Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/UKGovernmentBEIS/inspect_evalsnpx agentmods add skills/ukgovernmentbeis/inspect_evals/eval-report-workflowWrote 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-report-workflow)<a href="https://agentmods.dev/skills/ukgovernmentbeis/inspect_evals/eval-report-workflow"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/eval-report-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-report-workflow"><img src="https://agentmods.dev/badge/skills/ukgovernmentbeis/inspect_evals/eval-report-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 | $0.00057 | $0.02167 |
| Opus 5.5 | $0.00023 | $0.00867 |
| Sonnet 5.5 | $0.00011 | $0.00433 |
| Haiku 4.5 | $0.00006 | $0.00217 |
Grade A, and why
eval-report-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 2mo 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Make an Evaluation Report
This workflow drives tools/evaluation_report.py, which reads a per-eval report_config.yaml and produces a full reproducible report.md (results table, reference comparison, per-category breakdowns, token totals, approximate cost) plus header-only JSON copies of the input logs under results/. The report_config.yaml, regenerated report.md, and results/ folder are committed alongside the eval's eval.yaml.
Report Formatting
The evaluation report included in the README.md is the rendered report.md produced by tools/evaluation_report.py. It should run on the entire dataset or $5 of compute per model, whichever is cheaper. Use the token count from smaller runs to make this prediction.
A typical rendered report looks like this:
# Evaluation Report
## Implementation Details
Brief description of any deviations from the paper, known limitations, etc.
## Results
| Model | Inspect (accuracy) | Reference | Δ | Samples | Stderr | Time |
| ------------- | ------------------ | --------- | ------ | ------- | ------ | ---- |
| openai/... | 0.600 | 0.580 | +0.020 | 100/100 | 0.049 | 18s |
| anthropic/... | 0.400 | 0.420 | -0.020 | 100/100 | 0.049 | 6s |
_Reference: Paper, Table 3_
## Reproducibility Information
- Samples: 100 / 100 per model
- Run dates: 2026-04-29
- Versions: inspect_ai=0.3.x, inspect_evals=0.x
- Models: ...
- Total tokens: 1,234,567
- Approximate cost: $0.42 USD (prices as of 2026-04)
Reproduction commands: ...
Register entries: for register entries (register/<name>/eval.yaml), populate the optional evaluation_report block in eval.yaml instead of editing README.md directly — the README is regenerated from the YAML by make check. The block accepts timestamp, a results list (with model, accuracy, and optionally provider, stderr, time, date), and notes. Extra fields at either level are allowed for eval-specific metric columns. See register/README.md for the schema.
What ships with it
1 file 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.
- 2mo ago First seen · 114 lines · 57 tokens per session scan A c9c8e0db95ae
eval-report-workflow is a skill published in the GitHub repository UKGovernmentBEIS/inspect_evals (721 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 2,167 once invoked, about $0.0002 per session on Opus 5.5. 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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