ACOS v11: Visual Intelligence and Site Integrity

What changed, what broke first, and the systems that make sure it stays fixed.

TL;DR

ACOS v11 ships two major systems born from real failures: a 5-layer Visual Quality System (after 8 out of 21 batch-generated hero images shipped with critical defects) and a Site Integrity Audit that checked 302 pages, 816 links, 16 products, and 118 blog articles — finding 13 broken links, 97 orphan pages, and 17 articles with missing frontmatter. Also new: a two-model Prompt Optimizer that raises image prompt scores from ~49 to 95/100, and a VIS Registry Dedup Check across 408 indexed images. Everything runs automatically through hooks in Claude Code. Free on GitHub.

What Went Wrong With 21 Blog Hero Images?

In late March 2026, I batch-generated 21 hero images for blog articles. The generation itself was fast — roughly 20 minutes for all 21. The review happened after.

Eight of them had critical issues:

• Outdated model names baked into the image (referencing models that no longer exist or were renamed)
• Fake UI screenshots that looked plausible at thumbnail size but contained nonsense text
• Unauthorized logos — corporate logos that should never appear on frankx.ai (see: Oracle logo restriction)
• Leaked prompt text rendered directly into the image, visible at full resolution

None of these were caught before the images hit the blog. The generation pipeline had zero quality gates. The workflow was: prompt → generate → save → publish.

That sequence is the problem. Not the generation model. Not the prompts. The process.

How Does the 5-Layer Visual Quality System Work?

The fix is not "be more careful." The fix is five automated layers that make carelessness structurally impossible.

Layer 1: CLAUDE.md Rules

The foundation. Written rules in the project's CLAUDE.md that define three image categories with different quality requirements:

The key distinction: the rule is not "never use text in images." That rule is wrong. The MCP server architecture diagram — which included labeled components, protocol names, and connection types — was the single best image in the batch. The rule is: never use unverified text.

Accurate diagrams with researched labels are excellent. Random text from an LLM's imagination is not.

Layer 2: Skill Auto-Triggers

When ACOS detects image-related work (keywords like "generate," "hero image," "visual," "diagram"), it automatically loads the visual-creation and vis skills. These inject the full quality protocol into the session context without the user needing to remember to activate them.

# Skill auto-trigger pattern
triggers:
  - pattern: "generate.*image|hero.*image|visual|diagram|infographic"
    skills: ["visual-creation", "vis", "design-thinking"]
    priority: high

Layer 3: PreToolUse Hook

This is the enforcement layer. Before any image generation tool fires, a hook script runs four checks:

#!/bin/bash
# PreToolUse hook — visual quality gate
# Runs before: nanobanana.generate_image, vis.*, image tools

TOOL_NAME="$1"
TOOL_INPUT="$2"

# 1. Model tier check — no cheap models for final assets
if echo "$TOOL_INPUT" | grep -q "flash"; then
  echo "BLOCKED: Flash-tier models produce inconsistent text rendering."
  echo "Use pro-tier for any image containing text or branded elements."
  exit 1
fi

# 2. Grounding check — text content must reference verified sources
if echo "$TOOL_INPUT" | grep -qi "diagram\|architecture\|label\|text"; then
  echo "WARNING: Image contains text elements."
  echo "Verify all labels against source documentation before generating."
  echo "Required: list each text element and its verification source."
fi

# 3. Batch limit — max 3 images before mandatory review
BATCH_COUNT=$(cat /tmp/acos-image-batch-count 2>/dev/null || echo "0")
if [ "$BATCH_COUNT" -ge 3 ]; then
  echo "PAUSED: Batch limit reached (3 images)."
  echo "Review generated images before continuing."
  echo "Run: vis-audit to check quality."
  exit 1
fi
echo $((BATCH_COUNT + 1)) > /tmp/acos-image-batch-count

# 4. Risk pattern detection
if echo "$TOOL_INPUT" | grep -qi "oracle\|logo\|brand\|trademark"; then
  echo "BLOCKED: Potential trademark/brand content detected."
  echo "Corporate logos and trademarks require explicit approval."
  exit 1
fi

# 5. VIS dedup check — prevent regenerating existing assets
PROMPT_HASH=$(echo "$TOOL_INPUT" | md5sum | cut -d' ' -f1)
if grep -q "$PROMPT_HASH" data/visual-registry.json 2>/dev/null; then
  echo "SKIPPED: Similar image already exists in VIS registry."
  echo "Check data/visual-registry.json before generating duplicates."
  exit 1
fi

The batch limit is the most important check. The original failure was a batch of 21. With a hard limit of 3 before forced review, the worst case drops from "21 broken images in production" to "3 images waiting for review."

Layer 4: PostToolUse Hook

After generation, the PostToolUse hook:

1. Logs the generation to data/visual-registry.json with metadata (prompt, model, timestamp, tags)
2. Flags the image as "reviewStatus": "pending" — it exists but is not approved
3. Emits a warning if the session has pending reviews: "You have 3 unreviewed images. Run /vis-audit before publishing."

Layer 5: Council Agent Review

For branded and high-stakes images, ACOS spawns a council review. Three perspectives evaluate the image:

• Brand Guardian: Does this match frankx.ai visual identity? Correct colors, no unauthorized marks?
• Technical Reviewer: Is all text accurate? Do diagrams reflect real architecture?
• Accessibility Auditor: Sufficient contrast? Alt text prepared? Works at mobile sizes?

The council pattern is already proven in ACOS for content review. Extending it to visual assets was straightforward.

The Design Thinking 80/20 Rule

The deeper lesson from the image failure is about time allocation. The original workflow spent approximately:

• 5% of time on concepting (what should this image communicate?)
• 10% on prompting (writing the generation prompt)
• 80% on generating (running the model, waiting for results)
• 5% on review (a quick glance before publishing)

ACOS v11 inverts this:

• 80% concepting and prompt engineering (what, why, verified text, reference images)
• 20% generating (one or two targeted generations with a refined prompt)

The prompt optimizer (described below) is the tool that makes this inversion practical.

What Did the Site Integrity Audit Find?

Before v11, frankx.ai had never been audited as a whole. Individual pages got attention. The site as a system did not.

The audit script (scripts/pre-deploy-audit.mjs) runs in 6.3 seconds and checks everything:

The Numbers

What "97 Orphan Pages" Actually Means

An orphan page is a page that exists but has zero inbound links from the rest of the site. Users can only reach it via direct URL or search. On a 302-page site, having 97 orphans means 32% of the site is effectively invisible to navigation.

The fix was not deleting orphan pages — that violates the decision-making principle of never removing content that might have external links or search traffic. Instead, 14 new navigation links were added to connect the most valuable orphans to existing page structures. The remaining orphans are monitored but intentionally unlinked (admin pages, draft content, utility routes).

What "15/16 Products With Broken Delivery" Means

Fifteen out of sixteen products had checkout flows that either pointed to placeholder Stripe links, returned 404 on the download endpoint, or had email delivery templates referencing non-existent assets. Only one product — the ACOS guide PDF — had a fully working purchase-to-delivery pipeline.

This is a revenue problem masquerading as a technical problem. Every product page looks correct. The CTAs work. The forms submit. But the backend delivery was incomplete for 94% of the catalog.

The Dashboard

All audit data flows into seven JSON files and renders in the private /admin/site-health dashboard:

data/
├── audit-broken-links.json      # 13 broken links with source pages
├── audit-orphan-pages.json       # 97 orphan pages with traffic data
├── audit-product-delivery.json   # 16 products, delivery status
├── audit-blog-quality.json       # 118 articles, quality scores
├── audit-blog-categories.json    # Category coverage map
├── audit-nav-coverage.json       # Navigation completeness
└── audit-summary.json            # Roll-up metrics, timestamp

The pre-deploy audit runs automatically before every production push. If broken links exceed the threshold, the deploy pauses.

How Does the Prompt Optimizer Work?

The prompt optimizer uses a two-model pattern: a text model enhances the prompt before an image model generates the visual.

The Problem With Raw Prompts

A typical first-draft image prompt:

"Create a hero image for a blog post about AI agent orchestration"

This prompt scores roughly 49/100 on our quality rubric (specificity, visual detail, composition guidance, style consistency, brand alignment).

The Two-Model Pattern

Step 1 — Text model enriches the prompt:

The text model (Claude, in this case) receives the raw prompt plus the brand visual DNA (data/brand-visual-dna.json) and outputs an enhanced prompt with:

• Specific lighting direction and color temperature
• Texture and material descriptions
• Camera angle and focal length
• Composition rules (rule of thirds, negative space)
• Brand color constraints (dark gunmetal #1a1a1a, electric blue #00bfff, violet #7c3aed)
• Explicit exclusions (no text unless verified, no logos, no fake UI)

Step 2 — Image model generates from the enriched prompt.

The enhanced prompt scores 95/100 on the same rubric. The difference is not the image model — it is the input quality.

Raw prompt:    "AI agent orchestration hero image"
                → Score: 49/100
                → Result: Generic, off-brand, random composition

Enhanced prompt: "Isometric dark workspace, gunmetal #1a1a1a background,
                  three luminous agent nodes connected by electric blue
                  #00bfff data streams, subtle violet #7c3aed core glow
                  on central orchestrator, volumetric fog, 45-degree
                  camera angle, cinematic depth of field f/2.8, no text,
                  no logos, no UI elements, 16:9 aspect ratio"
                → Score: 95/100
                → Result: On-brand, intentional, reproducible

The optimizer runs automatically when the visual-creation skill is active. It adds roughly 10 seconds per prompt but eliminates most regeneration cycles.

What Does the VIS Registry Dedup Check Do?

The Visual Intelligence System (VIS) tracks 408 images across 37 directories in data/visual-registry.json. Each entry includes:

• File path and dimensions
• Tags, mood, theme, suitability scores
• Generation prompt (if AI-generated)
• Content hash for dedup

The PreToolUse hook queries this registry before every generation. If a semantically similar image already exists — same subject, similar composition, matching tags — the hook blocks generation and points to the existing asset.

This prevents the most common waste pattern: generating a new "AI architecture" hero image when three already exist in the registry. Over 21 articles, this check would have prevented at least 6 redundant generations.

What Changed From v10 to v11?

ACOS v10 was about autonomous intelligence — making the system learn from itself. v11 is about integrity — making sure what the system produces and what the site contains are both correct.

How to Use the New Systems

Run a Site Integrity Audit

node scripts/pre-deploy-audit.mjs

Output: seven JSON files in data/, summary printed to console, dashboard updated at /admin/site-health.

Check Visual Registry Before Generating

# Search existing assets before generating new ones
/vis-search "agent orchestration architecture"

Enable the Full Quality Pipeline

The visual quality system activates automatically when ACOS detects image work. To manually ensure it is loaded:

# In Claude Code
/acos-score  # Check current system status

All hooks, skills, and audit infrastructure ship with ACOS v11. No additional dependencies.

FAQ

Is ACOS v11 backwards compatible with v10?

Yes. The visual quality system and site integrity audit are additive — they add new hooks and scripts without modifying existing v10 systems. Experience Replay, Agent IAM, Circuit Breaker, and all v10 features continue to work unchanged.

Does the 5-layer visual system slow down image generation?

The PreToolUse hook adds approximately 200ms per generation (registry lookup, risk pattern scan). The prompt optimizer adds roughly 10 seconds (text model enhancement). Total overhead per image: ~10 seconds. Given that it eliminates most regeneration cycles (which cost 30-60 seconds each), the net effect is faster.

Can I use the site integrity audit on my own Next.js site?

The audit script (scripts/pre-deploy-audit.mjs) is designed for Next.js App Router projects. It crawls the app/ directory for routes, checks content/ for MDX frontmatter, and validates links. You would need to adjust product verification logic for your own checkout system, but the page and link auditing works on any Next.js 14+ project.

Why a batch limit of 3 instead of 5 or 10?

Three is the maximum number of images a single reviewer can meaningfully evaluate in one pass without quality degradation. At 5+, reviewers start rubber-stamping. The number comes from design review literature, not arbitrary choice. You can adjust it in the PreToolUse hook.

What happens when the pre-deploy audit finds issues?

Broken links above the threshold (currently set to 0) block the deploy with a non-zero exit code. Missing frontmatter and orphan pages generate warnings but do not block. Product delivery failures are logged and flagged on the dashboard but do not block deploys — they require manual intervention since fixing delivery pipelines involves Stripe configuration, not code.

How does the prompt optimizer handle non-English content?

The text model enhancement works in any language the underlying LLM supports. Brand visual DNA constraints (colors, composition rules) are language-independent. The optimizer has been tested with English and German prompts.

Where can I get ACOS v11?

ACOS is open source: github.com/frankxai/agentic-creator-os. The visual quality system, site integrity audit, and prompt optimizer all ship in v11. Setup takes roughly 15 minutes for an existing Claude Code project.

What Comes Next

The site integrity audit revealed something that manual reviews consistently missed: the gap between "pages that exist" and "pages that work end-to-end." Having 302 pages means nothing if 97 are unreachable and 15 out of 16 products cannot deliver what they sell.

ACOS v11 is the first version where the system checks its own output and the platform it publishes to. Visual quality and site integrity are two sides of the same principle: what ships must be correct, complete, and verified.

The visual quality system did not emerge from a planning document. It emerged from 8 broken images that made it to production. That is how most good systems start — with a failure specific enough to design against.

ACOS is free and open source. Built for creators who ship.