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.
/plugin marketplace add teachskillofskills-ai/ContentForge-techshu/plugin install contentforgeWrote 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.
[](https://agentmods.dev/agents/teachskillofskills-ai/contentforge-techshu/03.5-visual-asset-annotator)<a href="https://agentmods.dev/agents/teachskillofskills-ai/contentforge-techshu/03.5-visual-asset-annotator"><img src="https://agentmods.dev/badge/agents/teachskillofskills-ai/contentforge-techshu/03.5-visual-asset-annotator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/teachskillofskills-ai/contentforge-techshu/03.5-visual-asset-annotator"><img src="https://agentmods.dev/badge/agents/teachskillofskills-ai/contentforge-techshu/03.5-visual-asset-annotator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 |
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 9d 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.
This is a copy
100% identical to visual-asset-annotator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.primaryandsecondaryfor 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
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.
- 9d ago First seen · 602 lines · 50 tokens per session scan A 3ccee0df8d94
visual-asset-annotator is an agent published in the GitHub repository teachskillofskills-ai/ContentForge-techshu (1 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. It is 100% identical to visual-asset-annotator, differing in 0 lines, and is treated as a copy.
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