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 prepare-submission-workflowgit 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/prepare-submission-workflow)<a href="https://agentmods.dev/skills/ukgovernmentbeis/inspect_evals/prepare-submission-workflow"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/prepare-submission-workflow/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/prepare-submission-workflow"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/prepare-submission-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 30 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00051 | $0.00838 |
| Opus 5 | $0.00026 | $0.00419 |
| Sonnet 5 | $0.00010 | $0.00168 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
prepare-submission-workflow 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prepare Eval For Submission
Since May 2026, new evaluations are submitted as entries to the register — the evaluation code lives in your own upstream repository, and you add a pointer to it here. Code is no longer added directly to src/inspect_evals/. If the user appears to be submitting evaluation code into the repo, direct them to register/README.md for the full process.
Workflow Steps
To prepare an evaluation for submission as a pull request:
1. Verify upstream repo requirements
The upstream repo must:
- Have a
pyproject.tomlwith a[project]table so it can be installed viauv sync - Declare
inspect_aias a dependency - Define each task with the
@taskdecorator frominspect_ai
Ask the user whether their upstream repo meets these requirements. Offer to check for them — if they provide the GitHub repository URL, fetch the repo's pyproject.toml and task files (e.g. via WebFetch on the raw GitHub URLs) to verify the requirements are met. If any requirement is not met, tell the user what needs to be fixed upstream before they can register.
Important: The submitter must be the owner or a maintainer of the upstream repository. The submission workflow enforces this — if they are not, they will need to fork the repo so they can be listed as a maintainer.
2. Gather information and create register/<eval_name>/eval.yaml
Skip this step if register/<eval_name>/eval.yaml already exists.
Use register/example_eval.yaml as the template — it documents every field. Don't ask the user field-by-field; instead, derive what you can from the upstream repo first, then ask one batched question for what's missing.
Hints on what to derive from the upstream repo (don't ask):
source.repository_url— from step 1.source.repository_commit— fetch the latest commit SHA on the default branch (must be a 40-char SHA, not a tag or branch).tasks[].nameandtasks[].task_path— locate every@task-decorated function in the repo and record the function name and file path.title— from the upstream README heading orpyproject.toml[project].name.description— draft from the upstream README; keep to one short paragraph since the generated README links back upstream.source.maintainers— defaults to the repo owner; only override if the repo is org-owned and the real maintainers are individuals.
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 · 64 lines · 51 tokens per session scan A 6bbc24c75ffc
prepare-submission-workflow is a skill published in the GitHub repository UKGovernmentBEIS/inspect_evals (665 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 838 once invoked, about $0.0003 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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