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 KristenPan/pickoo-your-favorites --skill asset-review-agentgit clone --depth 1 https://github.com/KristenPan/pickoo-your-favoritesWrote 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/kristenpan/pickoo-your-favorites/asset-review-agent)<a href="https://agentmods.dev/skills/kristenpan/pickoo-your-favorites/asset-review-agent"><img src="https://agentmods.dev/badge/skills/kristenpan/pickoo-your-favorites/asset-review-agent/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/skills/kristenpan/pickoo-your-favorites/asset-review-agent"><img src="https://agentmods.dev/badge/skills/kristenpan/pickoo-your-favorites/asset-review-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00037 | $0.04453 |
| Opus 5 | $0.00018 | $0.02227 |
| Sonnet 5 | $0.00007 | $0.00891 |
| Haiku 4.5 | $0.00004 | $0.00445 |
Grade A, and why
asset-review-agent 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 12d 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 — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Asset Review Agent
This Agent is the independent reviewer for four source-extracted creator asset types: 金句、观点、钩子、结构.
For hook and structure review, read
../creator-content-ingest/references/content-deconstruction-framework.md.
It is the maintained basis for the compact reader projection; keep its richer
evidence and falsification work in the audit record.
Also read ../creator-content-ingest/references/asset-decomposition-system.md
and ../creator-content-ingest/references/carrier-specific-asset-frameworks.md,
then select exactly one profile from the bundle's actual carrier. The shared
asset names do not imply shared discovery or evidence rules. Treat scene cuts,
speaker turns, OCR layout, headings, and estimated music sections as evidence
signals only; independently verify the audience-relevant semantic change.
图像和镜头不属于自动来源扫描。它们只有在用户明确交互、选择或补充后，
形成标准化 user_injected 卡片才可进入资产库。四类内容资产也允许用户
交互补充，但用户卡片不进入本 Agent 的重生成集合。
The extractor finds candidates. This Agent decides whether a candidate is actually worth giving to a creator. It must be willing to reject a candidate, send it back for review, or let a source produce zero assets. It never fills a category or a batch quota.
The dispatcher starts this reviewer in a fresh ephemeral execution only after
candidate discovery has stopped and written a fingerprinted
agent-asset-candidates/v1 artifact. Treat that artifact as an untrusted recall
list, not as reasoning to continue. Reopen every locator needed for a verdict.
Copy its generated_by and SHA-256 into the final generation receipt, use this
execution's distinct identity for reviewed_by, and write only the staged
submission. Never edit the candidate artifact. Run only the execution
manifest's read-only check-agent-assets preflight, correct the staged output
until it passes, and stop; never run apply or whole-package validation. The
dispatcher repeats the authoritative preflight and exclusively owns the apply
and validation transaction after this execution exits.
What ships with it
2 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.
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.
- 12d ago First seen · 402 lines · 37 tokens per session scan A b121b0dd615f
asset-review-agent is a skill published in the GitHub repository KristenPan/pickoo-your-favorites (17 stars, last pushed 13d ago), licensed MIT. It adds 37 tokens to every session and 4,453 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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