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720eba8afb skills: Add /implement and dependencies.
This includes /test-driven-development and /codebase-design
2026-07-05 13:52:44 -04:00
28734f9777 skills: Add /to-tasks skill. 2026-07-05 13:01:26 -04:00
8aa3e8d62f skills: Add /to-spec skill. 2026-07-05 12:05:02 -04:00
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# Deepening
How to deepen a cluster of shallow modules safely, given its dependencies. Assumes the vocabulary in [SKILL.md](SKILL.md) — **module**, **interface**, **seam**, **adapter**.
## Dependency categories
When assessing a candidate for deepening, classify its dependencies. The category determines how the deepened module is tested across its seam.
### 1. In-process
Pure computation, in-memory state, no I/O. Always deepenable — merge the modules and test through the new interface directly. No adapter needed.
### 2. Local-substitutable
Dependencies that have local test stand-ins (PGLite for Postgres, in-memory filesystem). Deepenable if the stand-in exists. The deepened module is tested with the stand-in running in the test suite. The seam is internal; no port at the module's external interface.
### 3. Remote but owned (Ports & Adapters)
Your own services across a network boundary (microservices, internal APIs). Define a **port** (interface) at the seam. The deep module owns the logic; the transport is injected as an **adapter**. Tests use an in-memory adapter. Production uses an HTTP/gRPC/queue adapter.
Recommendation shape: *"Define a port at the seam, implement an HTTP adapter for production and an in-memory adapter for testing, so the logic sits in one deep module even though it's deployed across a network."*
### 4. True external (Mock)
Third-party services (Stripe, Twilio, etc.) you don't control. The deepened module takes the external dependency as an injected port; tests provide a mock adapter.
## Seam discipline
- **One adapter means a hypothetical seam. Two adapters means a real one.** Don't introduce a port unless at least two adapters are justified (typically production + test). A single-adapter seam is just indirection.
- **Internal seams vs external seams.** A deep module can have internal seams (private to its implementation, used by its own tests) as well as the external seam at its interface. Don't expose internal seams through the interface just because tests use them.
## Testing strategy: replace, don't layer
- Old unit tests on shallow modules become waste once tests at the deepened module's interface exist — delete them.
- Write new tests at the deepened module's interface. The **interface is the test surface**.
- Tests assert on observable outcomes through the interface, not internal state.
- Tests should survive internal refactors — they describe behaviour, not implementation. If a test has to change when the implementation changes, it's testing past the interface.

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# Design It Twice
When the user wants to explore alternative interfaces for a chosen deepening candidate, use this parallel sub-agent pattern. Based on "Design It Twice" (Ousterhout) — your first idea is unlikely to be the best.
Uses the vocabulary in [SKILL.md](SKILL.md) — **module**, **interface**, **seam**, **adapter**, **leverage**.
## Process
### 1. Frame the problem space
Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate:
- The constraints any new interface would need to satisfy
- The dependencies it would rely on, and which category they fall into (see [DEEPENING.md](DEEPENING.md))
- A rough illustrative code sketch to ground the constraints — not a proposal, just a way to make the constraints concrete
Show this to the user, then immediately proceed to Step 2. The user reads and thinks while the sub-agents work in parallel.
### 2. Spawn sub-agents
Spawn 3+ sub-agents in parallel using the Agent tool. Each must produce a **radically different** interface for the deepened module.
Prompt each sub-agent with a separate technical brief (file paths, coupling details, dependency category from [DEEPENING.md](DEEPENING.md), what sits behind the seam). The brief is independent of the user-facing problem-space explanation in Step 1. Give each agent a different design constraint:
- Agent 1: "Minimize the interface — aim for 13 entry points max. Maximise leverage per entry point."
- Agent 2: "Maximise flexibility — support many use cases and extension."
- Agent 3: "Optimise for the most common caller — make the default case trivial."
- Agent 4 (if applicable): "Design around ports & adapters for cross-seam dependencies."
Include both [SKILL.md](SKILL.md) vocabulary and `.claude/CONTEXT.md` vocabulary in the brief so each sub-agent names things consistently with the architecture language and the project's domain language.
Each sub-agent outputs:
1. Interface (types, methods, params — plus invariants, ordering, error modes)
2. Usage example showing how callers use it
3. What the implementation hides behind the seam
4. Dependency strategy and adapters (see [DEEPENING.md](DEEPENING.md))
5. Trade-offs — where leverage is high, where it's thin
### 3. Present and compare
Present designs sequentially so the user can absorb each one, then compare them in prose. Contrast by **depth** (leverage at the interface), **locality** (where change concentrates), and **seam placement**.
After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated — the user wants a strong read, not a menu.

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---
name: codebase-design
description: Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
---
# 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 repository) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise.
**Depth** — leverage at the interface: the amount of behaviour a caller (or test) can exercise per unit of interface they have to learn. A module is **deep** when a large amount of behaviour sits behind a small interface, **shallow** when the interface is nearly as complex as the implementation.
**Seam** _(Michael Feathers)_ — a place where you can alter behaviour without editing in that place; the *location* at which a module's interface lives. Where to put the seam is its own design decision, distinct from what goes behind it. _Avoid_: boundary (overloaded with DDD's bounded context).
**Adapter** — a concrete thing that satisfies an interface at a seam. Describes *role* (what slot it fills), not substance (what's inside).
**Leverage** — what callers get from depth: more capability per unit of interface they learn. One implementation pays back across N call sites and M tests.
**Locality** — what maintainers get from depth: change, bugs, knowledge, and verification concentrate in one place rather than spreading across callers. Fix once, fixed everywhere.
## Deep vs shallow
**Deep module** = small interface + lots of implementation:
```
┌─────────────────────┐
│ Small Interface │ ← Few methods, simple params
├─────────────────────┤
│ │
│ Deep Implementation│ ← Complex logic hidden
│ │
└─────────────────────┘
```
**Shallow module** = large interface + little implementation (avoid):
```
┌─────────────────────────────────┐
│ Large Interface │ ← Many methods, complex params
├─────────────────────────────────┤
│ Thin Implementation │ ← Just passes through
└─────────────────────────────────┘
```
When designing an interface, ask:
- Can I reduce the number of methods?
- Can I simplify the parameters?
- Can I hide more complexity inside?
## Principles
- **Depth is a property of the interface, not the implementation.** A deep module can be internally composed of small, mockable, swappable parts — they just aren't part of the interface. A module can have **internal seams** (private to its implementation, used by its own tests) as well as the **external seam** at its interface.
- **The deletion test.** Imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
- **The interface is the test surface.** Callers and tests cross the same seam. If you want to test *past* the interface, the module is probably the wrong shape.
- **One adapter means a hypothetical seam. Two adapters means a real one.** Don't introduce a seam unless something actually varies across it.
## Designing for testability
Good interfaces make testing natural:
1. **Accept dependencies, don't create them.**
```typescript
// Testable
function processOrder(order, paymentGateway) {}
// Hard to test
function processOrder(order) {
const gateway = new StripeGateway();
}
```
2. **Return results, don't produce side effects.**
```typescript
// Testable
function calculateDiscount(cart): Discount {}
// Hard to test
function applyDiscount(cart): void {
cart.total -= discount;
}
```
3. **Small surface area.** Fewer methods = fewer tests needed. Fewer params = simpler test setup.
## Relationships
- A **Module** has exactly one **Interface** (the surface it presents to callers and tests).
- **Depth** is a property of a **Module**, measured against its **Interface**.
- A **Seam** is where a **Module**'s **Interface** lives.
- An **Adapter** sits at a **Seam** and satisfies the **Interface**.
- **Depth** produces **Leverage** for callers and **Locality** for maintainers.
## Rejected framings
- **Depth as ratio of implementation-lines to interface-lines** (Ousterhout): rewards padding the implementation. We use depth-as-leverage instead.
- **"Interface" as the TypeScript `interface` keyword or a class's public methods**: too narrow — interface here includes every fact a caller must know.
## Going deeper
- **Deepening a cluster given its dependencies** — see [DEEPENING.md](DEEPENING.md): dependency categories, seam discipline, and replace-don't-layer testing.
- **Exploring alternative interfaces** — see [DESIGN-IT-TWICE.md](DESIGN-IT-TWICE.md): spin up parallel sub-agents to design the interface several radically different ways, then compare on depth, locality, and seam placement.

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### Steps ### Steps
The ordered actions the agent performs — when a skill has them, the primary tier of its content, and the part that earns its place in SKILL.md. Not every skill has steps: a skill can be all steps (`tdd`), all **reference** (a review), or both, independent of invocation. Every step ends on a **completion criterion**, clear or vague. The ordered actions the agent performs — when a skill has them, the primary tier of its content, and the part that earns its place in SKILL.md. Not every skill has steps: a skill can be all steps (`test-driven-development`), all **reference** (a review), or both, independent of invocation. Every step ends on a **completion criterion**, clear or vague.
_Avoid_: workflow, instructions, choreography _Avoid_: workflow, instructions, choreography

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---
name: implement
description: Implement a task file produced by /to-tasks, review it, and close it out.
disable-model-invocation: true
---
Implement a task file end-to-end: build it, review it, and close it out.
## Process
### 1. Read the task file and check blockers
The user passes the path to a task file (`.claude/tasks/<NNNN>-slug.md`, as produced by `/to-tasks`) explicitly — don't infer one from context.
If the task's frontmatter has a `blocked-by` field, read each referenced task file and check for any unresolved `- [ ]` acceptance criterion. If any blocker isn't fully resolved, warn the user which one and why, and confirm before proceeding — don't refuse outright.
### 2. Implement
Build the work described in the task's "What to build" section, satisfying its acceptance criteria. Use `/test-driven-development` where possible, at the seams already agreed when the spec or task was written.
Run typechecking regularly, single test files regularly, and the full test suite once at the end.
### 3. Stage the changes
Stage (`git add`) each file you create or modify, specifically — not `git add -A` — so nothing untracked and unrelated gets swept in.
### 4. Review
Run `/review-uncommitted`, passing the task file itself as the spec source — it already links back to its parent spec via its `spec` frontmatter field, if any. Address anything it raises before moving on.
### 5. Close out the task file
Mark every acceptance criterion `[x]` if satisfied or `[-]` if deliberately dropped, so none are left `[ ]`. Append a `## Implementation Notes` section explaining any deviations from the plan — dropped criteria (referencing which, and why), scope changes, decisions made mid-implementation, follow-ups worth flagging. Skip the section only if nothing deviated. Leave the `spec` and `blocked-by` frontmatter fields untouched — they're a permanent record, not a checklist to clear (see `to-tasks`'s `TASK-FORMAT.md`).
Stage the updated task file with the rest.
Do not commit — leave the commit itself for the user to make.

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---
name: test-driven-development
description: Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
---
# Test-Driven Development
## Philosophy
**Core principle**: Tests should verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't.
**Good tests** are integration-style: they exercise real code paths through public APIs. They describe _what_ the system does, not _how_ it does it. A good test reads like a specification - "user can checkout with valid cart" tells you exactly what capability exists. These tests survive refactors because they don't care about internal structure.
**Bad tests** are coupled to implementation. They mock internal collaborators, test private methods, or verify through external means (like querying a database directly instead of using the interface). The warning sign: your test breaks when you refactor, but behavior hasn't changed. If you rename an internal function and tests fail, those tests were testing implementation, not behavior.
**Tautological tests** restate the implementation inside the assertion, so they pass by construction and give zero confidence. When the expected value is computed the way the code computes it — `expect(add(a, b)).toBe(a + b)`, snapshotting a figure you derived by hand the same way the code does, asserting a constant equals itself — the test can never disagree with the code: break the code wrong and the assertion breaks wrong with it. The expected value must come from an independent source of truth — a known-good literal, a worked example, the spec.
See [tests.md](tests.md) for examples and [mocking.md](mocking.md) for mocking guidelines.
## Anti-Pattern: Horizontal Slices
**DO NOT write all tests first, then all implementation.** This is "horizontal slicing" - treating RED as "write all tests" and GREEN as "write all code."
This produces **crap tests**:
- Tests written in bulk test _imagined_ behavior, not _actual_ behavior
- You end up testing the _shape_ of things (data structures, function signatures) rather than user-facing behavior
- Tests become insensitive to real changes - they pass when behavior breaks, fail when behavior is fine
- You outrun your headlights, committing to test structure before understanding the implementation
**Correct approach**: Vertical slices via tracer bullets. One test → one implementation → repeat. Each test responds to what you learned from the previous cycle. Because you just wrote the code, you know exactly what behavior matters and how to verify it.
```
WRONG (horizontal):
RED: test1, test2, test3, test4, test5
GREEN: impl1, impl2, impl3, impl4, impl5
RIGHT (vertical):
RED→GREEN: test1→impl1
RED→GREEN: test2→impl2
RED→GREEN: test3→impl3
...
```
## Workflow
### 1. Planning
When exploring the codebase, read `.claude/CONTEXT.md` (if it exists) so that test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.
Identify opportunities for deep modules (small interface, deep implementation) — run the `/codebase-design` skill for the vocabulary and the testability checks. Do this regardless of what triggered this workflow.
**If a task file is already in context** (e.g. passed to `/implement`, which called this skill), its acceptance criteria are the behavior list to test — the interface and priorities were already agreed during `/to-spec` and `/to-tasks`. Don't re-confirm them with the user; go straight to the tracer bullet.
**Otherwise**, before writing any code:
- [ ] Confirm with user what interface changes are needed
- [ ] Confirm with user which behaviors to test (prioritize)
- [ ] List the behaviors to test (not implementation steps)
- [ ] Get user approval on the plan
Ask: "What should the public interface look like? Which behaviors are most important to test?"
**You can't test everything.** Confirm with the user exactly which behaviors matter most. Focus testing effort on critical paths and complex logic, not every possible edge case.
### 2. Tracer Bullet
Write ONE test that confirms ONE thing about the system:
```
RED: Write test for first behavior → test fails
GREEN: Write minimal code to pass → test passes
```
This is your tracer bullet - proves the path works end-to-end.
### 3. Incremental Loop
For each remaining behavior:
```
RED: Write next test → fails
GREEN: Minimal code to pass → passes
```
Rules:
- One test at a time
- Only enough code to pass current test
- Don't anticipate future tests
- Keep tests focused on observable behavior
### 4. Refactor
After all tests pass, look for [refactor candidates](refactoring.md):
- [ ] Extract duplication
- [ ] Deepen modules (move complexity behind simple interfaces)
- [ ] Apply SOLID principles where natural
- [ ] Consider what new code reveals about existing code
- [ ] Run tests after each refactor step
**Never refactor while RED.** Get to GREEN first.
## Checklist Per Cycle
```
[ ] Test describes behavior, not implementation
[ ] Test uses public interface only
[ ] Test would survive internal refactor
[ ] Expected values are independent literals, not recomputed from the code
[ ] Code is minimal for this test
[ ] No speculative features added
```

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# When to Mock
Mock at **system boundaries** only:
- External APIs (payment, email, etc.)
- Databases (sometimes - prefer test DB)
- Time/randomness
- File system (sometimes)
Don't mock:
- Your own classes/modules
- Internal collaborators
- Anything you control
## Designing for Mockability
At system boundaries, design interfaces that are easy to mock:
**1. Use dependency injection**
Pass external dependencies in rather than creating them internally:
```typescript
// Easy to mock
function processPayment(order, paymentClient) {
return paymentClient.charge(order.total);
}
// Hard to mock
function processPayment(order) {
const client = new StripeClient(process.env.STRIPE_KEY);
return client.charge(order.total);
}
```
**2. Prefer SDK-style interfaces over generic fetchers**
Create specific functions for each external operation instead of one generic function with conditional logic:
```typescript
// GOOD: Each function is independently mockable
const api = {
getUser: (id) => fetch(`/users/${id}`),
getOrders: (userId) => fetch(`/users/${userId}/orders`),
createOrder: (data) => fetch('/orders', { method: 'POST', body: data }),
};
// BAD: Mocking requires conditional logic inside the mock
const api = {
fetch: (endpoint, options) => fetch(endpoint, options),
};
```
The SDK approach means:
- Each mock returns one specific shape
- No conditional logic in test setup
- Easier to see which endpoints a test exercises
- Type safety per endpoint

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# Refactor Candidates
After TDD cycle, look for:
- **Duplication** → Extract function/class
- **Long methods** → Break into private helpers (keep tests on public interface)
- **Shallow modules** → Combine or deepen — see DEEPENING.md in `/codebase-design` for dependency categories and seam discipline
- **Feature envy** → Move logic to where data lives
- **Primitive obsession** → Introduce value objects
- **Existing code** the new code reveals as problematic

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# Good and Bad Tests
## Good Tests
**Integration-style**: Test through real interfaces, not mocks of internal parts.
```typescript
// GOOD: Tests observable behavior
test("user can checkout with valid cart", async () => {
const cart = createCart();
cart.add(product);
const result = await checkout(cart, paymentMethod);
expect(result.status).toBe("confirmed");
});
```
Characteristics:
- Tests behavior users/callers care about
- Uses public API only
- Survives internal refactors
- Describes WHAT, not HOW
- One logical assertion per test
## Bad Tests
**Implementation-detail tests**: Coupled to internal structure.
```typescript
// BAD: Tests implementation details
test("checkout calls paymentService.process", async () => {
const mockPayment = jest.mock(paymentService);
await checkout(cart, payment);
expect(mockPayment.process).toHaveBeenCalledWith(cart.total);
});
```
Red flags:
- Mocking internal collaborators
- Testing private methods
- Asserting on call counts/order
- Test breaks when refactoring without behavior change
- Test name describes HOW not WHAT
- Verifying through external means instead of interface
```typescript
// BAD: Bypasses interface to verify
test("createUser saves to database", async () => {
await createUser({ name: "Alice" });
const row = await db.query("SELECT * FROM users WHERE name = ?", ["Alice"]);
expect(row).toBeDefined();
});
// GOOD: Verifies through interface
test("createUser makes user retrievable", async () => {
const user = await createUser({ name: "Alice" });
const retrieved = await getUser(user.id);
expect(retrieved.name).toBe("Alice");
});
```
**Tautological tests**: Expected value restates the implementation, so the test passes by construction.
```typescript
// BAD: Expected value is recomputed the way the code computes it
test("calculateTotal sums line items", () => {
const items = [{ price: 10 }, { price: 5 }];
const expected = items.reduce((sum, i) => sum + i.price, 0);
expect(calculateTotal(items)).toBe(expected);
});
// GOOD: Expected value is an independent, known literal
test("calculateTotal sums line items", () => {
expect(calculateTotal([{ price: 10 }, { price: 5 }])).toBe(15);
});
```

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---
name: to-spec
description: Turn the current conversation into a spec and write it to .claude/spec/ — no interview, just synthesis of what you've already discussed.
disable-model-invocation: true
---
This skill takes the current conversation context and codebase understanding and produces a spec. Do NOT interview the user — just synthesize what you already know.
If the conversation doesn't actually contain a feature or problem to synthesize a spec from, say so and ask what it's for instead of fabricating one.
## Process
1. Explore the repo until you can name the existing modules, flows, and seams the feature will touch, 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. Derive a short kebab-case feature-slug from the feature's name (e.g. `checkout-flow`). Tell the user the path you're about to write to (`.claude/spec/<feature-slug>.md`). If a file already exists there, summarize what would change and confirm with the user before overwriting it — never overwrite silently.
4. Write the spec using the format in [SPEC-FORMAT.md](./SPEC-FORMAT.md) to `.claude/spec/<feature-slug>.md`, creating the `.claude/spec/` directory if it doesn't exist yet.

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# Spec Format
## Template
```md
## Problem Statement
The problem that the user is facing, from the user's perspective.
## Solution
The solution to the problem, from the user's perspective.
## User Stories
An extensive, numbered list of user stories, covering all aspects of the feature. Each user story should be in the format of:
1. As an <actor>, I want a <feature>, so that <benefit>
<user-story-example>
1. As a mobile bank customer, I want to see balance on my accounts, so that I can make better informed decisions about my spending
</user-story-example>
## Implementation Decisions
A list of implementation decisions that were made. This can include:
- The modules that will be built/modified
- The interfaces of those modules that will be modified
- Technical clarifications from the developer
- Architectural decisions
- Schema changes
- API contracts
- Specific interactions
## Testing Decisions
A list of testing decisions that were made. Include:
- A description of what makes a good test (only test external behavior, not implementation details)
- Which modules will be tested
- Prior art for the tests (i.e. similar types of tests in the codebase)
## Out of Scope
A description of the things that are out of scope for this spec.
## Further Notes
Any further notes about the feature.
```
## Rules
- **Don't include specific file paths or code snippets.** They may end up being outdated very quickly. Exception: if a prototype produced a snippet that encodes a decision more precisely than prose can (state machine, reducer, schema, type shape), inline it within the relevant decision in Implementation Decisions and note briefly that it came from a prototype. Trim to the decision-rich parts — not a working demo, just the important bits.

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---
name: to-tasks
description: Break a plan or spec into independently-grabbable task files under .claude/tasks/ using tracer-bullet vertical slices.
disable-model-invocation: true
---
# To Tasks
## Process
### 1. Gather context
Work from whatever is already in the conversation context. If the user passes a spec path or other reference as an argument, read it directly.
Determine the feature-slug this breakdown belongs to, if any: if a spec file is in context or was passed as an argument, derive it from the filename (`.claude/spec/<feature-slug>.md``<feature-slug>`) for each task's `spec` field — see [TASK-FORMAT.md](./TASK-FORMAT.md) for the field's rules. If no spec file exists, proceed without one.
### 2. Explore the codebase (optional)
If you have not already explored the codebase, do so to understand the current state of the code. Task titles and descriptions should use the project's domain glossary vocabulary, and respect ADRs in the area you're touching.
Look for opportunities to prefactor the code to make the implementation easier. "Make the change easy, then make the easy change."
### 3. Draft vertical slices
Break the plan into **tracer bullet** tasks — vertical slices, not horizontal layers.
<vertical-slice-rules>
- Each slice delivers a narrow but COMPLETE path through every layer the change requires (schema, API, UI, tests), never a horizontal slice of just one
- A completed slice is demoable or verifiable on its own
- Any prefactoring should be done first
</vertical-slice-rules>
### 4. Quiz the user
Number slices with a single sequence shared across every file already in `.claude/tasks/`: scan for the highest existing `NNNN` (four-digit, zero-padded lowercase hex, `0000`-`ffff`) and increment from there. Never restart the sequence per feature and never reuse a number.
Present the proposed breakdown as a numbered list. For each slice, show:
- **File**: the `NNNN-slice-slug` it will be written as, per the numbering above
- **Blocked by**: which other slices (if any) must complete first — "None" if it can start immediately
- **User stories covered**: which user stories this addresses (if the source material has them)
Ask the user:
- Does the granularity feel right? (too coarse / too fine)
- Are the dependency relationships correct?
- Should any slices be merged or split further?
Iterate until the user approves the breakdown, including the proposed numbers and slugs.
### 5. Write the task files
For each approved slice, write a file to `.claude/tasks/<NNNN>-<slice-slug>.md` (create the directory if it doesn't exist) using the numbers and slugs approved in step 4. Use the template in [TASK-FORMAT.md](./TASK-FORMAT.md).
Do NOT modify the parent spec file (`.claude/spec/<feature-slug>.md`) when writing tasks.

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# Task Format
## Template
```md
---
spec: <feature-slug>
blocked-by: <slice-slug-or-list>
---
## What to build
A concise description of this vertical slice. Describe the end-to-end behavior, not layer-by-layer implementation.
## Acceptance criteria
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3
```
## Rules
- **`spec`**: the feature-slug this task was written from. Omit the field entirely if there's no spec.
- **`blocked-by`**: which other task(s) must complete before this one can start. Omit the field entirely if there are none. Each value is the blocking task's full `<NNNN>-<slice-slug>` filename stem, not just its slug. A single blocker is a bare string (`blocked-by: 000a-add-schema`); more than one is a YAML list (`blocked-by: [000a-add-schema, 000b-wire-api]`). Once written, keep the field even after the referenced task is completed — it's a permanent record of the dependency, not a "still blocked" flag.
- **Don't include specific file paths or code snippets** in "What to build" — they go stale fast. Exception: if a prototype produced a snippet that encodes a decision more precisely than prose can (state machine, reducer, schema, type shape), inline it here and note briefly that it came from a prototype. Trim to the decision-rich parts — not a working demo, just the important bits.
- A task is done when every criterion in "Acceptance criteria" is resolved: mark `[x]` as satisfied, or `[-]` if deliberately dropped (`/implement` records the reason in the task's Implementation Notes) — track completion here, not anywhere else.
- A slice becomes pickable once every task named in `blocked-by` is done (all of its acceptance criteria resolved) — check the referenced tasks' state, not just whether the field is present. The file's number is an identifier and a rough ordering hint, not a strict gate — sibling slices with no blockers can be worked in parallel.

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@@ -128,6 +128,17 @@ Tests live at `~/.config/dot/tests/dot.fish`, run with
(`-eq`, `-ne`, `=`, `-e`, `-f`, `-d`, `-n`, `-z`); `-a`/`-o` combinators (`-eq`, `-ne`, `=`, `-e`, `-f`, `-d`, `-n`, `-z`); `-a`/`-o` combinators
aren't supported. aren't supported.
## Gotchas
- `~/.claude/` (Claude Code's own config: skills, agents, commands, etc.) is
a plain directory, not a separate git repo of its own — plain `git` commands
run from inside it report "not a git repository". It's tracked the same way
as everything else under `$HOME`: through the `dot` bare repo. Use
`dot add`/`dot status` on paths under `~/.claude/`, not a `git` invocation
scoped to that directory, and don't assume an unrelated repo (e.g. a
separate skills-source checkout elsewhere) is the tracked copy just because
it also holds a copy of the same files.
## Keybindings ## Keybindings
Whenever a keybind is added, changed, or removed in *any* config on this Whenever a keybind is added, changed, or removed in *any* config on this