# Lisa

Lisa turns a repository into a set of **software factories** — agent-operated production lines (Research → Plan → Implement → Verify) that build, verify, and ship software with as few humans as possible. Underneath the factories, Lisa is governance-as-code for AI-assisted software development: one set of standards, workflows, and quality gates applies identically whether the work is done by a human, by an AI agent on a laptop, or by a scheduled job — distributed to every project as a versioned dependency, and to every major coding agent platform (Claude Code, Codex, Cursor, OpenCode, Antigravity, Copilot) from one source.

> **How to read this README**
>
> Lisa changes frequently, so this file stays deliberately conceptual. It teaches the mental model, which is stable, and avoids the specifics that drift — exact command names, flags, file paths, supported stacks, and version-dependent behavior. Wherever you need a current, concrete answer, you'll find a **Prompt for your coding agent**: a question to ask Claude Code (or whichever agent you use) that reads the live repository and answers from source.
>
> - **Humans** — paste the prompt into your agent instead of trusting a hard-coded list that may be stale.
> - **Agents** — treat these prompts as instructions. Prefer discovering the current answer from the repository over asserting it from memory, and verify against source before stating it as fact. That habit is the same one Lisa enforces on all work: *prove it, don't assume it.*

## What Lisa is

Lisa wears four faces from a single package:

- **A factory operator.** Scheduled intake agents watch queues of ready work, adversarially gate what enters the pipeline, and run each item through Research → Plan → Implement → Verify without a human in the loop.
- **A standards engine.** It applies a curated set of configs, CI workflows, git hooks, and project rules into a target repository and keeps them current as the package updates.
- **An operating system for coding agents.** It ships the skills, commands, agent roles, rules, and hooks that define *how* an AI agent investigates, builds, reviews, ships, and verifies work — and it delivers that same content to every supported agent runtime from one source.
- **A configuration library.** Projects can consume Lisa's linting, testing, and TypeScript presets directly, per technology stack, without adopting the rest.

All four are versioned together, so "how we build software here" upgrades by bumping one dependency.

> **Prompt for your coding agent**
> "Give me a one-paragraph tour of this repository's top-level layout: where the apply/template engine lives, where the agent plugin content is authored, and where the shared config presets are exported. Read the source and cite paths."

## The factory model

Lisa's purpose is to run four **factories** in your project — production lines operated by agents, not people — so that **non-technical people can create scalable software** by describing outcomes while the factories supply the engineering discipline. **Research** creates PRDs. **Plan** turns a PRD into ordered work units. **Implement** turns work units into quality software — tests, code, UI, APIs, infrastructure. **Verify** issues a go/no-go by using the software the way a human would, and files any failures straight back into Implement as build-ready tickets, so the loop heals itself.

Humans don't work inside a factory. Handoff happens outside, at the **gates**: agents, humans, and automations submit inputs, and an intake agent adversarially evaluates each one — is it high-quality and unambiguous, and does the factory have the tooling *and provable access to that tooling* to execute it? The intake agent tries to resolve gaps itself first; what it genuinely can't resolve, it rejects and raises to a human rather than guessing.

Three loops keep the pipeline fed without anyone asking: **QA** explores the product like a first-time user and files bugs, **Product Planning** ideates PRDs, and **Monitoring** audits observability signals and files regressions. Everything runs on the coding agent's native scheduler, and by default the loops' outputs enter the gates pickup-ready — the adversarial intake is the quality control, not a human triager. Executed properly, end users have zero direct contact with coding agents: they see the tracker, the PRDs, and the shipped software.

> **Prompt for your coding agent**
> "Explain how this project's factory pipeline is wired right now: which automations exist and on what cadence, what each gate checks before admitting work, and where a human is still required. Read the installed automations and intake skills — don't guess."

## Core principles

These are the durable ideas. Everything concrete descends from them.

**Single source, many destinations.** Agent instructions are authored once and compiled into runtime-specific artifacts and into each project. You never hand-edit the generated output — you edit the source and rebuild.

> **Prompt for your coding agent**
> "In this repo, which directory is the source of truth for plugin/agent content, which directories are generated artifacts, and what command rebuilds them? Show me the guard that fails CI if the artifacts drift from source."

**Location-agnostic.** The same rules and gates hold on a developer's workstation, in a scheduled improvement job, and in a CI workflow reacting to a PR. Only the plumbing adapts — local integrations versus REST in CI — the standards do not.

**Layered quality gates.** Rules load into every agent session as context; git hooks are hard stops on commit and push; agent hooks bridge an agent's actions to the project's real tooling so linting, tests, and checks actually run.

> **Prompt for your coding agent**
> "List the quality gates that would run against a change in this project — session rules, git hooks, and agent hooks — and for each, tell me whether it blocks or only warns, and how you can tell from the code."

**Evidence-based verification.** A task is not "done" because an agent says so. Lisa requires work to be proven with reproducible, empirical evidence — the change is exercised, the behavior observed, the proof attached to the ticket and the PR — and it enforces this with gates that are not meant to be bypassed. If you take one idea from Lisa, take this one.

For DOM-web empirical verification, projects may explicitly opt into the guarded
[Kane CLI provider](docs/kane-cli-integration.md). It supplies a normalized live-browser result and
evidence pack while Lisa retains environment policy, the final verdict, durable evidence, and
native Playwright/Maestro regression authority.

> **Prompt for your coding agent**
> "Walk me through how this project verifies that a change actually works before it is considered complete. What evidence is required, where is it recorded, and which gate refuses to let unverified work ship?"

**Governed templates.** Every file Lisa distributes carries a governance intent: some are enforced and overwritten on every update, some are seeded once and then owned by the project, and some are merged so the project and Lisa both contribute. Knowing which is which tells you what you can safely edit.

> **Prompt for your coding agent**
> "For the file I want to change in my project, is it enforced by Lisa (overwritten on update), created-once (mine to edit), or merged? Read the template source and explain what will happen to my edits on the next Lisa update."

**One repository per unit of work.** Planning artifacts may span repositories, but anything actually built and shipped targets exactly one repository. This keeps ownership and review unambiguous.

## Getting started

Setup has three layers, and conflating them is the usual mistake:

```
vault  →  machine  →  each repo  →  each other surface
```

Two commands do the machine-and-surface work, and **neither needs a checkout** —
that matters, because the surfaces most in need of setup are the ones with no
repository attached.

| Command | Answers | Scope |
| --- | --- | --- |
| `workstation` | what binaries does this machine have | once per machine |
| `environment <surface>` | which tenant, and where do its credentials go | once per machine, and once per other surface |

`environment` runs where Lisa can execute and prints what to paste where it
cannot:

```
environment local        prepares this machine
environment container    an image definition and a run command
environment claude-web   text for the Claude environment dialog
environment codex-cloud  text for the Codex environment settings
```

### 1. The vault, once per tenant

Create the secrets, then a **machine account** granted only the projects agents
may read — that grant is what scopes every headless session. Annotate a secret's
note with `tool: <name>` where a CLI is implied (`sonar`, `aws`, `gh`); a session
with no checkout installs exactly what the notes name, and the whole catalogue
if they name nothing.

Issue each place its own access token, at the moment you set that place up. One
token shared across five places makes any compromise a five-place outage, and
makes every access in the audit log anonymous.

### 2. The machine

```bash
npx -y @codyswann/lisa@latest workstation --install --provider=bitwarden
exec $SHELL -l                                   # ~/.local/bin on PATH
npx -y @codyswann/lisa@latest environment local --tenant=<name>
```

Node is the only prerequisite. `workstation` is the one entry point that works
before any agent or repository exists — invoking a *skill* needs an installed
agent, and Lisa is a devDependency so it needs a clone; both are what this
creates.

`environment local` prompts for the token, stores it in the OS keychain (a
`0600` file where there is no keychain), materializes the credentials, and
installs the per-environment AWS profiles. **Re-run it to rotate** — it is not
an installer, so it will not reinstall six coding agents to replace one token.

`--tenant` is required because every namespace is a directory: guessing would
put one tenant's credentials where another tenant's sessions read.

Then authenticate the things that are *you* rather than the machine account —
`gh auth login`, `aws configure sso`, and each agent's own login.

### 3. Each repository

```bash
git clone … && cd …
npx -y @codyswann/lisa@latest apply    # lint, hooks, workflows, agent surfaces
bun install                            # postinstall installs the agent plugins
npx -y @codyswann/lisa@latest sync     # then set secrets.namespace
```

A repository only needs to say *which tenant it belongs to*. The credentials are
already on the machine.

Brownfield repositories then converge — see below.

### 4. Each other surface

Cloud environments are account-scoped and shared across every repository, so
this is once per environment rather than once per repository:

```bash
npx -y @codyswann/lisa@latest environment claude-web --tenant=<name>
```

Paste what it prints. Give that environment its **own** access token: those
dialogs store values in plain text, readable by everyone who can use the
environment.

A repository becomes usable *from* a cloud surface with
`lisa remote-env --install`, which writes two entrypoints to commit. They are
inert locally — a container that has just cloned the repository has never seen
the plugin they came from, so they have to be part of the clone.

---

Install Lisa as a development dependency in each project that uses it:

```bash
bun add --dev --trust @codyswann/lisa
```

Lisa's dependency `postinstall` runs `lisa apply` for that project. A later
`bun update @codyswann/lisa` reapplies the updated project-scoped artifacts.

That bootstrap is **loud but non-fatal**: a failed apply prints the real error
plus a warning naming the consequence, and still exits 0, because a postinstall
that aborts would break `bun install` in every environment where apply
legitimately cannot run. The durable signal is
`lisa doctor` — a successful apply writes `.lisa/apply-receipt.json`, and doctor
reports any repo whose receipt is missing or older than the installed Lisa
("this repo has not successfully applied templates since `<version>`"). Run it
whenever a project seems to have stopped tracking upstream changes; a repo can
otherwise sit silently stale for months.

The same check covers a second, quieter gap. A package install runs apply in
**postinstall-safe** mode (`--skip-git-check`), which deliberately skips every
agent emit — Codex, Claude, agy, Copilot, OpenCode — and the Sonar integration,
because those rewrite host-owned files. So no `bun install` at any version can
reconcile `.codex/config.toml`; only a full `lisa apply .` does. Doctor now says
so instead of reporting such a repo as current.

It covers a third. An unattended apply never replaces a managed file your
project may have customised — that is the point of the mode — so a release that
changes `eslint.config.ts` leaves yours where it is and names it in the install
output. That output scrolls away, and what remains is a file that has quietly
stopped receiving upstream fixes, security fixes included. Apply now records
every such path in the receipt, and doctor names them back with the remedy:
`lisa apply . --refresh-templates=<path>`, **one path at a time**. Do not reach
for the bare `--refresh-templates` — it is repo-wide and reverts every
deliberate fork in the project at once. To keep your version instead, add the
path to `.lisaignore`.

Lisa drives `js-yaml` through one interop shim and works with the 3.x, 4.x, and
5.x lines, so a host `overrides`/`resolutions` pin that collapses Lisa's own
copy is fine. A pin Lisa genuinely cannot drive is reported by `lisa doctor` as
a named incompatibility rather than a crash.
Lisa does not register user-wide Codex plugins, skills, hooks, rules, MCP
servers, or configuration. Other harnesses retain their existing delivery
behavior. For a new project, run the CLI ephemerally with
`bunx @codyswann/lisa setup-project ...`.

For Codex, apply emits a repository marketplace containing only the base Lisa
plugin plus detected stacks and explicitly configured features. Codex loads
native skills, hooks, and rules directly from those installed plugin bundles.
Skill bodies are not copied into the project and unrelated Lisa stacks are not
loaded. Project settings use `[features].hooks`; the deprecated `codex_hooks`
key is removed during reconciliation.

Remote coding environments use one vendor-neutral AWS bootstrap rather than
repository-specific or agent-specific IAM users. Run `/lisa:setup-remote-aws`
to install the common setup script and native Cursor/Copilot adapters; Claude,
Codex, Cursor, Copilot, and user-managed Antigravity/OpenCode hosts all consume
the same `LISA_AWS_BOOTSTRAP_JSON` bundle. See
[Remote coding-agent AWS access](docs/remote-agent-aws.md).

The supported stacks, setup flags, and exact invocation evolve as the project grows, so ask for the current set rather than copying a list that may have moved on:

> **Prompt for your coding agent**
> "Using this project's `lisa` CLI, show me how to (a) scaffold a new project and (b) apply Lisa to an existing one. List the project types it supports right now and the flags each command accepts — read `lisa --help` and the CLI source, don't guess."

### Brownfield projects: becoming agent-ready

A greenfield project is agent-ready by construction. An existing codebase carries years of tacit knowledge in people's heads, so Lisa converges it before the factories run unattended — in two steps. **Knowledge first**: an onboarding command reads every inventoried source without mutating it, redacts secrets, credentials, and policy-detected sensitive PII, omits or aggregates ordinary person-level user data before wiki persistence, and records each source as complete, partial, or unavailable. Partial and unavailable sources remain open gaps; humans answer gaps inline, a fresh session re-runs the ingest, and convergence requires both every source complete and zero open gaps. **Standards second**: apply Lisa's full rules and thresholds — the project will go red, deliberately — and let agents refactor to conform without changing behavior, proven by the test suite and empirical verification.

> **Prompt for your coding agent**
> "Is this project agent-ready? Run the agent-ready onboarding: inventory every connected source, ingest it read-only with redaction, show me each source's coverage status and the open gaps only I can answer, and tell me exactly what happens after I answer them."

## The work lifecycle

Lisa organizes a piece of work as a pipeline of specialized agent roles — the factories, seen from inside. Conceptually a work item moves through five stages:

1. **Understand** *(the Research factory)* — investigate the codebase and the problem, produce a spec or PRD.
2. **Plan** *(the Plan factory)* — decompose the spec into ordered work items in your tracker.
3. **Build** *(the Implement factory)* — take one item from spec to a merged PR: a team of agents implements, reviews, and ships it.
4. **Prove** *(the Verify factory)* — deploy, verify the behavior in the target environment with real evidence, and turn a passing manual check into a regression test.
5. **Learn** — after shipping, mine the work for edge cases and friction and fold accepted learnings back into the standards.

Most people invoke only the first stages explicitly; the rest run as nested sub-flows. The same logic runs whether you trigger it by hand or a scheduled job triggers it unattended.

> **Prompt for your coding agent**
> "List the current Lisa lifecycle commands for this project — understand, plan, build, prove, learn — with their exact names and arguments, and note any that run automatically as sub-steps. Read the installed commands, don't answer from memory."

### Unattended and batch work

Lisa can watch a queue of ready work and dispatch each item through the lifecycle on its own, which is what makes it usable as a scheduled operator. A standard automation fleet covers the pipeline movers (PRD intake, ticket intake, queue repair) and the three feeding loops (exploratory QA, product ideation, observability monitoring); Lisa can also recover queues that are stuck and report whether the fleet is healthy.

> **Prompt for your coding agent**
> "Which commands let this Lisa scan a work queue, dispatch ready items, repair stuck ones, and report on scheduled-automation health? Show me how I'd point one at my queue and what it expects in configuration."

## Working across trackers and sources

Lisa is deliberately vendor-neutral. The lifecycle runs the same whether your tickets live in one tracker or another and whether your product specs originate in one document tool or another — a thin dispatch layer selects the right integration from configuration, so the workflow you learn transfers.

> **Prompt for your coding agent**
> "Which issue trackers and which PRD/spec sources does this Lisa support today, and which config keys select them? Read the vendor dispatch layer and the setup skills, then show me a minimal configuration for my combination."

## The in-repository knowledge base

Lisa keeps a durable, markdown knowledge base inside the repo as the long-lived memory for architecture, workflows, decisions, and history — separate from the fast-moving code. It can ingest commits, PRs, design docs, and notes into that base and answer questions from it.

> **Prompt for your coding agent**
> "Where is this repo's knowledge base, what should I read first to orient myself, and how do I ask you to ingest recent changes into it? List the current entry points."

A downstream project can add the same knowledge base on demand rather than receiving it by default.

> **Prompt for your coding agent**
> "Does my project have Lisa's knowledge base enabled? If not, walk me through enabling and bootstrapping it using whatever the current install and setup commands are."

## Extending or contributing to Lisa

If you're changing Lisa itself: author agent content and templates at their source, never in generated output, and rebuild so the distributed artifacts regenerate. Lisa applies its own standards to itself, so the same gates that guard downstream projects guard this one — including the requirement to prove your change works.

> **Prompt for your coding agent**
> "I want to add or change a skill, rule, hook, or agent in Lisa. Show me the source location, the build step, what I must commit alongside it, and the CI check that fails if I edit a generated artifact directly. Then check my change against those rules before I commit."

## Just ask

You don't need to memorize any of this. Describe the outcome you want and let Lisa route it:

> "I have a ticket in our tracker — research it, plan it, and implement it."
>
> "Walk the checkout flow in a real browser and tell me what's broken."
>
> "Get test coverage on this module to 90% and prove it."

> **Prompt for your coding agent**
> "What can Lisa do in this project right now? List the available commands grouped by purpose, and flag anything that needs configuration I haven't set up yet."
