows-specific implementation

Document Platform Differences

When behavior differs by platform, document it in comments:

typescript
// On Windows, this returns CRLF line endings.
// On Unix, this returns LF line endings.
// Callers should normalize if consistent output is needed.
function readTextFile(path: string): string { ... }

Test on All Target Platforms

Don't assume Unix behavior works on Windows. Test explicitly:

  • CI should run on all supported platforms
  • Platform-specific code paths need platform-specific tests
  • Document which platforms are supported

Common Mistakes

MistakeRealityFix
"Just put it in utils for now"utils.ts becomes 2000 lines of unrelated codeName files by purpose from the start
"Edge cases are rare"Edge cases cause production incidentsHandle them. Model the full error space.
"We might need this abstraction later"Premature abstraction is harder to remove than addWait for the third use case
"It works on my Mac"It may not work on Windows or LinuxTest on target platforms
"The type system is too strict"Strictness catches bugs at compile timeFix the type error, don't bypass it

Red Flags

Stop and refactor when you see:

  • A utils.ts or helpers.ts file growing beyond 200 lines
  • Error handling that swallows errors or uses generic messages
  • Platform-specific code mixed with cross-platform code
  • Abstractions created for single use cases
  • Type assertions (as any) to bypass the type system
  • Code that "works on my machine" but isn't tested cross-platform

Commit Hygiene

Applies to all languages. Commits are the unit of review and bisect; treat them with the same care as the code they contain.

  • Each commit is a logical, atomic unit of change.
  • Every commit must build and pass all checks (bisect-able history).
  • Separate concerns: formatting fixes and refactoring go in separate commits from feature changes.
  • Use simple past and present tense in bodies: "Previously X happened. With this commit, Y now happens."
  • Commit message bodies use markdown. Do not use backticks in commit titles, but do use them in bodies.

Frequently asked questions

What does the Coding Effectively AI skill do?

ALWAYS use this skill when writing or refactoring code. Includes context-dependent sub-skills to empower different coding styles across languages and runtimes.

Why use Coding Effectively on TypingMind?

Because you install it once and use it with any model. Coding Effectively 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 Coding Effectively in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ed3dai/ed3d-plugins/tree/main/plugins/ed3d-house-style/skills/coding-effectively. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Coding Effectively?

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 Coding Effectively?

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

Is the Coding Effectively AI skill free?

It is published on GitHub by ed3dai. 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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