Gtm Diligence

GTM Diligence — pre-ship application audit

A comprehensive, adversarial audit of a whole application before it goes to market. Unlike a diff review, this traces important flows end-to-end — codebase, architecture, data flows, API interactions, authN/authZ, state management, async operations, error handling, and user-facing surfaces — and challenges the assumptions that only hold under ideal content, a single viewport, and a cooperative user.

The output is a decision: Safe to ship / Ship with known risks / Do not ship, backed by evidence. Be adversarial in security, systematic in accessibility, precise in UI.

Credit: prompt by @shugarDadddy.

When to use

• Before a launch, GA, or handing an app to real users
• "Is this safe to ship?", "production audit", "ship-readiness / GTM diligence"
• You want one report that spans security + reliability + a11y + UI, not a single-diff review

Not for: reviewing one diff/PR (use code-review), red-teaming a plan or spec (use adversarial-review / review-council), or pure visual/3D critique (use visual-critique).

Before you start (mandatory scope)

Every GTM audit confirms these first — an end-to-end audit is expensive and easy to mis-target. If the user doesn't answer, proceed only as an explicitly capped partial audit and say so.

1. Target + environment — which repo/app + commit/SHA, and can it actually be run (dev server, staging, prod-only)?
2. Priority flows — the top user-critical flows to trace (auth, checkout, submit…). No preference → pick the flows touching money, auth, or data-writes first.
3. Available test material — extra role/tenant accounts, seed data, or credentials? Their absence caps what authz / cross-tenant / concurrency checks can confirm (see coverage rule).

Declare the audit mode (top of the report)

State one, and cap the verdict accordingly (see release gate):

• Runtime verified — priority flows were actually exercised (app run, browser/requests driven).
• Partial runtime — some flows exercised, others static-only. List which is which.
• Static only — no runtime access. All runtime-dependent findings are Needs Verification; the audit may not call itself complete.

A category that requires runtime evidence (a11y contrast/screen-reader, races, CSRF, cross-tenant) but wasn't exercised is a coverage gap → Needs Verification, never a silent "no findings".

Rules of engagement

1. Do NOT modify code during the initial audit. Produce the complete findings report first.
2. Trace flows, don't skim files. Follow auth, checkout, submit, etc. end-to-end. For each priority flow, produce a one-row trace: route → client action → server endpoint/action → authz check → data stores touched → external services → failure states tested → evidence. No trace row = the flow was not actually traced.
3. Evidence over speculation — enforced, not goodwill. Every finding carries an evidence type (see report format). A finding with no evidence/repro is a Risk hypothesis, labelled as such, severity capped at Medium — it is not a defect.
4. Missing coverage ≠ no finding. If you couldn't test something, it's a coverage gap (Needs Verification), not a pass. Never let "nothing found" stand in for "tested and clean".
5. Don't assume client-side validation is sufficient. Check every trust boundary server-side.
6. Prioritise by real-world impact and exploitability, not by how easy the finding was to spot.

What to investigate

Many of these can only be confirmed at runtime. Where you have the app running, exercise it (drive the browser, fire concurrent/raw requests, run axe/contrast/keyboard checks). Where you don't, report the item as a static risk → Needs Verification and say what tool/access would confirm it. Do not present a static inference as a confirmed runtime finding.

Security & data exposure (be adversarial)

• AuthN/authZ flaws: missing server-side permission checks, privilege escalation, IDOR, cross-tenant data access.
• Sensitive data leaked via client code, env vars, API responses, logs, analytics, URLs, local/session storage, cookies, error messages, or source maps.
• Injection: SQL, command, template, prompt, HTML, script.
• XSS, CSRF, SSRF, open redirects, unsafe file uploads, path traversal, weak sessions, insecure token storage, missing security boundaries.
• Overly permissive DB rules, endpoints, CORS, storage buckets, webhook handlers, 3rd-party integrations.
• Secrets, API keys, credentials, internal endpoints, PII, or implementation details exposed.
• Missing validation/sanitisation at trust boundaries.

Race conditions, concurrency & state integrity

• Duplicate submissions from repeated clicks, retries, refreshes, concurrent requests.
• Non-idempotent operations creating duplicate records, payments, messages, bookings, jobs.
• Stale state, failed optimistic updates, lost updates, conflicting/out-of-order writes.
• Effects, subscriptions, listeners, timers, requests not cleaned up.
• Actions left available while an operation is already in progress.
• Cache invalidation bugs; client vs server vs persisted state inconsistency.
• Multi-tab, multi-device, poor-network scenarios.

Reliability & failure handling

• Unhandled rejections, swallowed errors, silent failures, infinite loading, broken retries, incomplete rollback.
• Missing loading / empty / error / offline / timeout / partial-success states.
• Failure paths that leave data or UI inconsistent.
• Unsafe assumptions about API shape, nullability, ordering, timing, network availability.
• Memory leaks, unnecessary rerenders, expensive ops, obvious perf bottlenecks affecting UX. (This is a static performance-risk review unless you run Lighthouse / profiles / measure a budget — say which. Don't imply measured perf you didn't measure.)

Accessibility (systematic — WCAG 2.2 AA)

Contrast, focus-trap, live-region, screen-reader, and zoom/reflow checks require runtime tooling (axe, computed-contrast, keyboard traversal, an actual screen reader). Without it, these are static a11y risks → Needs Verification — don't claim WCAG conformance from reading markup.

• Semantic HTML: landmarks, headings, labels, lists, tables, buttons, links.
• Keyboard nav, logical tab order, focus visibility, focus trapping, focus restoration.
• Accessible names/labels/descriptions and correct ARIA.
• Colour contrast, legibility, touch-target size, zoom, reduced-motion, colour-only meaning.
• Screen-reader behaviour for modals, menus, dropdowns, tabs, toasts, validation errors, loading states, and dynamically updated content.
• Forms: instructions, accessible validation, autocomplete attributes, error recovery.

Visual & interaction consistency (precise)

• Inconsistent spacing, type, colour, radii, shadows, icon sizing, alignment, dimensions, responsive behaviour.
• Components that look identical but behave differently (or vice-versa); inconsistent design-token and shared-component use.
• Missing/inconsistent hover, focus, active, selected, disabled, loading, success, warning, destructive, error states.
• Layout shift, clipping, overflow, truncation, wrapping, breakpoint issues, inconsistent empties.
• Copy: terminology drift, capitalisation, punctuation, date/number formats, action labels.

Responsive & edge-case behaviour

• Narrow mobile, tablet, desktop, ultra-wide, zoomed, large-text settings.
• Long names/emails, translated/expanded text, empty values, huge datasets, zero-result states, malformed/unexpected content.
• Assumptions that only work with ideal content or one viewport.

Report format

1. Coverage matrix (before any findings)

So "nothing found" can never masquerade as "tested and clean". One row per category:

Status ∈ Verified / Static reviewed / Not tested / N/A. A Not tested runtime-critical row caps the verdict (see release gate).

2. Findings

Ordering & dedup: one finding per root cause (list all affected instances under it, don't file ten copies). Sort by severity, then exploitability, then affected users/data, then confidence.

Severity (impact + exploitability, independent of confidence):

• Critical — exploitable now, or data-loss / auth-bypass / payment corruption in a priority flow.
• High — serious impact but gated (needs a role, a race window, specific input).
• Medium — real but limited blast radius, or a Risk hypothesis with no confirmed exploit.
• Low / Informational — hygiene, defence-in-depth, cosmetic.

For every finding report:

• Severity and Category (Security / Race Condition / Reliability / Accessibility / Performance / Visual Consistency)
• Location: exact file:line, component, function, endpoint, or flow
• Issue / Impact: what's wrong + what can realistically happen in production
• Evidence type + evidence: one of file:line + source→sink path / request/response / browser or tool output / failing command / screenshot. No evidence → label "Risk hypothesis", severity ≤ Medium — not a defect.
• Reproduction: concrete steps (required for Critical/High).
• Recommended fix: specific, actionable remediation.
• Confidence: Confirmed / High Confidence / Needs Verification (a separate axis from severity).

Deliverables (after the report)

1. Prioritised remediation plan — ordered by real-world impact.
2. Quick wins — safe to fix with low regression risk.
3. Deeper work — issues needing architectural change or further investigation.
4. Release recommendation — apply the release gate below (don't freehand it), then justify.

Release gate (verdict is a function of findings + coverage, not vibes)

Two audits of the same app should reach the same verdict. Take the first row that matches:

State the row you matched and why. "Safe to ship" requires runtime verification of the priority flows — a static-only audit can never award it.

Edge cases & failure modes