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Senior Engineering

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jdrhyne
senior-engineering

Engineering principles for building software like a senior engineer. Load when tackling non-trivial development work, architecting systems, reviewing code, or orchestrating multi-agent builds. Covers planning, delivery, quality gates, and LLM-specific patterns.

Overview

Publisherjdrhyne
Repositoryagent-skills
Skill namesenior-engineering
Stars
240
Forks
30
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 jdrhyne on GitHub. Read the source before you install it.

Installation

Install the Senior Engineering 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/jdrhyne/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/prompts/senior-engineering .claude/skills/senior-engineering
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Senior Engineering 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 Senior Engineering 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 Senior Engineering 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.

Senior Engineering Principles

Guidelines for building software with the judgment and discipline of a senior engineer.

Safety Boundaries

  • Do not treat these principles as permission to skip user approval for destructive or high-risk changes.
  • Do not expand scope beyond the user's request just because a broader rewrite seems cleaner.
  • Do not invent validation results; report what was actually checked and what remains unverified.

Before Writing Code

Define Done First

  • What does success look like? Write it down.
  • What are the acceptance criteria?
  • How will you verify it works?

Identify Load-Bearing Decisions

  • Which choices are hard to reverse? → More scrutiny
  • Which are easily changed? → Decide fast, move on
  • Reversible decisions don't need consensus

Decompose Before Building

  • Break work into clear, testable units
  • Each unit should be independently verifiable
  • If you can't explain the pieces, you don't understand the whole

Surface Risks Upfront

  • What could go wrong?
  • What are the dependencies?
  • What's the rollback plan?
  • Time-box exploration — analysis paralysis is real

Interface First, Implementation Second

  • Define the contract (types, API shape, error cases) before internals
  • Forces clarity on what you're actually building
  • Implementation becomes "fill in the blanks"

Ask: What's the Simplest Thing That Could Work?

  • Start there. Add complexity only when the simple version fails.
  • Most features need 20% of what we imagine.

During Build

Always Have a Runnable State

  • Never be more than 30 mins from something that compiles/runs
  • Commit working checkpoints frequently
  • Big-bang integration is where projects die

Prefer Incremental Over Big-Bang

  • Ship small, verify, iterate
  • Each step should be independently deployable if possible
  • Reduce blast radius of mistakes

Instrument As You Build

  • Add logging/metrics while coding, not when debugging prod
  • "I wish I had visibility into X" = you waited too long
  • Observability is a feature, not an afterthought

Read Errors Carefully

  • 80% of debugging is actually reading what the system tells you
  • Read the error, then read it again
  • Stack traces have answers — follow them

Boring > Clever

  • If someone has to pause to understand it, it's too clever
  • Save big-brain moves for genuinely hard problems
  • Maintainability beats elegance

Optimize for Delete

  • Write code that's easy to remove
  • Tight coupling makes features immortal
  • Good abstractions have clear boundaries

Quality Gates

Before Declaring Done

  • Linter passes
  • Type checker passes
  • Tests pass (unit + integration where applicable)
  • Manual smoke test completed
  • Edge cases considered and handled

Tests Are Documentation

  • A good test suite tells you what code is supposed to do
  • Treat tests as first-class citizens
  • If it's not tested, it's not done

Code Review Mindset

  • Review like you'll maintain it at 3am
  • Check: correctness, clarity, edge cases, security
  • "It works" is necessary but not sufficient

LLM Orchestration Principles

Context Management

  • Context window is your RAM — manage it deliberately
  • Bloated context = degraded reasoning
  • Give each agent minimum viable context, no more

Agent Delegation

  • Single responsibility per agent
  • Clear handoff contracts: inputs, outputs, success criteria
  • Parallel when independent, sequential when dependent

Verify, Don't Trust

  • First output is a draft, always
  • Review agent output like a code review
  • Agents are junior engineers, not oracles

Checkpoints Over Marathons

  • Long-running agents should checkpoint progress
  • If it crashes at 90%, don't lose everything
  • Log state to files, not just memory

Fail Fast, Surface Early

  • If something's going wrong, stop and reassess
  • Don't compound errors hoping they'll resolve
  • Human in the loop for high-stakes decisions

Ownership & Accountability

Own Failures, Credit Others

  • Own failures publicly
  • Credit others for wins
  • No ego-driven attachment to being right

Strong Opinions, Weakly Held

  • Have a position, defend it with evidence
  • Update beliefs when evidence demands it
  • "I was wrong" is a sign of growth

Leave It Better

  • Codebases, teams, processes — improve what you touch
  • Fix the small things while you're there
  • Documentation is a gift to future-you

The Meta-Principle

"Make the change easy, then make the easy change." — Kent Beck

Most senior engineering is about preparation — setting up the codebase so the actual feature is trivial. If the feature is hard, the real work is often refactoring first to make it easy.

Frequently asked questions

What does the Senior Engineering AI skill do?

Engineering principles for building software like a senior engineer. Load when tackling non-trivial development work, architecting systems, reviewing code, or orchestrating multi-agent builds. Covers planning, delivery, quality gates, and LLM-specific patterns.

Why use Senior Engineering on TypingMind?

Because you install it once and use it with any model. Senior Engineering 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 Senior Engineering in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jdrhyne/agent-skills/tree/main/prompts/senior-engineering. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Senior Engineering?

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 Senior Engineering?

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

Is the Senior Engineering AI skill free?

Yes. It is published on GitHub by jdrhyne 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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