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.
npx agentmods add skills/adologyai/content-intelligence-plugin/thumbnailsnpx skills add adologyai/content-intelligence-plugin --skill thumbnailsgit clone --depth 1 https://github.com/adologyai/content-intelligence-pluginWhat 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 | $0.00146 | $0.01505 |
| Opus 5 | $0.00073 | $0.00753 |
| Sonnet 5 | $0.00029 | $0.00301 |
| Haiku 4.5 | $0.00015 | $0.00151 |
Grade A, and why
thumbnails scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import base64, mimetypes, urllib.request How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thumbnails
Every item Adology returns from analyze carries a thumbnail in its base set — a poster
image, so a video item resolves to a still frame rather than a video file. Alongside it is
url, the post's own permalink. Those two fields are the whole visual citation.
An item with no stored asset comes back with an empty thumbnail. That's a real state, not
an error: drop the item, or swap it for the next candidate, rather than rendering a broken
image or inventing a placeholder.
Getting thumbnail URLs
Thumbnails ride on analyze. Two shapes cover almost everything:
- You already know which posts you want —
analyze({ projectId, itemIds: [...] }). The by-id deep dive returns each item's full creative read plusurlandthumbnail. This is the hydration step after any ranking: rank withquery_items(which carries lift multiples,isOutlier, andexternalUrl), collect theitemIdvalues, then hydrate them here for the images and creative fields. - You want a set that matches a description —
analyze({ projectId, query, distribution })withdistribution: 'top'for the highest-engagement items per feed,'recent'for newest, or'exhaustive'withsortByfor a deterministic ranked page you can page through.
Add mediaTypeFilter: 'image' or 'video' to keep the set to one media class — worth doing
whenever the question is about the frame itself, since a video's poster and a still image are
different creative artifacts even though both arrive as a thumbnail.
Reading what's in the frame
For visual and first-frame analysis, ask analyze for the fields that describe the image
rather than the copy: visualDescription, visualConcept, productDisplayStyle,
productionStyle, creativeExecution, and narrativeFormat. Pair those with
mediaTypeFilter so you're comparing like with like.
To quantify a visual pattern instead of reading individual frames, pivot on the scope's
visual label dimensions. Discover which dimensions exist first — list_labels({ projectId }),
or get_table_data({ projectId, listDimensions: true }) — then pivot:
get_table_data({ projectId, rows: ['<visual dimension>'], columns: 'focalVsRest', focalBrand, metrics: ['count','useRate','medianLikes'], mediaTypes: ['video'] }). Assumed dimension names
produce empty tables, so always take them from the discovery call.
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.
- yesterday First seen · 128 lines · 146 tokens per session scan A 164392c85f55
thumbnails is a skill published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 27d ago), licensed Apache-2.0. It adds 146 tokens to every session and 1,505 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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