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 skills add AIScientists-Dev/WorldSeed --skill asset-sourcinggit clone --depth 1 https://github.com/AIScientists-Dev/WorldSeedWrote 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/skills/aiscientists-dev/worldseed/asset-sourcing)<a href="https://agentmods.dev/skills/aiscientists-dev/worldseed/asset-sourcing"><img src="https://agentmods.dev/badge/skills/aiscientists-dev/worldseed/asset-sourcing.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.02264 |
| Opus 5 | $0.00016 | $0.01132 |
| Sonnet 5 | $0.00006 | $0.00453 |
| Haiku 4.5 | $0.00003 | $0.00226 |
Grade A, and why
asset-sourcing 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 — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Asset Sourcing
Use this skill when a user wants image candidates for a new or existing WorldSeed scene and the goal is to build a reviewable asset board, not auto-pick final assets.
Goal
Produce a local review bundle:
- candidate metadata
- selectively downloaded review-size candidate images
- a structured
manifest.json - an entity-first
review.html - a minimal selected-picks export from the HTML picker
Default bundle:
tmp/asset-sourcing/{scene_id}/
manifest.json
review.html
images/{entity_id}/...
This skill is standalone. It stops at a reviewable bundle and selected picks. Import into a scene should happen later as a separate step.
Supported Sources
Default sources:
openverseaicclevelandvamwellcomeycbamet
Optional sources:
wikimediawaltersnasa
Read references/source-catalog.md before using a source you have not used recently.
Execution Rules
- Use supported sources only.
- Treat sourcing as entity-grounded, not exact-illustration-only.
exact,adjacent, andvibecan all be usable. - Do not stop after one keyword search.
- Let the AI choose keywords and source order per entity.
- Search only the sources that are justified for the current entity.
- Search different sources in parallel when useful.
- Start with likely fast sources and widen only when needed.
- Treat
metas a later pass, not a universal first call. - Only use optional sources when they are justified:
wikimediafor known motifs, named figures, or coverage gapswaltersfor watches, keys, portraits, and manuscript-adjacent objectsnasafor cosmic or scientific entities only
- Preserve source URLs, image URLs, and rights text in the manifest.
- Search first. Download only shortlisted candidates by default.
- Treat metadata as recall only. Final retention and top picks must be image-verified.
- Reject obvious homonym and name-collision matches when the image subject is wrong, even if the title contains the entity token.
- Prefer a visually coherent set across entities. When multiple candidates are equally valid, favor the ones that fit the scene's overall medium, period, and mood.
- Avoid obvious horror, gore, medical shock, body horror, or otherwise unsettling imagery unless the user explicitly wants that tone.
- Do not auto-pick final assets. The default output is a candidate board for agent/human review in HTML.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 396 B
- references/review-output.md 4.4 KB
- references/source-catalog.md 7.2 KB
- scripts/apply_visual_review.py 4.1 KB runs code
- scripts/download_candidates.py 4.4 KB runs code
- scripts/init_bundle.py 1.9 KB runs code
- scripts/render_review.py 25 KB runs code
- scripts/search_candidates.py 40 KB runs code
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 · 302 lines · 32 tokens per session scan A 1d9ca129a5cf
asset-sourcing is a skill published in the GitHub repository AIScientists-Dev/WorldSeed (815 stars, last pushed 4mo ago), licensed MIT. It adds 32 tokens to every session and 2,264 once invoked, about $0.0002 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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