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 eval-quality-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/eval-quality-workflow)<a href="https://agentmods.dev/skills/ukgovernmentbeis/inspect_evals/eval-quality-workflow"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/eval-quality-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/eval-quality-workflow"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/eval-quality-workflow.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 | $0.00106 | $0.01583 |
| Opus 5 | $0.00053 | $0.00792 |
| Sonnet 5 | $0.00021 | $0.00317 |
| Haiku 4.5 | $0.00011 | $0.00158 |
Grade A, and why
eval-quality-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 9d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluation Quality — Fix or Review
This skill covers two closely related workflows for a single evaluation in src/inspect_evals/:
- Fix An Evaluation: Refactor the evaluation to comply with EVALUATION_CHECKLIST.md
- Review An Evaluation: Assess compliance without making changes
Identifying the Evaluation
If the user has given you a name, that takes priority. If you were just building an evaluation, or the user has uncommitted code for one specific evaluation, you can assume that's the correct one. If you are not confident which evaluation to look at, ask the user.
Fix An Evaluation
Our standards are in EVALUATION_CHECKLIST.md, with links to BEST_PRACTICES.md and CONTRIBUTING.md. Your job is to refactor the evaluation to meet these standards.
- Set up the working directory:
- If the user provides specific instructions about any step, assume the user's instructions override these instructions.
- If there is no evaluation name, ask the user for one.
- The evaluation name should be the eval folder name plus its version (from the @task function's version argument). For instance, GPQA version 1.1.2 becomes "gpqa_1_1_2". If this exact folder name already exists, add a number to it via "gpqa_1_1_2_analysis2". This name will be referred to as
<eval_name>. - Create a folder called
agent_artefacts/<eval_name>/fixif it isn't present. - Whenever you create a .md file as part of this workflow, assume it is made in
agent_artefacts/<eval_name>/fix. - Copy EVALUATION_CHECKLIST.md to the folder.
- Create a NOTES.md file for miscellaneous helpful notes. Err on the side of taking lots of notes. Create an UNCERTAINTIES.md file to note any uncertainties.
- Go over each item in the EVALUATION_CHECKLIST, using the linked documents for context where necessary, going from top to bottom. It is important to go over every single item in the checklist!
- For each checklist item, assess your confidence that you know what is being asked of you. Select Low, Medium, or High.
- If you have High confidence, fix the evaluation to pass the checklist if needed, then edit EVALUATION_CHECKLIST.md to place a check next to it. It is acceptable to check off an item without making any changes if it already passes the requirement.
- If you have Medium confidence, make a note of this in UNCERTAINTIES.md along with any questions you have, then do your best to solve it as per the High confidence workflow.
- If you have Low confidence, make a note of this in UNCERTAINTIES.md along with any questions you have, then do not attempt to solve it and leave the checkmark blank.
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.
- 9d ago First seen · 60 lines · 106 tokens per session scan A f2bb2ff1c931
eval-quality-workflow is a skill published in the GitHub repository UKGovernmentBEIS/inspect_evals (662 stars, last pushed today), licensed MIT. It adds 106 tokens to every session and 1,583 once invoked, about $0.0005 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.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.