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 UKGovernmentBEIS/inspect_evals --skill generate-asset-actionsgit clone --depth 1 https://github.com/UKGovernmentBEIS/inspect_evalsWrote 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/ukgovernmentbeis/inspect_evals/generate-asset-actions)<a href="https://agentmods.dev/skills/ukgovernmentbeis/inspect_evals/generate-asset-actions"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/generate-asset-actions/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/ukgovernmentbeis/inspect_evals/generate-asset-actions"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/generate-asset-actions.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 | $0.00858 |
| Opus 5 | $0.00018 | $0.00429 |
| Sonnet 5 | $0.00007 | $0.00172 |
| Haiku 4.5 | $0.00004 | $0.00086 |
Grade A, and why
generate-asset-actions 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 11d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Asset Policy
Regenerate internal/audits/asset-actions.yaml and internal/audits/audit-summary.md from ASSETS.yaml.
If ASSETS.yaml may be stale, run uv run python tools/generate_asset_manifest.py first.
Run uv run python tools/summarise_asset_manifest.py to get aggregate counts (by type, by state, totals). Use these numbers when populating audit-summary.md.
Classification
Read ASSETS.yaml. For each asset, determine target stage first, then priority. Process both state: floating assets AND state: pinned assets that match known-unstable sources (since their target is controlled, they are not yet at their target stage).
Target stages (per ADR-0007)
The target stage depends on host reliability, not asset type:
controlled(Stage 2) — any asset where upstream has broken before, maintainer is unresponsive/deprecated, OR host is unreliable (personal repos, Google Drive,.edudomains, university servers, any host without version control). This applies togit_clone,direct_url, andhuggingfacealike.pinned(Stage 1) — assets on reliable, version-controlled hosts (GitHub, HuggingFace, well-known CDNs) with no history of breakage.
Per ADR-0007: "Anything hosted on a less reliable domain (personal websites, Google Drive, university servers, or any host without version control) should skip straight to Stage 2."
Priority tiers
- Urgent — all other floating refs on reliable hosts. Target is
pinned. - High — matches a known-unstable source (see registry below). Target is
controlled. - Medium — unreliable host (
drive.google.com,.edudomains, personal repos/websites) not already in the known-unstable registry. Target iscontrolled.
For assets with state: pinned and a {SHA} placeholder but no checksum, classify as Low (target: pinned with checksum).
Omit assets already at their target stage.
Every entry needs: eval, source, type, state, target, action, reason.
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.
- 11d ago First seen · 62 lines · 37 tokens per session scan A 5398a27649f0
generate-asset-actions is a skill published in the GitHub repository UKGovernmentBEIS/inspect_evals (665 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 858 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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