Skills For Real Engineers

Note

This fork intentionally diverges from upstream by adding the to-orc skill, introducing an orchestration tagging system, and making /grill-with-docs more efficient by asking all questions at once.

A collection of practical agent skills for real engineering work.

Developing real applications is hard. Approaches like GSD, BMAD, and Spec-Kit try to help by owning the process. But while doing so, they take away your control and make bugs in the process hard to resolve.

These skills are designed to be small, easy to adapt, and composable. They work with any model. Fork them, trim them, and reshape them to match your workflow.

Quickstart (30-second setup)

1. Run the skills.sh installer:

npx skills@latest add 100nandoo/skills

2. Pick the skills you want, and which coding agents you want to install them on. Make sure you select /setup-repo-skills.

3. Run /setup-repo-skills in your agent. It will:

   • Ask you which issue tracker you want to use (GitHub, Linear, or local files)
   • Ask you what labels you apply to tickets when you triage them (/triage uses labels)
   • Ask you where you want to save any docs we create

4. Bam - you're ready to go.

Why These Skills Exist

This repo exists to fix common failure modes in Claude Code, Codex, and other coding agents.

#1: The Agent Didn't Do What I Want

"No-one knows exactly what they want"

David Thomas & Andrew Hunt, The Pragmatic Programmer

The Problem. The most common failure mode in software development is misalignment. You think the dev knows what you want. Then you see what they've built - and you realize it didn't understand you at all.

This is just the same in the AI age. There is a communication gap between you and the agent. The fix for this is a grilling session - getting the agent to ask you detailed questions about what you're building.

The Fix is to use:

• /grill-me - for non-code uses
• /grill-with-docs - same as /grill-me, but adds more goodies (see below)

These skills help you align with the agent before you get started, and think deeply about the change you're making. Use them every time you want to make a change.

#2: The Agent Is Way Too Verbose

With a ubiquitous language, conversations among developers and expressions of the code are all derived from the same domain model.

Eric Evans, Domain-Driven-Design

The Problem: At the start of a project, devs and the people they're building the software for (the domain experts) are usually speaking different languages.

The same tension shows up with coding agents. Agents are usually dropped into a project and asked to figure out the jargon as they go. So they use 20 words where 1 will do.

The Fix for this is a shared language. It's a document that helps agents decode the jargon used in the project.

Example

Here's an example CONTEXT.md shape. Which one is easier to read?

• BEFORE: "There's a problem when a lesson inside a section of a course is made 'real' (i.e. given a spot in the file system)"
• AFTER: "There's a problem with the materialization cascade"

This concision pays off session after session.

This is built into /grill-with-docs. It's a grilling session that helps you build a shared language with the AI and document hard-to-explain decisions in ADRs.

It's hard to explain how powerful this is. It might be the single coolest technique in this repo. Try it, and see.

Tip

A shared language has many other benefits than reducing verbosity:

• Variables, functions and files are named consistently, using the shared language
• As a result, the codebase is easier to navigate for the agent
• The agent also spends fewer tokens on thinking, because it has access to a more concise language

#3: The Code Doesn't Work

"Always take small, deliberate steps. The rate of feedback is your speed limit. Never take on a task that’s too big."

David Thomas & Andrew Hunt, The Pragmatic Programmer

The Problem: Let's say that you and the agent are aligned on what to build. What happens when the agent still produces crap?

It's time to look at your feedback loops. Without feedback on how the code it produces actually runs, the agent will be flying blind.

The Fix: You need the usual tranche of feedback loops: static types, browser access, and automated tests.

For automated tests, a red-green-refactor loop is critical. This is where the agent writes a failing test first, then fixes the test. This helps give the agent a consistent level of feedback that results in far better code.

A /tdd skill is included and can slot into any project. It encourages red-green-refactor and gives the agent plenty of guidance on what makes good and bad tests.

For debugging, I've also built a /diagnosing-bugs skill that wraps best debugging practices into a simple loop.

#4: We Built A Ball Of Mud

"Invest in the design of the system every day."

Kent Beck, Extreme Programming Explained

"The best modules are deep. They allow a lot of functionality to be accessed through a simple interface."

John Ousterhout, A Philosophy Of Software Design

The Problem: Most apps built with agents are complex and hard to change. Because agents can radically speed up coding, they also accelerate software entropy. Codebases get more complex at an unprecedented rate.

The Fix for this is a radical new approach to AI-powered development: caring about the design of the code.

This is built in to every layer of these skills:

• /to-prd quizzes you about which modules you're touching before creating a PRD

And crucially, /improve-codebase-architecture helps you rescue a codebase that has become a ball of mud. I recommend running it on your codebase once every few days.

Summary

Software engineering fundamentals matter more than ever. These skills condense those fundamentals into repeatable practices you can adapt to your own repos.

Reference

These split on one axis — who can invoke them. User-invoked skills are reachable only when you type them (e.g. /grill-me); their job is to orchestrate. Model-invoked skills can be invoked by you or reached for automatically by the agent when the task fits; they hold the reusable discipline. A user-invoked skill may invoke model-invoked skills, but never another user-invoked one.

Engineering

Daily skills for code work.

User-invoked

• ask-matt — Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.
• grill-with-docs — Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates CONTEXT.md and ADRs inline.
• triage — Move issues through a state machine of triage roles.
• improve-codebase-architecture — Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
• setup-repo-skills — Scaffold the per-repo config (issue tracker, triage label vocabulary, domain doc layout, orchestration-label policy) that the engineering skills consume. Run once per repo before using to-issues, to-prd, triage, tdd, diagnosing-bugs, or improve-codebase-architecture.
• to-issues — Break any plan, spec, or PRD into independently-grabbable issues using vertical slices.
• to-prd — Turn the current conversation into a PRD and publish it to the issue tracker. No interview — just synthesizes what you've already discussed, including rollout/orchestration notes when useful.
• do-work — Execute a GitHub issue using to-orc stage labels, either by selecting the next unblocked issue or by honoring an explicit target such as do-work 67, then choosing the correct dev vs worktree flow and branch base.
• to-orc — Design dependency stages, parallel lanes, and chat routing for implementation issues so multiple agent chats can execute safely.
• prototype — Build a throwaway prototype to flesh out a design — either a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.

Model-invoked

• diagnosing-bugs — Disciplined diagnosis loop for hard bugs and performance regressions: reproduce → minimise → hypothesise → instrument → fix → regression-test.
• tdd — Test-driven development with a red-green-refactor loop. Builds features or fixes bugs one vertical slice at a time.
• domain-modeling — Actively build and sharpen a project's domain model — challenge terms against the glossary, stress-test with edge-case scenarios, and update CONTEXT.md and ADRs inline.
• codebase-design — Shared discipline and vocabulary for designing deep modules: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface.

Productivity

General workflow tools, not code-specific.

User-invoked

• grill-me — Get relentlessly interviewed about a plan or design until every branch of the decision tree is resolved.
• handoff — Compact the current conversation into a handoff document so another agent can continue the work.
• teach — Teach the user a new skill or concept over multiple sessions, using the current directory as a stateful teaching workspace.
• writing-great-skills — Reference for writing and editing skills well: the vocabulary and principles that make a skill predictable.

Model-invoked

• grilling — Interview the user relentlessly about a plan or design until every branch of the decision tree is resolved. The reusable loop behind grill-me and grill-with-docs.

Misc

Tools kept around but used less often.

• git-guardrails-claude-code — Set up Claude Code hooks to block dangerous git commands (push, reset --hard, clean, etc.) before they execute.
• migrate-to-shoehorn — Migrate test files from as type assertions to @total-typescript/shoehorn.
• scaffold-exercises — Create exercise directory structures with sections, problems, solutions, and explainers.
• setup-pre-commit — Set up Husky pre-commit hooks with lint-staged, Prettier, type checking, and tests.