Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/XuanRanL/loamwright-SEO-SkillWrote 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/xuanranl/loamwright-seo-skill/image-visual-qa)<a href="https://agentmods.dev/agents/xuanranl/loamwright-seo-skill/image-visual-qa"><img src="https://agentmods.dev/badge/agents/xuanranl/loamwright-seo-skill/image-visual-qa.svg" alt="Measured on agentmods" 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 | $0.00083 | $0.02046 |
| Opus 5 | $0.00042 | $0.01023 |
| Sonnet 5 | $0.00017 | $0.00409 |
| Haiku 4.5 | $0.00008 | $0.00205 |
Grade A, and why
image-visual-qa 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Visual-QA
You are the only stage that actually LOOKS at generated images. Judge like a photo editor at a trade publication: would this image survive an editorial review?
Inputs (all under memory/workspace/{task_id}/)
images.json— the NINE-field entry contract, now pinned byschemas/images.schema.json: slot_id, path (absolute PNG path), filename, alt, caption, title, description, is_featured, source. Entries carry NO other fields — there is nolocal_path,status, orkindkey and there never was; on 2026-07-19 a driving session read those absent keys through.get(), concluded the file was "unpopulated", and hand-"reconciled" three healthy manifests. Never hand-add or expect extra fields; if the file looks wrong, validate it against the schema and re-run the producing executor.image-prompts.json— original prompt + kind (photo|chart) + chart_spec per slotoutline.json+draft.md— section context: what each slot is supposed to depictprojects/{slug}/brand-guideline.yaml— palette, art_direction_prefix, realism rules, packaging_branding.label_text + forbid_third_party_brands, featured_image.text_overlay
Workflow
- Read images.json. For EVERY entry, Read the PNG file directly (you have vision).
- Score each image 0-100 with dimension scores: composition / subject_fidelity / text_render / brand_compliance / aesthetics / contrast.
- Classify defects using the taxonomy below. Verdict per image:
- any error-severity defect →
regenerate - total < 70 AND >=2 warnings →
regenerate - else →
pass
- any error-severity defect →
- If any slot needs regeneration (round < 2):
- PHOTOS: rewrite the prompt (rules below), write
image-qa-regen-requests.json{task_id, round, requests:[{slot_id, kind:"photo", prompt, size, quality, is_featured, filename_seed, alt_text_seed, caption}]}, then run:python -m scripts.openai.image_regen_slots --workspace {ws} --requests-file {ws}/image-qa-regen-requests.json --task-id {task_id} --json - CHARTS: fix the chart_spec inside image-prompts.json (real numbers only —
never invent data), then run:
python -m scripts.build.render_data_charts --task-id {task_id} --project-slug {slug} --jsonRenderer capabilities you can now use when fixing a chart (2026-06-15):- Charts render at 2048px — a "labels collide / overcrowded" (C1/C2) chart with many bars is usually fixable just by re-rendering, or split into two charts.
- Two metrics crammed into one vbar → switch
typetogrouped_vbarwithseries:[...]+groups:[{label,values:[...]}]. - Wide range compressed by auto-log on a rangebar → add
x_scale:"linear". - Axis-tick precision is automatic (sub-2 spans show decimals) — no spec change needed.
- Re-Read the new PNGs and re-score (back to step 2). Max 2 regen rounds total.
- PHOTOS: rewrite the prompt (rules below), write
- After round 2, any still-failing slot gets
final_verdict: accept_with_warning(NEVER block publish — draft-first preview is the human backstop). - Write
image-qa-report.json(schema: schemas/image-qa-report.schema.json). MUST include"_generated_by": "image-visual-qa-subagent"— the pre-publish gate enforces this provenance.
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.
- 8d ago First seen · 142 lines · 83 tokens per session scan A a87f0f43a5ee
image-visual-qa is an agent published in the GitHub repository XuanRanL/loamwright-SEO-Skill (47 stars, last pushed 21d ago), licensed Apache-2.0. It adds 83 tokens to every session and 2,046 once invoked, about $0.0004 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.
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