evist0/okf-matt-skills

# Codebase Design

Design **deep modules**: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface. Use this language and these principles wherever code is being designed or restructured. The aim is leverage for callers, locality for maintainers, and testability for everyone.

## Glossary

Use these terms exactly — don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point.

**Module** — anything with an interface and an implementation. Deliberately scale-agnostic: a function, class, package, or tier-spanning slice. _Avoid_: unit, component, service.

**Interface** — everything a caller must know to use the module correctly: the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. _Avoid_: API, signature (too narrow — they refer only to the type-level surface).

**Implementation** — what's inside a module, its body of code. Distinct from **Adapter**: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise.

# Debug

A discipline for hard bugs. Skip phases only when explicitly justified.

When exploring the codebase, read the project's OKF knowledge bundle if it exists (`knowledge/glossary/` for a clear mental model of the relevant modules, `knowledge/adr/` for decisions), and check ADRs in the area you're touching.

## Phase 1 — Build a feedback loop

**This is the skill.** Everything else is mechanical. If you have a **tight** pass/fail signal for the bug — one that goes red on _this_ bug — you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.

Spend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**

### Ways to construct one — try them in roughly this order

# Domain Modeling

Actively build and sharpen the project's domain model as you design. This is the *active* discipline — challenging terms, inventing edge-case scenarios, and writing the glossary and decisions down the moment they crystallise. (Merely *reading* the knowledge bundle's glossary for vocabulary is not this skill — that's a one-line habit any skill can do. This skill is for when you're changing the model, not just consuming it.)

## File structure

The domain model lives in an **Open Knowledge Format (OKF)** knowledge bundle at `knowledge/` in the repo root. Most repos have a single context:

```
knowledge/
├── index.md              ← bundle map (reserved: no frontmatter)
├── log.md                ← change history, newest first (reserved: no frontmatter)
├── glossary/             ← one file per term (the glossary)
│   ├── index.md

Run a `/grilling` session, using the `/domain-modeling` skill.

Run a `/grilling` session.

Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask the questions one at a time, waiting for feedback on each question before continuing. Asking multiple questions at once is bewildering.

If a *fact* can be found by exploring the codebase, look it up rather than asking me. The *decisions*, though, are mine — put each one to me and wait for my answer.

Do not enact the plan until I confirm we have reached a shared understanding.

Write a handoff document summarising the current conversation so a fresh agent can continue the work. Save to the temporary directory of the user's OS - not the current workspace.

Include a "suggested skills" section in the document, which suggests skills that the agent should invoke.

Do not duplicate content already captured in other artifacts (PRDs, plans, ADRs, issues, commits, diffs). Reference them by path or URL instead.

Redact any sensitive information, such as API keys, passwords, or personally identifiable information.

If the user passed arguments, treat them as a description of what the next session will focus on and tailor the doc accordingly.

Implement the work described by the user in the spec or tickets.

Use /tdd where possible, at pre-agreed seams.

Run typechecking regularly, single test files regularly, and the full test suite once at the end.

Once done, use /vet to review the work.

After the review's findings are resolved, run /reconcile to bring the OKF knowledge bundle back into agreement with what you built — over the same diff the review used.

Commit your work to the current branch, so code and knowledge land in one commit.

# Improve Codebase Architecture

Surface architectural friction and propose **deepening opportunities** — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.

This command is _informed_ by the project's domain model and built on a shared design vocabulary:

- Run the `/codebase-design` skill for the architecture vocabulary (**module**, **interface**, **depth**, **seam**, **adapter**, **leverage**, **locality**) and its principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use these terms exactly in every suggestion — don't drift into "component," "service," "API," or "boundary."
- The domain language in `knowledge/glossary/` gives names to good seams; ADRs in `knowledge/adr/` record decisions this command should not re-litigate.

## Process

### 1. Explore

Read the project's domain glossary (`knowledge/glossary/`) and any ADRs (`knowledge/adr/`) in the area you're touching first.

# Prototype

A prototype is **throwaway code that answers a question**. The question decides the shape.

## Pick a branch

Identify which question is being answered — from the user's prompt, the surrounding code, or by asking if the user is around:

- **"Does this logic / state model feel right?"** → [LOGIC.md](LOGIC.md). Build a tiny interactive terminal app that pushes the state machine through cases that are hard to reason about on paper.
- **"What should this look like?"** → [UI.md](UI.md). Generate several radically different UI variations on a single route, switchable via a URL search param and a floating bottom bar.

The two branches produce very different artifacts — getting this wrong wastes the whole prototype. If the question is genuinely ambiguous and the user isn't reachable, default to whichever branch better matches the surrounding code (a backend module → logic; a page or component → UI) and state the assumption at the top of the prototype.

## Rules that apply to both

# Reconcile

A change landed: the code moved, but the OKF bundle (`knowledge/`) may not have. **Reconcile** them — bring durable knowledge back into agreement with what was actually built, so the next session starts from a consistent bundle.

This is the *writing* discipline of the **build** phase — the mirror of `/domain-modeling`, which writes the bundle during **design**. It runs last in the `/implement` closeout, over the same diff `/vet` judged, so code and knowledge land in one commit.

## Scope

Reconcile against the diff, never the whole bundle. Fixed point is the one `/vet` used, or one the user supplies: `git diff <fixed-point>...HEAD`.

Durable knowledge is everything under `knowledge/` — the canonical categories `glossary/`, `adr/`, and `spec/`, plus any project-specific concept category registered in `knowledge/index.md`. Touch a concept only when the diff gives you a reason to.

## Process

Spin up a **background agent** to do the research, so you keep working while it reads.

Its job:

1. Investigate the question against **primary sources** — official docs, source code, specs, first-party APIs — not a secondary write-up of them. Follow every claim back to the source that owns it.
2. Write the findings to a single Markdown file, citing each claim's source.
3. Save it where the repo already keeps such notes; match the existing convention, and if there is none, put it somewhere sensible and say where.

# Setup okf-matt-skills

Scaffold the per-repo configuration that the engineering skills assume:

- **Issue tracker** — where issues live (GitHub by default; local markdown is also supported out of the box)
- **Triage labels** — the strings used for the five canonical triage roles
- **Domain docs** — where the OKF knowledge bundle (`knowledge/`) lives, and the consumer rules for reading it

All three land in the **agent layer** at `knowledge/_agents/` — a reserved, `_`-prefixed
service directory inside the OKF bundle that holds agent-operational config and notes, and
is **never** a domain context (see "The agent layer" below).

This is a prompt-driven skill, not a deterministic script. Explore, present what you found, confirm with the user, then write.

# Test-Driven Development

TDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle — consult them before and during the loop, not after.

When exploring the codebase, read the project's OKF knowledge bundle if it exists (`knowledge/glossary/` for the domain language, `knowledge/adr/` for decisions) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.

## What a good test is

Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification — "user can checkout with valid cart" tells you exactly what capability exists — and survives refactors because it doesn't care about internal structure.

See [tests.md](tests.md) for examples and [mocking.md](mocking.md) for mocking guidelines.

## Seams — where tests go

The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.

## Teaching Workspace

Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:

- `MISSION.md`: A document capturing the _reason_ the user is interested in the topic. This should be used to ground all teaching. Use the format in [MISSION-FORMAT.md](./MISSION-FORMAT.md).
- `./reference/*.html`: A directory of reference materials. These are the compressed learnings from the lessons - cheat sheets, reference algorithms, syntax, yoga poses, glossaries. They are the raw units of learning. They should be beautiful documents which print out well, and are designed for quick reference.
- `RESOURCES.md`: A list of resources which can be explored to ground your teaching in contextual knowledge, or to acquire knowledge and wisdom. Use the format in [RESOURCES-FORMAT.md](./RESOURCES-FORMAT.md).
- `./learning-records/*.md`: A directory of learning records, which capture what the user has learned. These are loosely equivalent to architectural decision records in software development - they capture non-obvious lessons and key insights that may need to be revised later, or drive future sessions. These should be used to calculate the zone of proximal development. They are titled `0001-<dash-case-name>.md`, where the number increments each time. Use the format in [LEARNING-RECORD-FORMAT.md](./LEARNING-RECORD-FORMAT.md).
- `./lessons/*.html`: A directory of lessons. A **lesson** is a single, self-contained HTML output that teaches one tightly-scoped thing tied to the mission. This is the primary unit of teaching in this workspace.
- `./assets/*`: Reusable **components** shared across lessons. See [Assets](#assets).
- `NOTES.md`: A scratchpad for you to jot down user preferences, or working notes.

This skill takes the current conversation context and codebase understanding and produces a spec (you may know this document as a PRD). Do NOT interview the user — just synthesize what you already know.

The issue tracker and triage label vocabulary should have been provided to you — run `/setup-okf-matt-skills` if not.

## Process

1. Explore the repo to understand the current state of the codebase, if you haven't already. Use the project's domain glossary vocabulary throughout the spec, and respect any ADRs in the area you're touching.

2. Sketch out the seams at which you're going to test the feature. Existing seams should be preferred to new ones. Use the highest seam possible. If new seams are needed, propose them at the highest point you can. The fewer seams across the codebase, the better - the ideal number is one.

Check with the user that these seams match their expectations.

3. Write the spec using the template below and save it as an OKF concept under `knowledge/spec/` (e.g. `knowledge/spec/<slug>.md`), then append a line to `knowledge/log.md`. The spec is now a durable knowledge record; breaking it into agent-ready tickets on the tracker is `/to-tickets`' job.

# To Tickets

Break a plan, spec, or conversation into a set of **tickets** — tracer-bullet vertical slices, each declaring the tickets that **block** it.

The issue tracker and triage label vocabulary should have been provided to you — run `/setup-okf-matt-skills` if not.

## Process

### 1. Gather context

Work from whatever is already in the conversation context. If the user passes a reference (a spec path, an issue number or URL) as an argument, fetch it and read its full body and comments.

### 2. Explore the codebase (optional)

# Triage

Move issues on the project issue tracker through a small state machine of triage roles.

If this repo treats external pull requests as a request surface (see the issue-tracker config), triage covers them too: **a PR is an issue with attached code** — same roles, same states, same machine, with a few deltas marked "for a PR" below. Resolve a bare `#42` to an issue or PR per the tracker config.

Every comment or issue posted to the issue tracker during triage **must** start with this disclaimer:

```
> *This was generated by AI during triage.*
```

## Reference docs

Two-axis review of the diff between `HEAD` and a fixed point the user supplies:

- **Standards** — does the code conform to this repo's documented coding standards?
- **Spec** — does the code faithfully implement the originating issue / PRD / spec?

Both axes run as **parallel sub-agents** so they don't pollute each other's context, then this skill aggregates their findings.

The issue tracker should have been provided to you — run `/setup-okf-matt-skills` if `knowledge/_agents/issue-tracker.md` is missing.

## Process

### 1. Pin the fixed point

Whatever the user said is the fixed point — a commit SHA, branch name, tag, `main`, `HEAD~5`, etc. If they didn't specify one, ask for it.

A loose idea has arrived — too big for one agent session, and wrapped in fog: the way from here to the **destination** isn't visible yet. Wayfinding is about finding that way, not charging at the destination. This skill charts the way as a **shared map** on the repo's issue tracker, then works its tickets one at a time until the route is clear.

The destination varies per effort, and naming it is the first act of charting — it shapes every ticket. It might be a spec to hand off and iterate on, a decision to lock before planning starts, or a change made in place like a data-structure migration. The map is domain-agnostic — engineering work, course content, whatever fits the shape.

## Plan, don't do

Wayfinder is **planning** by default: each ticket resolves a decision, and the map is done when the way is clear — nothing left to decide before someone goes and does the thing. The pull to just do the work is usually the signal you've reached the edge of the map and it's time to hand off. An effort can override this in its **Notes** — carrying execution into the map itself — but absent that, produce decisions, not deliverables.

When the way is clear and no tickets remain, the map is done: hand the cleared route to `/to-spec`, which captures it as a durable spec under `knowledge/spec/` for the multi-session build to follow (or, if the effort turned out small enough, implement directly).

## Refer by name

Every map and ticket is an issue, so it has a **name** — its title. In everything the human reads — narration, the map's Decisions-so-far — refer to it by that name, never by a bare id, number, or slug. A wall of `#42, #43, #44` is illegible; names read at a glance. The id and URL don't vanish — a name wraps its link — but they ride *inside* the name, never stand in for it.

# Which skill?

You don't remember every skill, so ask.

A **flow** is a path through the skills. Most paths run along one **main flow**, and two **on-ramps** merge onto it. Everything else is standalone, or a vocabulary layer that runs underneath.

## The main flow: idea → ship

The route most work travels. You have an idea and want it built.

1. **`/grill`** — sharpen the idea by interview. Start here when you **have a codebase**: it's stateful, retaining what it learns in the OKF knowledge bundle (`knowledge/` — glossary and ADRs). (No codebase? Use `/grill-plain` — see Standalone. Both run the same `/grilling` primitive; `grill` is the one that leaves a paper trail.)
2. **Branch — can you settle every question in conversation?** If a question needs a runnable answer (state, business logic, a UI you have to see), detour through a prototype, bridged by **`/handoff`** in both directions (see Crossing sessions):
   - **`/handoff`** out, then open a fresh session against that file,
   - **`/prototype`** to answer the question with throwaway code,

A skill exists to wrangle determinism out of a stochastic system. **Predictability** — the agent taking the same _process_ every run, not producing the same output — is the root virtue; every lever below serves it.

**Bold terms** are defined in [`GLOSSARY.md`](GLOSSARY.md); look them up there for the full meaning.

## Invocation

Two choices, trading different costs:

- A **model-invoked** skill keeps a **description**, so the agent can fire it autonomously _and_ other skills can reach it (you can still type its name too). It contributes to **context load** — the description sits in the window every turn. Mechanics: omit `disable-model-invocation`, and write a model-facing description with rich trigger phrasing ("Use when the user wants…, mentions…").
- A **user-invoked** skill strips the description from the agent's reach: only you, typing its name, can invoke it — and no other skill can. Zero context load, but it spends **cognitive load**: _you_ are the index that must remember it exists. Mechanics: set `disable-model-invocation: true`; the `description` becomes human-facing — a one-line summary, trigger lists stripped.

Pick model-invocation only when the agent must reach the skill on its own, or another skill must. If it only ever fires by hand, make it user-invoked and pay no context load.

When user-invoked skills multiply past what you can remember, that piled-up cognitive load is cured by a **router skill**: one user-invoked skill that names the others and when to reach for each.