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42 changes: 42 additions & 0 deletions .agents/config/domain.md
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# Domain Docs

How the engineering skills should consume this repo's domain documentation when
exploring the codebase.

## Before exploring, read these

- **`CONTEXT.md`** at the repo root.
- **`docs/adr/`** - read ADRs that touch the area you're about to work in.

If any of these files don't exist, **proceed silently**. Don't flag their
absence; don't suggest creating them upfront. The producer skill
(`/grill-with-docs`) creates them lazily when terms or decisions actually get
resolved.

## File structure

Single-context repo:

```text
/
|-- CONTEXT.md
|-- docs/adr/
| |-- 0001-event-sourced-orders.md
| `-- 0002-postgres-for-write-model.md
`-- src/
```

## Use the glossary's vocabulary

When your output names a domain concept in an issue title, a refactor proposal,
a hypothesis, or a test name, use the term as defined in `CONTEXT.md`. Don't
drift to synonyms the glossary explicitly avoids.

If the concept you need isn't in the glossary yet, that's a signal: either
you're inventing language the project doesn't use, or there's a real gap to note
for `/grill-with-docs`.

## Flag ADR conflicts

If your output contradicts an existing ADR, surface it explicitly rather than
silently overriding it.
29 changes: 29 additions & 0 deletions .agents/config/issue-tracker.md
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# Issue tracker: GitHub

Issues and PRDs for this repo live as GitHub issues. Use the `gh` CLI for all
operations.

## Conventions

- **Create an issue**: `gh issue create --title "..." --body "..."`. Use a
heredoc for multi-line bodies.
- **Read an issue**: `gh issue view <number> --comments`, filtering comments by
`jq` and also fetching labels.
- **List issues**:
`gh issue list --state open --json number,title,body,labels,comments --jq '[.[] | {number, title, body, labels: [.labels[].name], comments: [.comments[].body]}]'`
with appropriate `--label` and `--state` filters.
- **Comment on an issue**: `gh issue comment <number> --body "..."`
- **Apply / remove labels**: `gh issue edit <number> --add-label "..."` /
`--remove-label "..."`
- **Close**: `gh issue close <number> --comment "..."`

Infer the repo from `git remote -v` - `gh` does this automatically when run
inside a clone.

## When a skill says "publish to the issue tracker"

Create a GitHub issue.

## When a skill says "fetch the relevant ticket"

Run `gh issue view <number> --comments`.
17 changes: 17 additions & 0 deletions .agents/config/triage-labels.md
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# Triage Labels

The skills speak in terms of five canonical triage roles. This file maps those
roles to the actual label strings used in this repo's issue tracker.

| Label in mattpocock/skills | Label in our tracker | Meaning |
| -------------------------- | -------------------- | ---------------------------------------- |
| `needs-triage` | `needs-triage` | Maintainer needs to evaluate this issue |
| `needs-info` | `needs-info` | Waiting on reporter for more information |
| `ready-for-agent` | `ready-for-agent` | Fully specified, ready for an AFK agent |
| `ready-for-human` | `ready-for-human` | Requires human implementation |
| `wontfix` | `wontfix` | Will not be actioned |

When a skill mentions a role, use the corresponding label string from this
table.

Edit the right-hand column to match whatever vocabulary you actually use.
117 changes: 117 additions & 0 deletions .agents/skills/diagnose/SKILL.md
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---
name: diagnose
description: Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
---

# Diagnose

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

When exploring the codebase, use the project's domain glossary to get a clear mental model of the relevant modules, 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 fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause — bisection, hypothesis-testing, and instrumentation all just consume that signal. 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

1. **Failing test** at whatever seam reaches the bug — unit, integration, e2e.
2. **Curl / HTTP script** against a running dev server.
3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.
4. **Headless browser script** (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
5. **Replay a captured trace.** Save a real network request / payload / event log to disk; replay it through the code path in isolation.
6. **Throwaway harness.** Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
7. **Property / fuzz loop.** If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
8. **Bisection harness.** If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can `git bisect run` it.
9. **Differential loop.** Run the same input through old-version vs new-version (or two configs) and diff outputs.
10. **HITL bash script.** Last resort. If a human must click, drive _them_ with `scripts/hitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.

Build the right feedback loop, and the bug is 90% fixed.

### Iterate on the loop itself

Treat the loop as a product. Once you have _a_ loop, ask:

- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
- Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)

A 30-second flaky loop is barely better than no loop. A 2-second deterministic loop is a debugging superpower.

### Non-deterministic bugs

The goal is not a clean repro but a **higher reproduction rate**. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not — keep raising the rate until it's debuggable.

### When you genuinely cannot build a loop

Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do **not** proceed to hypothesise without a loop.

Do not proceed to Phase 2 until you have a loop you believe in.

## Phase 2 — Reproduce

Run the loop. Watch the bug appear.

Confirm:

- [ ] The loop produces the failure mode the **user** described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
- [ ] The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
- [ ] You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.

Do not proceed until you reproduce the bug.

## Phase 3 — Hypothesise

Generate **3–5 ranked hypotheses** before testing any of them. Single-hypothesis generation anchors on the first plausible idea.

Each hypothesis must be **falsifiable**: state the prediction it makes.

> Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."

If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.

**Show the ranked list to the user before testing.** They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK.

## Phase 4 — Instrument

Each probe must map to a specific prediction from Phase 3. **Change one variable at a time.**

Tool preference:

1. **Debugger / REPL inspection** if the env supports it. One breakpoint beats ten logs.
2. **Targeted logs** at the boundaries that distinguish hypotheses.
3. Never "log everything and grep".

**Tag every debug log** with a unique prefix, e.g. `[DEBUG-a4f2]`. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.

**Perf branch.** For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, `performance.now()`, profiler, query plan), then bisect. Measure first, fix second.

## Phase 5 — Fix + regression test

Write the regression test **before the fix** — but only if there is a **correct seam** for it.

A correct seam is one where the test exercises the **real bug pattern** as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.

**If no correct seam exists, that itself is the finding.** Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.

If a correct seam exists:

1. Turn the minimised repro into a failing test at that seam.
2. Watch it fail.
3. Apply the fix.
4. Watch it pass.
5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.

## Phase 6 — Cleanup + post-mortem

Required before declaring done:

- [ ] Original repro no longer reproduces (re-run the Phase 1 loop)
- [ ] Regression test passes (or absence of seam is documented)
- [ ] All `[DEBUG-...]` instrumentation removed (`grep` the prefix)
- [ ] Throwaway prototypes deleted (or moved to a clearly-marked debug location)
- [ ] The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns

**Then ask: what would have prevented this bug?** If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the `/improve-codebase-architecture` skill with the specifics. Make the recommendation **after** the fix is in, not before — you have more information now than when you started.
41 changes: 41 additions & 0 deletions .agents/skills/diagnose/scripts/hitl-loop.template.sh
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#!/usr/bin/env bash
# Human-in-the-loop reproduction loop.
# Copy this file, edit the steps below, and run it.
# The agent runs the script; the user follows prompts in their terminal.
#
# Usage:
# bash hitl-loop.template.sh
#
# Two helpers:
# step "<instruction>" → show instruction, wait for Enter
# capture VAR "<question>" → show question, read response into VAR
#
# At the end, captured values are printed as KEY=VALUE for the agent to parse.

set -euo pipefail

step() {
printf '\n>>> %s\n' "$1"
read -r -p " [Enter when done] " _
}

capture() {
local var="$1" question="$2" answer
printf '\n>>> %s\n' "$question"
read -r -p " > " answer
printf -v "$var" '%s' "$answer"
}

# --- edit below ---------------------------------------------------------

step "Open the app at http://localhost:3000 and sign in."

capture ERRORED "Click the 'Export' button. Did it throw an error? (y/n)"

capture ERROR_MSG "Paste the error message (or 'none'):"

# --- edit above ---------------------------------------------------------

printf '\n--- Captured ---\n'
printf 'ERRORED=%s\n' "$ERRORED"
printf 'ERROR_MSG=%s\n' "$ERROR_MSG"
47 changes: 47 additions & 0 deletions .agents/skills/grill-with-docs/ADR-FORMAT.md
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# ADR Format

ADRs live in `docs/adr/` and use sequential numbering: `0001-slug.md`, `0002-slug.md`, etc.

Create the `docs/adr/` directory lazily — only when the first ADR is needed.

## Template

```md
# {Short title of the decision}

{1-3 sentences: what's the context, what did we decide, and why.}
```

That's it. An ADR can be a single paragraph. The value is in recording *that* a decision was made and *why* — not in filling out sections.

## Optional sections

Only include these when they add genuine value. Most ADRs won't need them.

- **Status** frontmatter (`proposed | accepted | deprecated | superseded by ADR-NNNN`) — useful when decisions are revisited
- **Considered Options** — only when the rejected alternatives are worth remembering
- **Consequences** — only when non-obvious downstream effects need to be called out

## Numbering

Scan `docs/adr/` for the highest existing number and increment by one.

## When to offer an ADR

All three of these must be true:

1. **Hard to reverse** — the cost of changing your mind later is meaningful
2. **Surprising without context** — a future reader will look at the code and wonder "why on earth did they do it this way?"
3. **The result of a real trade-off** — there were genuine alternatives and you picked one for specific reasons

If a decision is easy to reverse, skip it — you'll just reverse it. If it's not surprising, nobody will wonder why. If there was no real alternative, there's nothing to record beyond "we did the obvious thing."

### What qualifies

- **Architectural shape.** "We're using a monorepo." "The write model is event-sourced, the read model is projected into Postgres."
- **Integration patterns between contexts.** "Ordering and Billing communicate via domain events, not synchronous HTTP."
- **Technology choices that carry lock-in.** Database, message bus, auth provider, deployment target. Not every library — just the ones that would take a quarter to swap out.
- **Boundary and scope decisions.** "Customer data is owned by the Customer context; other contexts reference it by ID only." The explicit no-s are as valuable as the yes-s.
- **Deliberate deviations from the obvious path.** "We're using manual SQL instead of an ORM because X." Anything where a reasonable reader would assume the opposite. These stop the next engineer from "fixing" something that was deliberate.
- **Constraints not visible in the code.** "We can't use AWS because of compliance requirements." "Response times must be under 200ms because of the partner API contract."
- **Rejected alternatives when the rejection is non-obvious.** If you considered GraphQL and picked REST for subtle reasons, record it — otherwise someone will suggest GraphQL again in six months.
77 changes: 77 additions & 0 deletions .agents/skills/grill-with-docs/CONTEXT-FORMAT.md
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# CONTEXT.md Format

## Structure

```md
# {Context Name}

{One or two sentence description of what this context is and why it exists.}

## Language

**Order**:
{A concise description of the term}
_Avoid_: Purchase, transaction

**Invoice**:
A request for payment sent to a customer after delivery.
_Avoid_: Bill, payment request

**Customer**:
A person or organization that places orders.
_Avoid_: Client, buyer, account

## Relationships

- An **Order** produces one or more **Invoices**
- An **Invoice** belongs to exactly one **Customer**

## Example dialogue

> **Dev:** "When a **Customer** places an **Order**, do we create the **Invoice** immediately?"
> **Domain expert:** "No — an **Invoice** is only generated once a **Fulfillment** is confirmed."

## Flagged ambiguities

- "account" was used to mean both **Customer** and **User** — resolved: these are distinct concepts.
```

## Rules

- **Be opinionated.** When multiple words exist for the same concept, pick the best one and list the others as aliases to avoid.
- **Flag conflicts explicitly.** If a term is used ambiguously, call it out in "Flagged ambiguities" with a clear resolution.
- **Keep definitions tight.** One sentence max. Define what it IS, not what it does.
- **Show relationships.** Use bold term names and express cardinality where obvious.
- **Only include terms specific to this project's context.** General programming concepts (timeouts, error types, utility patterns) don't belong even if the project uses them extensively. Before adding a term, ask: is this a concept unique to this context, or a general programming concept? Only the former belongs.
- **Group terms under subheadings** when natural clusters emerge. If all terms belong to a single cohesive area, a flat list is fine.
- **Write an example dialogue.** A conversation between a dev and a domain expert that demonstrates how the terms interact naturally and clarifies boundaries between related concepts.

## Single vs multi-context repos

**Single context (most repos):** One `CONTEXT.md` at the repo root.

**Multiple contexts:** A `CONTEXT-MAP.md` at the repo root lists the contexts, where they live, and how they relate to each other:

```md
# Context Map

## Contexts

- [Ordering](./src/ordering/CONTEXT.md) — receives and tracks customer orders
- [Billing](./src/billing/CONTEXT.md) — generates invoices and processes payments
- [Fulfillment](./src/fulfillment/CONTEXT.md) — manages warehouse picking and shipping

## Relationships

- **Ordering → Fulfillment**: Ordering emits `OrderPlaced` events; Fulfillment consumes them to start picking
- **Fulfillment → Billing**: Fulfillment emits `ShipmentDispatched` events; Billing consumes them to generate invoices
- **Ordering ↔ Billing**: Shared types for `CustomerId` and `Money`
```

The skill infers which structure applies:

- If `CONTEXT-MAP.md` exists, read it to find contexts
- If only a root `CONTEXT.md` exists, single context
- If neither exists, create a root `CONTEXT.md` lazily when the first term is resolved

When multiple contexts exist, infer which one the current topic relates to. If unclear, ask.
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