What is visual-asset-annotator?

An agent that scans written content for useful visuals, such as charts, illustrations, and diagrams, and records items that still need human work.

Use it to review article drafts, create charts from verified research, optionally generate images, and mark visual placeholders for later action.

It helps turn a text draft and checked research into a clearer, more visual piece without losing track of unsupported statistics or unfinished visuals.

visual-asset-annotator — Agent for Claude Code by indranilbanerjee

Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code

/plugin marketplace add indranilbanerjee/contentforge

Claude Code

/plugin install contentforge

Made for: Claude Code.

Or install contentforge, the plugin that ships this one along with the rest of its 22 skills, 9 commands, 13 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/agents/indranilbanerjee/contentforge/03.5-visual-asset-annotator.svg)](https://agentmods.dev/agents/indranilbanerjee/contentforge/03.5-visual-asset-annotator)

Your own site

<a href="https://agentmods.dev/agents/indranilbanerjee/contentforge/03.5-visual-asset-annotator"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/contentforge/03.5-visual-asset-annotator.svg" alt="Measured on agentmods" height="20"></a>

Per session 50 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 7,526 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00050	$0.07526
Opus 5	$0.00025	$0.03763
Sonnet 5	$0.00010	$0.01505
Haiku 4.5	$0.00005	$0.00753

Measured 6d ago against content hash 3ccee0df8d94, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

visual-asset-annotator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

visual-asset-annotator — 100% identical, 0 lines differ

agents/03.5-visual-asset-annotator.md · 602 lines

How it starts

The opening of the file, as written. The whole thing — 602 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Visual Asset Annotator Agent — ContentForge Phase 3.5

Role: Scan the draft for visual opportunities, generate data charts from verified statistics using matplotlib, optionally generate AI images (feature images, contextual illustrations, diagrams) via image generation MCP servers when user opts in, and create structured annotation markers for any remaining visuals that require human action.

INPUTS

The orchestrator passes you {brand-slug} and {run_id}. Read prior artifacts with the Read tool — do not expect them inlined in your prompt.

Read from:

~/.claude-marketing/{brand-slug}/runs/{run_id}/phase-3-draft.md — Draft v1 with [VISUAL-PLACEHOLDER: ...] markers + Draft Metadata block
~/.claude-marketing/{brand-slug}/runs/{run_id}/phase-2-factcheck.md — Verified Research Brief: Statistics Verification Report (which stats verified, at what confidence), resolved Citation Library
~/.claude-marketing/{brand-slug}/runs/{run_id}/phase-1-research.md — SERP analysis, competitor visual patterns (do top results use charts, tables, infographics?)

Do NOT call pipeline-tracker. Phase timing is handled exclusively by the orchestrator.

From Brand Profile:

Brand Colors — output_preferences.brand_colors.primary and secondary for chart styling
Content Type — Determines visual density target
Industry — Pharma/healthcare content requires higher chart accuracy standards

YOUR MISSION

Process the draft to add visual richness through three activities:

Scan for visual opportunities — Identify where charts, images, screenshots, or diagrams would enhance the content, starting with Phase 3 placeholders and detecting additional data-rich passages
Generate data visualizations — For statistics from verified sources, produce matplotlib chart specifications that render headlessly (Agg backend, no display required)
Create annotation markers — For visuals the pipeline cannot auto-generate (screenshots, stock photos, diagrams), insert structured HTML comment markers with precise instructions for human editors

Read the full file on GitHub · 602 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

6d ago First seen · 602 lines · 50 tokens per session scan A 3ccee0df8d94

Subscribe to this mod's changes

visual-asset-annotator is an agent published in the GitHub repository indranilbanerjee/contentforge (26 stars, last pushed 20d ago), licensed MIT. It adds 50 tokens to every session and 7,526 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Creative and media Research aeo claude-code claude-plugin content-pipeline fact-checking seo

seo-image-gen

SEO image analyst. Audits existing OG/social preview images, identifies missing or low-quality images, and creates an image generation plan with prompts for key pages. Does NOT auto-generate images.

AgriciDaniel/claude-seo · 42 tokens

comfy-researcher

Discovers and ranks ComfyUI custom node packs for a stated image-generation problem.

artokun/comfyui-mcp · 22 tokens

image-visual-qa

Vision QA for generated article images. Reads every PNG in images.json, scores 13 defect classes (composition, anatomy, garbled text, empty labels, brand drift, content mismatch, contrast, AI-look, chart legibility), rewrites prompts and triggers targeted regeneration (max 2 rounds), writes image-qa-report.json. Never…

XuanRanL/loamwright-SEO-Skill · 83 tokens

visual-asset-annotator