release 1.0.0: cost control, 41 tools, 29 skills, custom commands, auto-load

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Muhammad Zakir Ramadhan committed 2026-09-07 19:59:58 +07:00
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---
name: accessibility
description: Make a UI accessible. Use when adding a feature that must work with a keyboard or screen reader, fixing contrast or focus issues, or reviewing for WCAG.
---
# Accessibility
Accessibility is usability for everyone, including people using a keyboard, a screen reader, a
magnifier, or a noisy display. Build it in, not on.
## Semantic HTML does the heavy lifting
A `<button>`, `<a>`, `<input>`, `<nav>`, `<main>` carries behaviour and meaning for free
that a `<div>` with a click handler does not. Reach for the native element first; add ARIA only
when no native element fits. The first rule of ARIA is do not use ARIA if a native element exists.
## Keyboard is the baseline
- Every interactive element is reachable and operable with Tab and Enter/Space alone.
- A visible focus indicator on everything — never `outline: none` without a replacement.
- Logical tab order following the visual order, and focus managed into and out of modals,
menus, and dialogs (trapped while open, returned to the trigger on close).
## Screen readers hear structure
- Headings in order (`h1` once, then down a level at a time) so the page has a navigable outline.
- Every `<input>` has a `<label>`; every icon-only button has an accessible name; every image
has alt text that conveys its point (or empty alt when it is purely decorative).
- Dynamic changes announce themselves: a toast, an error, a loaded region uses a live region so
it is heard, not just seen.
## Contrast and meaning
Text meets 4.5:1 against its background (3:1 for large text). Colour is never the only carrier
of meaning — pair it with an icon, a label, or a pattern. A red-only "error" is invisible to a
colour-blind user.
## Test it the way it is used
Tab through the whole flow. Turn on a screen reader and listen. Zoom to 200% and 400%. Run an
automated checker for the mechanical half — then do the manual half it cannot cover, because
most accessibility failures are not machine-detectable.
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---
name: api-design
description: Design or revise an HTTP or library API. Use when adding an endpoint, shaping request/response bodies, naming resources, or reviewing an API for consistency.
---
# API design
An API is a contract. Every choice is a promise you cannot take back without a major version.
## Resource before action
Name things, not verbs. `POST /users` to create, not `POST /createUser`. The URL is the
noun; the method is the verb. When you reach for a verb in the path, that is a sign the
resource is missing — `POST /users/:id/deactivations` reads better than `/deactivateUser`
when the operation has state of its own.
## Shape the body for the reader
- Field names are `snake_case` or `camelCase`, picked once for the whole API. A body that
mixes both is a body nobody documented.
- Return the object, not a wrapper, unless the wrapper carries something: `{ "user": {...} }`
only when there is also pagination, a cursor, or an error envelope.
- Errors have a stable shape: a machine-readable `code`, a human `message`, and the field
that failed. A client should never have to parse the message.
## Status codes mean something
- `201` for a created resource, with the resource in the body.
- `204` for success with nothing to return.
- `400` for a body that failed validation, `401` unauthenticated, `403` authenticated but
not allowed, `404` not found or not allowed to know, `409` a conflict with current state,
`422` well-formed but semantically wrong.
- Never `200` with an error in the body. A client checking only the status will treat it as
success.
## Idempotency and safety
GET, PUT, DELETE must be safe to retry: same request, same state. POST is not. If a client can
double-submit, provide an idempotency key or a natural unique constraint, and say which.
## Version when you must, not before
Add fields freely; removing or renaming is a break. If you are not yet committed, say so with
a `beta` or `v0` marker rather than locking a shape you have not used. Document the contract
you guarantee, not the implementation that happens to produce it.
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---
name: ci-cd
description: Write or repair CI/CD pipelines and workflow files. Use when a build fails in CI but not locally, when adding a workflow, or when caching, matrix, or deploy steps need design.
---
# CI/CD
CI is a second machine that does not have your setup. "Works on my machine" means the pipeline
is missing something your machine has.
## Reproduce the environment, not the symptom
When CI fails and local passes, the difference is the environment: the toolchain version, an
uncommitted file, a cache, an env var, the OS. Diff those before touching the code. Read the
failing log literally — the first error, not the last, which is usually a downstream echo.
## Pin everything that can move
- Toolchain versions (`node`, `bun`, `python`), action versions, base images. `latest`
is a build that breaks on a day you did nothing.
- Lockfiles go in the repo and the install respects them (`--frozen-lockfile`, `ci`). An
install that re-resolves in CI is a different build from the one you tested.
## Cache the expensive, deterministic part
Dependencies are the cache; build output usually is not. Key the cache on the lockfile hash so
a changed dependency invalidates it. A cache that is too broad serves stale artifacts; too
narrow saves nothing.
## Fail fast, in the right order
Cheap checks first: lint and typecheck before the test matrix, tests before the deploy. A
pipeline that deploys before it verifies publishes the bug it was built to catch.
## Secrets and deploys
Secrets live in the CI secret store, never in the file, and are masked in logs. A deploy step
is gated: on a tag, on a protected branch, on a manual approval — never on every push. Assume
every log line is public and write the pipeline accordingly.
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---
name: commit
description: Stage and commit work. Use when asked to commit, or to split existing changes into commits.
---
# Committing
Never commit unless the user asked. If it is unclear whether they did, ask. A commit is a
durable statement about shared history, not a save-point.
## Look before you stage
`git_status` and `git_diff` first — read the whole diff you are about to commit. You are
looking for three things:
1. **Changes that are not yours.** Another agent or the user may share this worktree, and
`git add .` takes their half-finished work with yours.
2. **Files that should never be committed:** `.env`, credentials, keys, large build
output, anything a `.gitignore` rule was supposed to catch and did not. Flag these to
the user rather than committing them — a committed secret is a secret to rotate.
3. **Your own accidents:** debug prints, commented-out code, a stray `TODO`, a file you
opened and saved by mistake. Revert them before staging, not in a follow-up commit.
Stage the specific paths you changed. `git add .` is how unrelated work ends up in a
commit that then has to be reverted whole.
## One commit, one reason
If the diff does two unrelated things, make two commits. A commit that both fixes a bug
and renames a module cannot be reverted, cherry-picked, or bisected usefully. Each commit
should pass the tests on its own — a series of broken commits defeats `git bisect`.
## The message
Match the repository's existing style — read `git_log` before writing one. Failing that:
- A subject line under 70 characters, imperative mood, saying what changed: "Fix off-by-one
in pagination", not "fixed a bug" or "changes".
- A body explaining *why* when the reason is not obvious from the diff. Wrap at 72.
- No "as requested", no restating the diff line by line, no emoji unless the repo uses them,
no sign-off noise the repo does not already use.
## Do not
- Do not `--amend` a commit that has been pushed. Write a new one.
- Do not `--no-verify`. If a hook rejects the commit, the hook found something — read it.
- Do not `git push` unless asked, and never force-push without being asked explicitly.
- Do not commit and then immediately fix it up with a second commit. Get it right, or say
what is wrong.
## After committing
Report the short hash and the subject. If a hook rewrote files, say so, and confirm the
final state — `git_status` again — is what was intended.
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---
name: data
description: Process, validate, or transform data. Use when parsing files, cleaning datasets, designing a data pipeline, or debugging a transform that produces wrong output.
---
# Data
Bad data fails silently and far away from where it entered. Validate at the boundary, keep the
raw, and make every transform checkable.
## Validate at the boundary
Parse and validate when data enters the system, not when it is used. A schema check at the edge
turns a corrupt record into a clear rejection; skipping it turns the same record into a wrong
answer three layers later. Reject loudly, with the record and the reason — never coerce and
carry on.
## Keep the raw
Store the untransformed input alongside the derived. When a transform turns out to be wrong,
the raw lets you recompute; without it, the information is gone. Derived data is rebuildable;
source data is not.
## Transformations are pure and tested
A transform takes input and returns output with no hidden state, so it can be tested on a
fixture and re-run safely. Test the edge cases that actually occur in data: the empty field,
the wrong type, the unexpected null, the duplicate, the encoding that is not UTF-8.
## Duplicates, nulls, and ranges are the usual corruption
Check for: unexpected duplicates on a key, nulls where a value is required, values outside a
sane range (a negative age, a date in the future), and referential breaks (an id pointing at
nothing). These four catch most real-world data problems before they reach a report.
## Idempotent pipelines
A step that can be re-run without duplicating or corrupting its output is a step you can retry
after a failure. Key on a stable id and upsert rather than blind-insert. A pipeline you cannot
safely re-run is a pipeline you will one day have to fix by hand at 2am.
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---
name: db
description: Design schemas, write migrations, or fix query and data problems. Use when adding a table, writing a migration, debugging a slow query, or choosing keys and indexes.
---
# Databases
The schema is the hardest thing to change in the whole system. Design it for the queries, not
the object model.
## Keys and constraints are the real schema
- Every table has a primary key; prefer a surrogate `id` unless a natural key is truly stable.
- Foreign keys and `NOT NULL` are not optional decoration — they are the constraints that stop
bad data at the door instead of in application code six months later.
- Unique constraints belong on the thing that must be unique (email, slug), enforced by the
database, not by a check-then-insert that races.
## Migrations are one-way and additive where possible
- Never edit a migration that has run anywhere. Add a new one.
- Destructive changes (drop column, rename, change type) are two migrations: add the new shape,
deploy code that writes both, then remove the old in a later release. A single migration that
renames a column breaks every old copy of the app still running.
- Test a migration against real data volume. `ALTER` on ten rows is instant; on ten million it
locks the table.
## Indexes follow the queries
Index the columns you filter and join on, in the order the query uses them. A composite index
`(a, b)` serves `WHERE a` and `WHERE a, b` but not `WHERE b` alone. Read the query plan
(`EXPLAIN`) before adding one — a guess is an index that costs writes and serves nothing.
## The N+1 is the default bug
A query per row in a loop is the most common database performance defect. Fetch the set with a
join or a batched `WHERE id IN (...)`. If a page does one query per item, that is the fix
before any caching.
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---
name: debug
description: Track down a bug whose cause is not obvious. Use when a test fails for unclear reasons, behaviour differs between environments, or an earlier fix did not hold.
---
# Debugging
Do not guess. A guess that happens to work leaves the real cause in place, and it will
fire again — usually in production, usually at a worse time.
## Reproduce first
Find the smallest command that shows the failure and record it with `remember`. If you
cannot reproduce it, say so and ask what the user did differently — do not proceed on a
hypothesis you cannot test.
Shrink the reproduction until it is minimal: one input, one call, one assertion. Every
moving part you leave in is a place the bug can hide. A reproduction that takes thirty
steps will not get run often enough to confirm the fix.
## Three hypotheses, then evidence
Write down at least three causes that would produce this exact symptom — not "the code is
wrong" but specific mechanisms: "the offset is off by one when the page is empty", "the
cache is read before the write lands". Rank them by how cheap they are to disprove, then
disprove them in that order. State which one you are testing before you test it.
Evidence means observed output: a log line, a failing assertion, a value printed at the
point of failure. "It should be X" is not evidence. When the evidence contradicts your
favoured hypothesis, the hypothesis is wrong — do not explain the evidence away.
## Localise before you fix
Assert the value at each boundary until one is wrong. The bug lives between the last
boundary where the value is right and the first where it is wrong. Fixing before you have
that bracket means editing the wrong place and learning nothing.
## Bisect when the space is large
- Recent regression: `git bisect` or read what changed last. The bug arrived in a commit;
find which one.
- Unclear layer: assert the value at each boundary until one is wrong.
- Intermittent: run it in a loop and capture the failing case with full logging. Do not
reason about a race abstractly — make it happen on demand, then it is no longer
intermittent.
## Fix the cause, not the symptom
Once you know the cause, fix that and nothing else. Do not tidy surrounding code in the
same change — a bugfix diff should contain only the bug, so it can be reverted whole if it
is wrong.
Write a test that fails before the fix and passes after. Watch it fail first; a test you
never saw fail proves nothing. If you cannot express the bug as a test, say why — and say
what you ran instead to confirm the fix.
## After two failed attempts
Stop. Re-read the error text literally, character by character — most "impossible" bugs
are a misread message. Then check your assumption about which code is actually running:
the wrong file, a stale build, a shadowed import, a cached dependency, or an env var that
differs from your shell. Verify by printing something at the point you *think* executes;
if it does not print, that is your answer.
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---
name: deps
description: Manage dependencies: choosing, adding, updating, or removing them. Use when evaluating a library, resolving a version conflict, pruning unused deps, or hardening the supply chain.
---
# Dependencies
Every dependency is code you did not write but now maintain. Add deliberately, prune regularly.
## Choose on maintenance, not features
Before adding: is it actively maintained (recent commits, responsive issues), widely used, and
small enough to be worth it? A dependency that saves a day and is abandoned in a year costs a
week. For something small and stable, a dozen lines in your own codebase often beats a package.
## Pin and lock
Exact versions in the manifest for anything that matters, a lockfile committed, and installs
that respect it. A `^` range means your build tomorrow differs from your build today. The
lockfile is the build's memory; do not delete it to "fix" a conflict — resolve the conflict.
## Update on a schedule, read the changelog
Routine small updates beat a yearly painful one. For a major bump: read the changelog and the
migration guide, find every call site of the changed API, and apply one shape of change (see
the migrate skill). Update one thing at a time so a regression has an obvious cause.
## Know your transitive tree
A direct dependency drags in dozens of transitive ones. Audit the tree for: known
vulnerabilities (`audit`/SCA tooling), abandoned packages deep in it, and duplicate copies of
the same library at different versions bloating the bundle. Remove what you no longer use — an
unused dependency is attack surface and install time for nothing.
## Supply chain is a trust decision
A package runs its install scripts with your permissions. Prefer packages with provenance and a
reproducible build, be wary of sudden ownership transfers, and pin so a hijacked publish does
not reach you automatically. The lockfile is also your audit trail of exactly what shipped.
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---
name: docker
description: Write or fix Dockerfiles and container setups. Use when an image is too large, a build is slow, a container will not start, or layering and caching need design.
---
# Docker
An image is a build artifact. Small, reproducible, and boring is the goal.
## Layer cache is the whole speed game
Order instructions from least to most frequently changed: base image, then dependency
manifests, then `install`, then source copy, then build. Copying `.` before installing
dependencies means every code change re-runs the install — the single most common Dockerfile
mistake.
## Small images, on purpose
- Use multi-stage builds: build in a full toolchain stage, copy only the artifact into a slim
runtime stage. The compiler does not ship to production.
- Pick a slim or distroless base unless you need the tooling. Alpine is small but musl breaks
some binaries; know why you chose it.
- One `RUN` with `&&` for related steps, cleaning up in the same layer — a separate `RUN rm`
does not shrink the image, the data is still in the earlier layer.
## The container is not a VM
- One process per container, as PID 1, so signals work. Use an init if the app spawns children.
- Do not run as root. Add a user and `USER` it.
- Read-only filesystem where possible; write to a mounted volume for anything that must persist.
Nothing in the image is writable state.
## .dockerignore is as important as the Dockerfile
Exclude `.git`, `node_modules`, build output, and any secret file. A context that sends the
whole repo is slow, and a secret copied into an image layer is a secret to rotate.
## Healthcheck and logs
The process logs to stdout/stderr, never to a file inside the container — the runtime collects
it. Add a `HEALTHCHECK` that proves the service answers, not just that the process exists.
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---
name: docs
description: Write or update documentation, READMEs, and guides. Use when asked to document a feature, write usage docs, or bring docs back in line with the code.
---
# Documentation
Docs lie by omission. Write only what you have verified in the code.
## Ground every claim in the source
Before documenting a behaviour, read it. A flag, a default, an error message — open the
code and quote what it actually does, not what the name suggests. The most damaging doc
line is the confident one that was true two versions ago. If the code and the existing
docs disagree, the code is right; say so and fix the doc.
## Answer the reader's actual question
A reader opens a doc with a task, not a desire for completeness. Lead with the thing they
came to do, in the order they will do it:
- **A reference** lists what exists: every flag, every field, with its default and its type.
- **A guide** walks one path to one outcome. Resist documenting every branch — link instead.
- **A README** orients in sixty seconds: what it is, install, the first command that works.
## Show, then say
A working example beats a paragraph about one. Every command in the doc must be one you
ran, with its real output. A snippet that was never executed is a bug waiting for a reader.
## Match the house style
Read the neighbouring docs first: their heading depth, their code-fence language tags,
their tone. A doc that reads foreign is a doc nobody trusts enough to maintain.
## Keep it true over time
Document the stable contract, not the current implementation, unless the point is the
implementation. The fewer specifics a doc pins down, the less it rots.
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---
name: frontend
description: Build or fix a web UI. Use when working on components, state, rendering performance, forms, or anything the user sees and interacts with in a browser.
---
# Frontend
The user's experience is the metric. Fast, clear, and forgiving beats clever.
## State lives as low as it can
Lift state only as high as the components that share it. Global state for something two siblings
need is re-render and complexity for everything. Server data is not client state — cache it with
the data layer rather than duplicating it into a store you must keep in sync by hand.
## Rendering is the usual bottleneck
Before optimising, find what re-renders. A component that re-renders on every parent render
because of an inline object or function prop is the common case. Memoize the expensive subtree,
not everything — `useMemo` and `useCallback` have a cost too, and slapping them everywhere is
its own slowdown.
## Forms respect the user
- Validate on blur or submit, not on every keystroke, and show the message at the field.
- Never clear a form on an error. The user's input is the most expensive thing on the page.
- Disable the submit while submitting, and say what is happening. A double-submitted form is a
duplicate record.
## Accessibility is not a later pass
Semantic HTML first: a `<button>` that looks like a button beats a `<div>` with a click
handler. Keyboard-reachable everything, visible focus, labels on inputs, alt text that conveys
the point not the pixels. Colour is never the only carrier of meaning.
## Measure what the user feels
Load: get the first meaningful paint and the time-to-interactive down before micro-tuning.
Bundle: split the route nobody opens, lazy-load the heavy component. A Lighthouse number is a
proxy; the goal is that it never feels slow.
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---
name: git-workflow
description: Work with branches, rebases, merges, and history. Use when untangling a branch, preparing a PR, deciding rebase vs merge, or recovering from a git mistake.
---
# Git workflow
History is a communication tool. Write it for the person who reads it in six months — usually you.
## One branch, one purpose
A branch that does two things produces a PR that can only be reviewed as all-or-nothing and
reverted only whole. Keep it small and single-purpose; open the second thing as its own branch.
## Rebase to clean up, merge to preserve
- Rebase your own unpushed work freely: it makes a linear, readable history.
- Never rebase a branch others have pulled — it rewrites commits they have, and the next pull
becomes a mess. Merge shared branches instead.
- Interactive rebase before opening the PR: squash the "fix typo" and "wip" commits into the
change they belong to. The PR should read as a series of intentional steps, not a diary.
## Recover without panic
- `git reflog` finds almost anything you "lost": the branch you deleted, the commit you reset
away. Nothing committed is truly gone for ~30 days.
- A bad merge: `git merge --abort`. A bad rebase: `git rebase --abort`. Both stop cleanly
rather than pushing forward into a worse state.
- Committed to the wrong branch: `git reset --soft` to keep the work, switch, recommit.
## The commit message is the review's first page
Subject under 70 chars, imperative, says what changed. Body explains *why* when it is not
obvious. A reviewer who cannot tell why a change exists from its message will ask, or worse,
approve without understanding.
## Read the conflict, do not guess
On a conflict, open the file and understand both sides before resolving. Taking "ours" or
"theirs" wholesale because it is faster is how a resolved conflict silently drops someone's
work.
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---
name: i18n
description: Internationalise or localise a product. Use when extracting strings for translation, formatting dates and numbers for a locale, handling pluralisation, or fixing layout that breaks in another language.
---
# Internationalisation
Hard-coded English is a bug in every other language. Externalise strings and never assume a
grammar.
## Every user-facing string is a key
No string in the UI lives in code; it lives in a message catalogue under a key. Concatenating
translated fragments is the classic bug: "You have " + n + " messages" cannot be reordered for
a language whose grammar puts the number elsewhere. Use a format with named placeholders:
`{count, plural, ...}`, translated as a whole.
## Pluralisation and gender are not English
Languages have one, two, several, or no plural forms, with rules that do not map to "1 vs other".
Use the ICU plural machinery of your i18n library and let the translator fill in every form the
locale needs. The same goes for gendered agreement.
## Format dates, numbers, and currencies by locale
Never `dd/mm/yyyy` by hand: `03/04/2025` is March 4th to one user and April 3rd to another.
Use the platform's locale-aware formatter (`Intl.DateTimeFormat`, `Intl.NumberFormat`).
Store and transmit ISO 8601 / UTC; format for display only.
## Layout breaks in translation
German runs ~30% longer than English; some scripts are right-to-left. Flexible layout, no fixed
widths on translated text, and CSS logical properties (`margin-inline-start` not
`margin-left`) so RTL mirrors correctly. Test with a pseudo-locale that lengthens and accents
every string to find the overflows before a translator does.
## Sort and search correctly
String order is locale-dependent: `ä` sorts with `a` in German, after `z` in Swedish. Use
locale-aware collation (`Intl.Collator` or the database's) rather than byte order, and normalise
Unicode before comparing, because the same character has more than one byte representation.
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---
name: incident
description: Respond to a production incident. Use when something is down, degraded, or misbehaving in production and must be diagnosed and mitigated under time pressure.
---
# Incident response
Mitigate first, diagnose second. Restore service, then find out why.
## Confirm and scope before touching anything
What is actually broken, for whom, since when? Check the signal, not the report: the dashboard,
the error rate, the health endpoint. A wrong scope sends you chasing a symptom. State the impact
plainly in one line before you start changing things.
## Recent change is the prime suspect
Most incidents follow a deploy, a config change, a flag flip, or a scaling event. What changed
in the window before it broke? Check the deploy log and the diff. The fastest fix is usually to
undo the last change, not to understand it.
## Mitigate, then understand
- Roll back the deploy, flip the flag off, fail over, scale up, restart the wedged process —
whichever restores service fastest, even if you do not yet know the root cause.
- A mitigation you can reverse beats a perfect diagnosis that takes an hour. Note what you did so
it can be undone or made permanent later.
- Do not deploy an unreviewed "fix" into the fire; it adds a second change to a system already
misbehaving.
## Preserve evidence before it rotates away
Capture the logs, the error, the relevant metrics, a snapshot of the state — before a restart or
a rollback destroys it. You will want it for the postmortem, and it may be the only copy.
## Communicate and follow up
Say what is broken, what you are doing, and when the next update is — to whoever is affected,
in plain language, on a schedule. Afterwards: write the timeline, the root cause, and the
follow-ups that stop it recurring. An incident with no follow-up is a loan against the next one.
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---
name: logging
description: Add or improve logging and observability. Use when debugging in production, adding structured logs, choosing log levels, or making a system traceable.
---
# Logging
Logs are how you debug a system you cannot attach a debugger to. Write them for the 3am
incident, not the happy path.
## Structure over prose
Emit fields, not sentences: `{ user: id, action: "checkout", ms: 142, ok: false }`, not
`"User checked out"`. Structured logs are searchable and aggregable; a sentence is neither.
One event, one line, one level.
## Levels are a contract
- `error` — something is broken and someone should look. Not "a user gave bad input".
- `warn` — unexpected but handled; worth a glance.
- `info` — the meaningful state transitions: started, finished, the decision made. Sparse.
- `debug` — everything you might want while diagnosing, off in production.
A log at the wrong level trains people to ignore the right one. If everything is `error`,
nothing is.
## Log the decision points, not every line
At a boundary — a request in, a call out, a branch taken — log what was decided and the inputs
that decided it, with a correlation id that follows the request across services. You should be
able to trace one request end to end from the id alone.
## Never log a secret
No passwords, tokens, session ids, full card numbers, or personal data beyond what policy
allows. Redact at the point of logging, not by hoping a downstream filter catches it. A secret
in a log aggregator is a secret to rotate.
## Measure, do not just log
For anything with a latency or a rate, a metric answers "is it slow?" faster than a thousand
log lines. Logs explain *why*; metrics tell you *that* something is wrong in the first place.
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---
name: migrate
description: Upgrade a dependency, framework, or language version across a codebase. Use when a major version bump, a deprecation, or a breaking API change has to be applied.
---
# Migration
The failure mode is a half-applied migration: it compiles, most tests pass, and one code
path still uses the old API.
## Read the changelog before the code
Find what actually broke. A major version usually has a migration guide; read it and list
the changes that apply to this codebase specifically. Below 1.0, treat a minor bump as
breaking — semver promises nothing there.
## Find every call site before changing one
Grep for the old API across the whole repository, including tests, scripts, config, CI
workflows, Dockerfiles, and documentation. A version literal pinned in a workflow while the
manifest says something else is a split-brain deploy.
Write the list down with `todo_write`. The list is the migration; the edits are mechanical.
## Change in one shape
Apply the same transformation everywhere rather than improving each site as you pass
through it. A migration mixed with refactoring cannot be reviewed, and cannot be reverted
if the upgrade turns out to be wrong.
`apply_patch` is the tool for this: one atomic patch across the files that must land
together.
## Verify at the boundary that broke
Type checks catch signature changes and miss behaviour changes — the two ways a migration
actually breaks you. Run the tests, then actually *use* the thing that was upgraded: start
the server, run the CLI, execute the query, hit the endpoint. A green suite over an
untested upgrade path proves only that the suite did not cover it.
Pay special attention to silent behaviour changes: a default that flipped, a deprecated
call that still runs but does something subtly different, an error type that changed shape.
These compile, pass type checks, and still break production.
## Never hand-merge a lockfile
On a conflict, take either side whole and regenerate with the package manager. The resolver
owns that file; a hand-merge is a split-brain dependency tree that installs differently on
every machine.
## Report
The version before and after, every file class touched, what you verified by running, the
behaviour changes you checked by hand, and anything the changelog said applies that you
deliberately did not do — with the reason.
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---
name: onboarding
description: Orient in an unfamiliar codebase. Use when dropped into a new project and asked to understand it, or when writing the docs that help a newcomer get productive.
---
# Onboarding to a codebase
Understand the running system before the source. The goal is a correct mental model, not to have
read every file.
## Get it running first
Build it, run it, run the tests. A project you can execute you can interrogate; one you have
only read you can only guess at. The README and the `package.json`/`Makefile` scripts tell you
the intended commands; if they do not work, that is your first finding.
## Trace one request end to end
Pick the central thing the system does and follow it: the entry point, the route or main, the
handler, the data out and back. One full path teaches you the architecture faster than reading
any single module. Note the layers you cross — that is the system's real structure.
## Read the structure, not the files
- The directory layout names the major components and their boundaries.
- The dependency manifest names the frameworks and the big choices already made.
- The tests show what the code is supposed to do, often better than the code does.
- `git log` on a core file shows what changes often and why — the living parts versus the
stable ones.
## Map the seams
Where does data enter and leave (HTTP, a queue, a file)? Where is state kept (a database,
memory, a cache)? Where are the trust boundaries? Those are the places bugs and features both
live. You do not need to know every file; you need to know where a change of a given kind would
go.
## Ask the codebase, then a person
Grep and the outline/symbol tools answer most "where is X" faster than reading. When genuinely
stuck on *why* something exists — that is a question for a person or the history, not more
reading.
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---
name: optimize-sql
description: Diagnose and fix a slow SQL query. Use when a query is slow, a page makes too many queries, or an execution plan needs reading.
---
# SQL optimisation
Read the plan before changing anything. `EXPLAIN` (or `EXPLAIN ANALYZE`) tells you what the
database actually does; guessing at it is how you add an index that helps nothing.
## Read the plan for the expensive node
Find the node with the highest cost: a sequential scan over a large table, a nested loop over
many rows, a sort that spills to disk. Optimise that node. A plan with ten cheap nodes and one
expensive one has exactly one thing to fix.
## The index that matches the query
- Index the columns in the `WHERE` and `JOIN` clauses, and for a sort, the `ORDER BY`.
- A composite index `(a, b, c)` serves a leftmost prefix: `a`, `a,b`, `a,b,c` — not
`b` alone. Order the columns by the equality filters first, then the range, then the sort.
- A covering index includes every column the query reads, so the table is never touched. That
is the fastest a read gets.
## Write the query so the index is usable
- `WHERE lower(email) = ...` cannot use a plain index on `email`; either store it lowered or
use a functional index. A function on the column defeats the index.
- Leading `LIKE '%x'` cannot use a B-tree index; `LIKE 'x%'` can.
- `OR` across different columns often defeats an index; `UNION` of two indexed queries can be
faster.
## Kill the N+1 first
Before any index: if the page runs one query per row, that is the fix. Batch with
`WHERE id IN (...)` or a join. Ten queries become one beats ten individually-fast queries.
## Measure the change
`EXPLAIN ANALYZE` before and after, against realistic data volume. An index that helps a
100-row table may not justify its write cost at 100 million. Report the timing you actually
measured, not the improvement you expected.
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---
name: perf-frontend
description: Make a web page faster. Use when a page loads slowly, feels janky, fails Core Web Vitals, or ships too much JavaScript.
---
# Frontend performance
Measure the user's experience first: a Lighthouse lab score and, better, real-user data. Optimise
the metric that is actually failing, not the one easiest to move.
## The vitals and what drives them
- **LCP** (largest contentful paint) — almost always the hero image or a web font. Preload it,
size it correctly, serve it in a modern format, and do not let render-blocking resources delay it.
- **INP / responsiveness** — long tasks on the main thread. Break up work, defer non-urgent JS,
and keep event handlers fast. A click that responds in 50ms feels instant; 300ms feels broken.
- **CLS** (layout shift) — images and embeds without dimensions, late-injected banners, web fonts
swapping. Reserve the space before the content arrives.
## Ship less JavaScript
The bundle is usually the problem. Route-level code splitting so a page loads only what it needs,
lazy-load the heavy below-the-fold component, and audit the dependency tree for a large library
imported for one function. Removing 100KB of JS beats most micro-optimisations.
## Network discipline
- Cache static assets with long, content-hashed lifetimes; the second visit should cost almost
nothing.
- Compress (brotli/gzip) and serve images at the size they are displayed, responsive `srcset`,
not a 3000px original in a 300px slot.
- Fetch in parallel, not in waterfalls: start independent requests together, and preload the
critical few.
## Change one thing, measure it
Take a baseline (the metric, the page, the device class), make one change, re-measure on the same
setup. Two changes at once and you do not know which paid. Report the before/after you actually
measured, on a realistic device and connection — a developer's fast laptop and fiber hides what a
mid-range phone on 4G feels.
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---
name: perf
description: Make something faster, or find out why it is slow. Use when a command, request, test suite, or build takes longer than it should.
---
# Performance
Measure first. A change made without a number before it is a guess with extra steps, and
most "optimisations" made on a guess make the code worse and no faster.
## Get a number
Time the actual operation, not a proxy for it: `time`, the framework's own timing output,
or a loop around the slow call with a timestamp either side. Use realistic input — a fast
result on a tiny fixture tells you nothing about the production case. Record the baseline
with `remember` so the comparison survives compaction, and run it enough times that a
warm cache and jitter do not fool you.
If you cannot measure it, say so and stop. Optimising an unmeasured path is how a codebase
accumulates complexity that buys nothing.
## Find where the time goes
- **Wall-clock dominated by one call?** Look there and nowhere else. The biggest node is
the only one worth touching.
- **Spread evenly?** Suspect the loop around it: an O(n²) walk, a query per row, a file
read per iteration, an allocation per element.
- **Idle time?** It is waiting, not computing: a sequential chain of independent awaits, an
unpooled connection, a contended lock, a slow remote call.
The usual culprits, in the order they actually appear: N+1 queries, work repeated inside a
loop that could be hoisted, a missing index, sequential awaits that could run together,
reading a whole file to use one line, and re-parsing something that could be parsed once.
## Change one thing
One change, then re-measure on the same setup. Two changes together and you do not know
which one paid — and one of them may have cost. If the number did not move, revert the
change; an optimisation that does not measure is just complexity.
## Stop when it is fast enough
State the target before you start: "the test suite under a minute", "the endpoint under
200ms". Past the target, further work is complexity with no user on the other end of it.
## Report
Baseline, the change, the new number, and what you deliberately did not do. A 40% win with
one line changed is a better report than a 45% win that restructured a module.
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---
name: plan
description: Break a non-trivial task into an ordered, verifiable sequence before writing code. Use when a request is large, spans several files, or its steps depend on each other.
---
# Planning
A plan that cannot be checked is a wish. Every step ends in something you can run.
## Understand before you sequence
Read enough to know the real shape of the work: the entry point, the data's path, the
module that owns the behaviour. A plan made from filenames alone reorders itself the
moment you open the first file. Grep the actual call sites; do not plan around a guess.
## Order by dependency, not by file
A step may depend on another's output: a type before its callers, a schema before its
migration, a test helper before the tests that use it. Sequence so nothing references
what does not exist yet. If two steps are independent, say so — the order between them
is free and you may take the cheaper one first to derisk the rest.
## One step, one verifiable outcome
Each step names the command that proves it done: a test that passes, a build that
compiles, a script that runs. "Wire it up" is not a step. Write the list with
`todo_write`, then work it in order, marking done immediately — not in a batch at the end.
## Keep it small
The plan is a scaffold, not the building. If a step grows past "change these few files",
split it. If the task turns out smaller than it looked, drop the remaining steps and say
why rather than inventing work to fill them.
## Replan when the ground moves
New information that changes the order or the scope is a reason to rewrite the list, not
to push through it. A stale plan followed faithfully is worse than no plan.
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---
name: readme
description: Write or fix a project README. Use when creating a README, when a new user cannot get the project running from it, or when it has drifted from the code.
---
# README
A README has sixty seconds to answer: what is this, do I want it, and how do I run it. Everything
else is secondary to those three.
## The first screen answers three questions
1. **What it is** in one or two sentences, concrete about the problem it solves — not "a modern
solution" but "a CLI that lints Terraform plans against your org's policies".
2. **Install** — the one command that gets it.
3. **The first thing that works** — the minimal command or snippet that produces visible output.
If a new user cannot get a win in two minutes, most leave.
## Verify every command
Run each command in the README against a clean environment and paste its real output. The most
common README defect is an install or quickstart that no longer works because the code moved and
the doc did not. If you cannot run it, do not write it.
## Structure for scanning
After the quickstart, in the order a new user needs them: features as a short list of what it
does (not how), the common tasks as copy-paste examples, configuration as a table of options with
defaults, then links to deeper docs. Headings let a reader jump; a wall of prose gets skimmed
past the thing they needed.
## Show, do not tell
A three-line example of real use beats a paragraph describing capability. Show the input and the
output. A screenshot or asciinema of the actual tool running is worth a hundred adjectives —
include one if the tool has any visual surface.
## Keep it true
Document the stable interface, not this week's implementation, or the README rots. Re-read it on
every release: a README that contradicts the current version is worse than a short one, because
it actively misleads.
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---
name: refactor
description: Restructure code without changing behaviour. Use when asked to refactor, clean up, extract, or reorganise.
---
# Refactoring
Behaviour must not change. That is the whole constraint — every other goal (clarity,
structure, naming) is subordinate to it. The moment behaviour changes, you are no longer
refactoring, you are editing, and the safety argument below stops holding.
## Establish the safety net first
Run the existing tests and record that they pass — with `remember`, so the baseline
survives compaction. If the code has no tests, write one that pins current behaviour,
*including the ugly parts*: the odd return value, the quirk callers depend on. You are not
judging the behaviour, you are freezing it. Refactoring untested code is not refactoring;
it is rewriting, and it belongs under the edit workflow with its own verification.
## Then move in small steps
One transformation at a time, tests green between each. Rename, then extract, then move —
not all three in one edit. The mechanical refactorings are the safe ones: rename, extract
function, inline, move. Compose them. A large refactor that fails leaves you unable to
tell which of five steps broke it; a small one that fails tells you exactly which.
After each step, run the tests, not just the typechecker. Types catch signature drift;
they do not catch a reordered conditional or a dropped early return.
## What not to do
- Do not fix bugs while refactoring. Note them, finish the refactor green, then fix in a
separate change — otherwise a regression could be either the refactor or the fix.
- Do not add abstraction for a single caller. Duplication beats a premature interface;
the third caller is when the abstraction earns its name.
- Do not widen the scope. The request was this code, not its neighbours. A refactor that
"while we're here" touches five more files is five more files of unreviewable risk.
- Do not change public API unless asked; if it must change, say so first and update every
caller in the same change.
## Done means
Tests pass, behaviour is identical, and the diff is smaller than the reader feared. If the
diff is larger than the code it moved, you abstracted too early — put it back.
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---
name: release
description: Cut a release: versioning, changelogs, tagging, publishing. Use when asked to release, bump a version, write release notes, or fix a broken publish.
---
# Release
A release is a promise that a specific, identified state of the code works. Make it
reproducible or do not make it.
## Version says what changed
Semver: breaking is a major, a feature is a minor, a fix is a patch. The number is a message to
whoever upgrades, not a marketing choice. Below 1.0, say so plainly — semver promises nothing
and the version should not pretend otherwise.
## The changelog is for the upgrader
- Group by what the reader must do: breaking changes and required actions first, then features,
then fixes.
- Write it as "you can now X" or "Y no longer Z", from the user's side, not the commit's. A
changelog that is a git log is a changelog nobody reads.
- Every breaking change names the migration: what to change to keep working.
## Verify before you tag
The release candidate builds clean from a fresh checkout, the tests pass, and the version string
in the source matches the tag you are about to push. A version/tag mismatch published is the
kind of thing that ships "0.4" labelled as "0.3" forever.
## Tag the commit, publish the artifact
Tag the exact commit that was verified, and build the artifact from that tag — not from a
working tree that has since moved. The tag is immutable; never move it to a different commit.
If a release is wrong, cut a new one with a new number; do not quietly re-tag.
## If it goes wrong
Have the rollback ready before you need it: the previous artifact still available, the deploy
reversible. A bad release is fixed forward with a patch release, not by deleting the evidence.
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---
name: review
description: Review a diff or a file for defects. Use when asked to review, critique, or check code before it ships.
---
# Code review
Severity order. Do not lead with style — a review that opens on naming while a real bug
sits three lines down has failed at its one job.
1. **Incorrect behaviour** — wrong result, wrong edge case, wrong state after failure.
2. **Missing validation at trust boundaries** — user input, network responses, file contents,
anything crossing a process line. Internal calls need no defensive checks.
3. **Security** — injection, path traversal, secrets in logs or errors, missing authz.
4. **Resource handling** — unclosed handles, unbounded growth, unawaited promises.
5. **Clarity** — only when it will cause a future defect.
## How to read the change
- Read the diff against its intent. Does it actually do what the title/commit says? A
correct-looking diff that solves the wrong problem is the most expensive approval.
- Read the *deleted* lines as carefully as the added ones. Behaviour is often lost in a
removal, and diffs render deletions quietly.
- Follow each new call one level into the callee. The assumption that breaks it is usually
one level down, invisible in the diff itself.
## For each finding
State file and line, the concrete failure (what input makes it break, or why it always
breaks), and the change. Show the fix as code when it is short. "This could be a problem"
without a path to a real input is noise; either trace it or drop it.
Order findings by severity and lead with the worst. Skip anything a formatter would fix.
Skip preference. If a choice is defensible, leave it — a review is not a place to impose
your style on code that works.
## Say when it is fine
A review that invents problems to look thorough is worse than a short one. If the change
is correct, say so plainly and stop. "Looks correct, and here is what I checked" is a
complete and useful review.
## Verify, do not assume
Read the surrounding code before calling something a bug. A "missing" null check often
exists one level up; a "redundant" guard often covers a caller you have not seen. Run the
tests or write the failing input if that is what settles it. A finding you verified is
worth ten you suspected.
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---
name: security
description: Review code for security defects, or write code that handles untrusted input. Use when touching authentication, user input, file paths, shell commands, SQL, or anything reachable from the network.
---
# Security
Find the trust boundary first. Everything crossing it is hostile until parsed.
## The boundaries in most codebases
- Request bodies, query strings, headers, cookies.
- File contents and filenames, including paths a user supplied.
- Environment variables in a multi-tenant deployment.
- Anything a model or a third-party API returned.
Inside a boundary, values are already validated and re-checking them is noise. At the
boundary, nothing is optional.
## What to look for, in order
1. **Injection.** String-built SQL, shell commands assembled from input, `eval`, template
rendering with user data as the template rather than the data. The fix is parameters and
argument arrays, never escaping.
2. **Missing authorisation.** An endpoint that checks *who* you are but not *what* you may
touch. Look for an id taken from the request and used without an ownership check.
3. **Path traversal.** `../` in anything joined onto a filesystem root. Resolve, then verify
the result is still inside the root — a prefix check on the raw input misses
`a/../../secret`.
4. **Secrets in the wrong place.** Keys in source, in logs, in error messages, in a commit.
A secret that reached a log is a secret to rotate.
5. **Server-side request forgery.** A URL from input, fetched. Block private and loopback
addresses by *resolved* address, and re-check every redirect hop.
6. **Weak crypto and hand-rolled auth.** Homemade token formats, `Math.random` for anything
security-bearing, comparisons on secrets that are not constant time.
## Verify the path before reporting
Trace each candidate from an attacker-controlled value to the sink before you name it. A
"this could be unsafe" without that path is noise that buries the real finding. If you
cannot construct the malicious input that reaches the sink, either keep looking or say
plainly that you could not confirm it.
Do not fix a symptom at one caller when the sink is shared. Grep every caller and fix the
seam once — a sanitiser at one of five call sites is four open holes and one false sense
of safety.
## Reporting
File, line, the path from input to sink, a concrete payload, and the fix. Rank by
exploitability: a reachable injection beats a theoretical weakness in dead code. Say
plainly when a thing that looks dangerous is actually fine, and why — a reviewer's
confidence in the clean parts is worth as much as a finding.
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---
name: test
description: Write or repair tests. Use when adding coverage, fixing a flaky test, or asked how something should be tested.
---
# Testing
A test earns its place by failing when the code is wrong. A test that cannot fail — or
that passes regardless — is not a test, it is overhead with a green checkmark.
## Match the project
Read two existing test files first. Use their runner, their assertion style, their file
layout, their naming, their way of building fixtures. A test that looks foreign is a test
nobody maintains, and an unmaintained test is deleted the first time it goes red.
## Test behaviour, not implementation
Assert on what a caller observes: the return value, the written file, the emitted event,
the status code. A test that reaches into private state or mocks a collaborator's
internals breaks on every refactor while catching nothing real. If you cannot say what
the caller sees, you are testing the how, and the how is allowed to change.
Cover, in this order of value:
- **The failure** — the bad input, the missing file, the null. Failure cases catch more
real defects than happy paths, because most code is written for the happy path first.
- **The boundaries** — empty, one, the maximum, off-by-one at each edge.
- **The normal case** — one, to prove the wiring works at all.
## Never do this
- Do not assert what the code currently returns without knowing it is correct — that pins
the bug into the suite and calls it a specification.
- Do not weaken an assertion to make a test pass. If it fails, either the code or the
expectation is wrong; find out which before you touch either.
- Do not delete a failing test to go green. It is telling you something; listen.
- Do not test the framework or the library. Your code is the subject; their code has its
own suite.
## Flaky tests
A test that passes alone and fails in a suite is a shared-state problem: a global, a temp
directory, a port, an unawaited promise, leftover data, or ordering. Find which — run it
repeatedly and in isolation to confirm, then remove the shared state. Do not add a retry:
a retried flake is a real intermittent bug you have decided to stop hearing about.
## Verify
Run the test and watch it fail before the fix, pass after. A test you never saw fail is
not known to test anything.
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---
name: ux-copy
description: Write user-interface text: labels, errors, empty states, onboarding. Use when wording a button, an error message, a confirmation, or any text the user reads in the product.
---
# UX copy
Interface text is part of the interface. Clear, short, and honest beats clever.
## Lead with what the user cares about
- Buttons say the action and the object: "Save changes", not "OK". "Delete project", not "Yes".
- Headings say the outcome or the thing, not the category: "Your invoices" over "Billing section".
- Front-load the information word. Users scan the first two words; "3 errors found" scans, "Found
3 errors" buries it.
## Error messages say what happened and what to do
Never blame, never jargon, never just a code. "Couldn't save because you're offline. Your changes
are kept — try again when you're back." The pattern is: what went wrong, whether their work is
safe, the one action to take. An error that only says "Something went wrong" makes the user do
the debugging.
## Empty states teach, they do not apologise
An empty screen is a chance to say what goes here and how to start: "No projects yet. Create your
first to see it here." plus the button. "Nothing to display" wastes the moment the user is most
receptive to guidance.
## Be honest about destructive and irreversible actions
A confirmation names exactly what will happen and that it cannot be undone: "Delete 'Invoices
2024'? This permanently removes 3,120 records and cannot be undone." The destructive button
repeats the verb: "Delete", never a bare "Confirm" that could mean anything.
## Consistent words for consistent things
Pick one term per concept and use it everywhere — if it is a "project" in one place it is not a
"workspace" in another. Sentence case for labels, no exclamation marks, no "please", no "simply".
The tone is a competent colleague, not a marketing page.
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---
name: verify
description: Confirm a change actually works by using it, not by reading it. Use before reporting a task complete, or when asked whether something works.
---
# Verification
A green test suite says the tests pass. It does not say the feature works. The two are
different claims, and only one of them is what the user asked for.
## Run the artifact, not the source
Build it and use it the way a user would, end to end:
- **CLI** — build the binary and run it. Happy path, bad input, `--help`. Read the actual
output, not the output you expected.
- **HTTP service** — start it and `curl` the endpoint. Check the status line and the body,
not just that it returned something.
- **Library** — write a throwaway script that imports and calls the new code end to end,
the way a consumer would.
- **Script or job** — run it against real input and inspect what it produced.
Delete the throwaway afterwards. A verification script left behind becomes clutter the
next person trips over.
## What counts as evidence
Command output you actually saw. Paste the relevant lines, not a summary of them — a
summary hides the one line that mattered.
These are not evidence:
- "The tests pass" for a change the tests do not cover.
- "The types check" for anything about runtime behaviour.
- "It compiles" for anything about correctness.
- "It should work now" for anything at all.
## Check the failure path too
Feed it the input you expect to be rejected and confirm it is rejected, with a message
that says why. Then the edge case at the boundary. A feature that works only on correct
input is half-built, and the half that is missing is the half users hit first.
## Report what you did not verify
Say plainly what you could not run and why: a missing credential, a service you cannot
start, a platform you are not on. An honest gap is useful — the reader can fill it. A
claim that hides one is a bug you just shipped in prose.
## When verification fails
The defect is yours to fix in this turn. Do not report the task complete with a note that
it did not work — that is a failure report, not a completion.