gstack/ARCHITECTURE.md

Architecture

This document explains why gstack is built the way it is. For setup and commands, see CLAUDE.md. For contributing, see CONTRIBUTING.md.

The core idea

gstack gives Claude Code a persistent browser and a set of opinionated workflow skills. The browser is the hard part — everything else is Markdown.

The key insight: an AI agent interacting with a browser needs sub-second latency and persistent state. If every command cold-starts a browser, you're waiting 3-5 seconds per tool call. If the browser dies between commands, you lose cookies, tabs, and login sessions. So gstack runs a long-lived Chromium daemon that the CLI talks to over localhost HTTP.

Claude Code                     gstack
─────────                      ──────
                               ┌──────────────────────┐
  Tool call: $B snapshot -i    │  CLI (compiled binary)│
  ─────────────────────────→   │  • reads state file   │
                               │  • POST /command      │
                               │    to localhost:PORT   │
                               └──────────┬───────────┘
                                          │ HTTP
                               ┌──────────▼───────────┐
                               │  Server (Bun.serve)   │
                               │  • dispatches command  │
                               │  • talks to Chromium   │
                               │  • returns plain text  │
                               └──────────┬───────────┘
                                          │ CDP
                               ┌──────────▼───────────┐
                               │  Chromium (headless)   │
                               │  • persistent tabs     │
                               │  • cookies carry over  │
                               │  • 30min idle timeout  │
                               └───────────────────────┘

First call starts everything (~3s). Every call after: ~100-200ms.

Why Bun

Node.js would work. Bun is better here for three reasons:

1. Compiled binaries. bun build --compile produces a single ~58MB executable. No node_modules at runtime, no npx, no PATH configuration. The binary just runs. This matters because gstack installs into ~/.claude/skills/ where users don't expect to manage a Node.js project.

2. Native SQLite. Cookie decryption reads Chromium's SQLite cookie database directly. Bun has new Database() built in — no better-sqlite3, no native addon compilation, no gyp. One less thing that breaks on different machines.

3. Native TypeScript. The server runs as bun run server.ts during development. No compilation step, no ts-node, no source maps to debug. The compiled binary is for deployment; source files are for development.

4. Built-in HTTP server. Bun.serve() is fast, simple, and doesn't need Express or Fastify. The server handles ~10 routes total. A framework would be overhead.

The bottleneck is always Chromium, not the CLI or server. Bun's startup speed (~1ms for the compiled binary vs ~100ms for Node) is nice but not the reason we chose it. The compiled binary and native SQLite are.

The daemon model

Why not start a browser per command?

Playwright can launch Chromium in ~2-3 seconds. For a single screenshot, that's fine. For a QA session with 20+ commands, it's 40+ seconds of browser startup overhead. Worse: you lose all state between commands. Cookies, localStorage, login sessions, open tabs — all gone.

The daemon model means:

• Persistent state. Log in once, stay logged in. Open a tab, it stays open. localStorage persists across commands.
• Sub-second commands. After the first call, every command is just an HTTP POST. ~100-200ms round-trip including Chromium's work.
• Automatic lifecycle. The server auto-starts on first use, auto-shuts down after 30 minutes idle. No process management needed.

State file

The server writes .gstack/browse.json (atomic write via tmp + rename, mode 0o600):

{ "pid": 12345, "port": 34567, "token": "uuid-v4", "startedAt": "...", "binaryVersion": "abc123" }

The CLI reads this file to find the server. If the file is missing, stale, or the PID is dead, the CLI spawns a new server.

Port selection

Random port between 10000-60000 (retry up to 5 on collision). This means 10 Conductor workspaces can each run their own browse daemon with zero configuration and zero port conflicts. The old approach (scanning 9400-9409) broke constantly in multi-workspace setups.

Version auto-restart

The build writes git rev-parse HEAD to browse/dist/.version. On each CLI invocation, if the binary's version doesn't match the running server's binaryVersion, the CLI kills the old server and starts a new one. This prevents the "stale binary" class of bugs entirely — rebuild the binary, next command picks it up automatically.

Security model

Localhost only

The HTTP server binds to localhost, not 0.0.0.0. It's not reachable from the network.

Bearer token auth

Every server session generates a random UUID token, written to the state file with mode 0o600 (owner-only read). Every HTTP request must include Authorization: Bearer <token>. If the token doesn't match, the server returns 401.

This prevents other processes on the same machine from talking to your browse server. The cookie picker UI (/cookie-picker) and health check (/health) are exempt — they're localhost-only and don't execute commands.

Cookie security

Cookies are the most sensitive data gstack handles. The design:

1. Keychain access requires user approval. First cookie import per browser triggers a macOS Keychain dialog. The user must click "Allow" or "Always Allow." gstack never silently accesses credentials.

2. Decryption happens in-process. Cookie values are decrypted in memory (PBKDF2 + AES-128-CBC), loaded into the Playwright context, and never written to disk in plaintext. The cookie picker UI never displays cookie values — only domain names and counts.

3. Database is read-only. gstack copies the Chromium cookie DB to a temp file (to avoid SQLite lock conflicts with the running browser) and opens it read-only. It never modifies your real browser's cookie database.

4. Key caching is per-session. The Keychain password + derived AES key are cached in memory for the server's lifetime. When the server shuts down (idle timeout or explicit stop), the cache is gone.

5. No cookie values in logs. Console, network, and dialog logs never contain cookie values. The cookies command outputs cookie metadata (domain, name, expiry) but values are truncated.

Shell injection prevention

The browser registry (Comet, Chrome, Arc, Brave, Edge) is hardcoded. Database paths are constructed from known constants, never from user input. Keychain access uses Bun.spawn() with explicit argument arrays, not shell string interpolation.

The ref system

Refs (@e1, @e2, @c1) are how the agent addresses page elements without writing CSS selectors or XPath.

How it works

1. Agent runs: $B snapshot -i
2. Server calls Playwright's page.accessibility.snapshot()
3. Parser walks the ARIA tree, assigns sequential refs: @e1, @e2, @e3...
4. For each ref, builds a Playwright Locator: getByRole(role, { name }).nth(index)
5. Stores Map<string, RefEntry> on the BrowserManager instance (role + name + Locator)
6. Returns the annotated tree as plain text

Later:
7. Agent runs: $B click @e3
8. Server resolves @e3 → Locator → locator.click()

Why Locators, not DOM mutation

The obvious approach is to inject data-ref="@e1" attributes into the DOM. This breaks on:

• CSP (Content Security Policy). Many production sites block DOM modification from scripts.
• React/Vue/Svelte hydration. Framework reconciliation can strip injected attributes.
• Shadow DOM. Can't reach inside shadow roots from the outside.

Playwright Locators are external to the DOM. They use the accessibility tree (which Chromium maintains internally) and getByRole() queries. No DOM mutation, no CSP issues, no framework conflicts.

Ref lifecycle

Refs are cleared on navigation (the framenavigated event on the main frame). This is correct — after navigation, all locators are stale. The agent must run snapshot again to get fresh refs. This is by design: stale refs should fail loudly, not click the wrong element.

Ref staleness detection

SPAs can mutate the DOM without triggering framenavigated (e.g. React router transitions, tab switches, modal opens). This makes refs stale even though the page URL didn't change. To catch this, resolveRef() performs an async count() check before using any ref:

resolveRef(@e3) → entry = refMap.get("e3")
                → count = await entry.locator.count()
                → if count === 0: throw "Ref @e3 is stale — element no longer exists. Run 'snapshot' to get fresh refs."
                → if count > 0: return { locator }

This fails fast (~5ms overhead) instead of letting Playwright's 30-second action timeout expire on a missing element. The RefEntry stores role and name metadata alongside the Locator so the error message can tell the agent what the element was.

Cursor-interactive refs (@c)

The -C flag finds elements that are clickable but not in the ARIA tree — things styled with cursor: pointer, elements with onclick attributes, or custom tabindex. These get @c1, @c2 refs in a separate namespace. This catches custom components that frameworks render as <div> but are actually buttons.

Logging architecture

Three ring buffers (50,000 entries each, O(1) push):

Browser events → CircularBuffer (in-memory) → Async flush to .gstack/*.log

Console messages, network requests, and dialog events each have their own buffer. Flushing happens every 1 second — the server appends only new entries since the last flush. This means:

• HTTP request handling is never blocked by disk I/O
• Logs survive server crashes (up to 1 second of data loss)
• Memory is bounded (50K entries × 3 buffers)
• Disk files are append-only, readable by external tools

The console, network, and dialog commands read from the in-memory buffers, not disk. Disk files are for post-mortem debugging.

SKILL.md template system

The problem

SKILL.md files tell Claude how to use the browse commands. If the docs list a flag that doesn't exist, or miss a command that was added, the agent hits errors. Hand-maintained docs always drift from code.

The solution

SKILL.md.tmpl          (human-written prose + placeholders)
       ↓
gen-skill-docs.ts      (reads source code metadata)
       ↓
SKILL.md               (committed, auto-generated sections)

Templates contain the workflows, tips, and examples that require human judgment. Placeholders are filled from source code at build time:

Placeholder	Source	What it generates
{{COMMAND_REFERENCE}}	commands.ts	Categorized command table
{{SNAPSHOT_FLAGS}}	snapshot.ts	Flag reference with examples
{{PREAMBLE}}	gen-skill-docs.ts	Startup block: update check, session tracking, contributor mode, AskUserQuestion format
{{BROWSE_SETUP}}	gen-skill-docs.ts	Binary discovery + setup instructions
{{BASE_BRANCH_DETECT}}	gen-skill-docs.ts	Dynamic base branch detection for PR-targeting skills (ship, review, qa, plan-ceo-review)
{{QA_METHODOLOGY}}	gen-skill-docs.ts	Shared QA methodology block for /qa and /qa-only
{{DESIGN_METHODOLOGY}}	gen-skill-docs.ts	Shared design audit methodology for /plan-design-review and /design-review
{{REVIEW_DASHBOARD}}	gen-skill-docs.ts	Review Readiness Dashboard for /ship pre-flight
{{TEST_BOOTSTRAP}}	gen-skill-docs.ts	Test framework detection, bootstrap, CI/CD setup for /qa, /ship, /design-review

This is structurally sound — if a command exists in code, it appears in docs. If it doesn't exist, it can't appear.

The preamble

Every skill starts with a {{PREAMBLE}} block that runs before the skill's own logic. It handles five things in a single bash command:

1. Update check — calls gstack-update-check, reports if an upgrade is available.
2. Session tracking — touches ~/.gstack/sessions/$PPID and counts active sessions (files modified in the last 2 hours). When 3+ sessions are running, all skills enter "ELI16 mode" — every question re-grounds the user on context because they're juggling windows.
3. Contributor mode — reads gstack_contributor from config. When true, the agent files casual field reports to ~/.gstack/contributor-logs/ when gstack itself misbehaves.
4. AskUserQuestion format — universal format: context, question, RECOMMENDATION: Choose X because ___, lettered options. Consistent across all skills.
5. Search Before Building — before building infrastructure or unfamiliar patterns, search first. Three layers of knowledge: tried-and-true (Layer 1), new-and-popular (Layer 2), first-principles (Layer 3). When first-principles reasoning reveals conventional wisdom is wrong, the agent names the "eureka moment" and logs it. See ETHOS.md for the full builder philosophy.

Why committed, not generated at runtime?

Three reasons:

1. Claude reads SKILL.md at skill load time. There's no build step when a user invokes /browse. The file must already exist and be correct.
2. CI can validate freshness. gen:skill-docs --dry-run + git diff --exit-code catches stale docs before merge.
3. Git blame works. You can see when a command was added and in which commit.

Template test tiers

Tier	What	Cost	Speed
1 — Static validation	Parse every $B command in SKILL.md, validate against registry	Free	<2s
2 — E2E via claude -p	Spawn real Claude session, run each skill, check for errors	~$3.85	~20min
3 — LLM-as-judge	Sonnet scores docs on clarity/completeness/actionability	~$0.15	~30s

Tier 1 runs on every bun test. Tiers 2+3 are gated behind EVALS=1. The idea is: catch 95% of issues for free, use LLMs only for judgment calls.

Command dispatch

Commands are categorized by side effects:

• READ (text, html, links, console, cookies, ...): No mutations. Safe to retry. Returns page state.
• WRITE (goto, click, fill, press, ...): Mutates page state. Not idempotent.
• META (snapshot, screenshot, tabs, chain, ...): Server-level operations that don't fit neatly into read/write.

This isn't just organizational. The server uses it for dispatch:

if (READ_COMMANDS.has(cmd))  → handleReadCommand(cmd, args, bm)
if (WRITE_COMMANDS.has(cmd)) → handleWriteCommand(cmd, args, bm)
if (META_COMMANDS.has(cmd))  → handleMetaCommand(cmd, args, bm, shutdown)

The help command returns all three sets so agents can self-discover available commands.

Error philosophy

Errors are for AI agents, not humans. Every error message must be actionable:

• "Element not found" → "Element not found or not interactable. Run snapshot -i to see available elements."
• "Selector matched multiple elements" → "Selector matched multiple elements. Use @refs from snapshot instead."
• Timeout → "Navigation timed out after 30s. The page may be slow or the URL may be wrong."

Playwright's native errors are rewritten through wrapError() to strip internal stack traces and add guidance. The agent should be able to read the error and know what to do next without human intervention.

Crash recovery

The server doesn't try to self-heal. If Chromium crashes (browser.on('disconnected')), the server exits immediately. The CLI detects the dead server on the next command and auto-restarts. This is simpler and more reliable than trying to reconnect to a half-dead browser process.

E2E test infrastructure

Session runner (test/helpers/session-runner.ts)

E2E tests spawn claude -p as a completely independent subprocess — not via the Agent SDK, which can't nest inside Claude Code sessions. The runner:

1. Writes the prompt to a temp file (avoids shell escaping issues)
2. Spawns sh -c 'cat prompt | claude -p --output-format stream-json --verbose'
3. Streams NDJSON from stdout for real-time progress
4. Races against a configurable timeout
5. Parses the full NDJSON transcript into structured results

The parseNDJSON() function is pure — no I/O, no side effects — making it independently testable.

Observability data flow

  skill-e2e-*.test.ts
        │
        │ generates runId, passes testName + runId to each call
        │
  ┌─────┼──────────────────────────────┐
  │     │                              │
  │  runSkillTest()              evalCollector
  │  (session-runner.ts)         (eval-store.ts)
  │     │                              │
  │  per tool call:              per addTest():
  │  ┌──┼──────────┐              savePartial()
  │  │  │          │                   │
  │  ▼  ▼          ▼                   ▼
  │ [HB] [PL]    [NJ]          _partial-e2e.json
  │  │    │        │             (atomic overwrite)
  │  │    │        │
  │  ▼    ▼        ▼
  │ e2e-  prog-  {name}
  │ live  ress   .ndjson
  │ .json .log
  │
  │  on failure:
  │  {name}-failure.json
  │
  │  ALL files in ~/.gstack-dev/
  │  Run dir: e2e-runs/{runId}/
  │
  │         eval-watch.ts
  │              │
  │        ┌─────┴─────┐
  │     read HB     read partial
  │        └─────┬─────┘
  │              ▼
  │        render dashboard
  │        (stale >10min? warn)

Split ownership: session-runner owns the heartbeat (current test state), eval-store owns partial results (completed test state). The watcher reads both. Neither component knows about the other — they share data only through the filesystem.

Non-fatal everything: All observability I/O is wrapped in try/catch. A write failure never causes a test to fail. The tests themselves are the source of truth; observability is best-effort.

Machine-readable diagnostics: Each test result includes exit_reason (success, timeout, error_max_turns, error_api, exit_code_N), timeout_at_turn, and last_tool_call. This enables jq queries like:

jq '.tests[] | select(.exit_reason == "timeout") | .last_tool_call' ~/.gstack-dev/evals/_partial-e2e.json

Eval persistence (test/helpers/eval-store.ts)

The EvalCollector accumulates test results and writes them in two ways:

1. Incremental: savePartial() writes _partial-e2e.json after each test (atomic: write .tmp, fs.renameSync). Survives kills.
2. Final: finalize() writes a timestamped eval file (e.g. e2e-20260314-143022.json). The partial file is never cleaned up — it persists alongside the final file for observability.

eval:compare diffs two eval runs. eval:summary aggregates stats across all runs in ~/.gstack-dev/evals/.

Test tiers

Tier	What	Cost	Speed
1 — Static validation	Parse $B commands, validate against registry, observability unit tests	Free	<5s
2 — E2E via claude -p	Spawn real Claude session, run each skill, scan for errors	~$3.85	~20min
3 — LLM-as-judge	Sonnet scores docs on clarity/completeness/actionability	~$0.15	~30s

Tier 1 runs on every bun test. Tiers 2+3 are gated behind EVALS=1. The idea: catch 95% of issues for free, use LLMs only for judgment calls and integration testing.

What's intentionally not here

• No WebSocket streaming. HTTP request/response is simpler, debuggable with curl, and fast enough. Streaming would add complexity for marginal benefit.
• No MCP protocol. MCP adds JSON schema overhead per request and requires a persistent connection. Plain HTTP + plain text output is lighter on tokens and easier to debug.
• No multi-user support. One server per workspace, one user. The token auth is defense-in-depth, not multi-tenancy.
• No Windows/Linux cookie decryption. macOS Keychain is the only supported credential store. Linux (GNOME Keyring/kwallet) and Windows (DPAPI) are architecturally possible but not implemented.
• No iframe support. Playwright can handle iframes but the ref system doesn't cross frame boundaries yet. This is the most-requested missing feature.