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 HoangNguyen0403/agent-skills-standard --skill evals-rungit clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standardWrote 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/hoangnguyen0403/agent-skills-standard/evals-run)<a href="https://agentmods.dev/skills/hoangnguyen0403/agent-skills-standard/evals-run"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/evals-run/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/hoangnguyen0403/agent-skills-standard/evals-run"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/evals-run.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 | $0.00011 | $0.00877 |
| Opus 5.5 | $0.00004 | $0.00351 |
| Sonnet 5.5 | $0.00002 | $0.00175 |
| Haiku 4.5 | $0.00001 | $0.00088 |
Grade A, and why
evals-run 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 20d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evals Run Skill
[!IMPORTANT] Workflow skill for evals run.
Optional args: slug=, ticket=<id/url>, mode=interactive|autonomous|channel, channel=, auto_continue=true|false, profile=business|hybrid|technical.
Instructions
When the user asks to perform this workflow, execute the following steps:
description: Run blinded live skill evals and publish reproducible v2 results.
Goal
Measure whether a skill changes agent behavior with isolated, immutable, outcome-based eval evidence. Distinguish textual transcript assertions from executable task verifications.
Steps
1. Choose or resume a run
-
For ordinary maintenance after a complete catalog baseline exists, run
pnpm evals:baselinefirst. It creates or resumes a selective manifest, reuses only compatible evidence, and prints the model, reasoning level, concurrency, and fresh-answer count without starting workers. -
Review that plan before spending quota. Start workers only with
pnpm evals:baseline -- --execute; the default isgpt-5.6-lunawithhighreasoning and one worker. Override intentionally withEVALS_MODEL,EVALS_REASONING_EFFORT, orEVALS_CONCURRENCY(maximum four workers). -
If usage is exhausted, keep the run directory and rerun the identical
--executecommand after access resumes; completed answers are reused automatically. -
Use
pnpm evals:manifest -- --category <category>for one category orpnpm evals:manifest -- --allfor the complete catalog. -
Use
pnpm evals:manifest -- --resume <runId>only when deliberately continuing an existing run; a new invocation always creates a collision-safe run ID. -
Record the printed run ID. The manifest records source hashes, the v2 schema, and the generation protocol.
2. Answer each blinded case
- Run each baseline and with-skill arm in a separate worker/context.
- Baseline receives only the prompt. With-skill receives the same prompt plus that skill's
SKILL.md. - Neither arm receives coaching pressure or answer-anchor hints. Textual assertions measure transcript evidence; distinguish clearly from executable task outcomes.
- Trigger cases receive only the skill name and one-line description; never open the full skill body or expose the expected label.
- Trigger prompt filenames use opaque case IDs; never infer the expected label from filenames or ordering.
- For
allruns, write answers underanswers/<category>/<skill>/<case>; category runs useanswers/<skill>/<case>. - Mark known compromised baselines in the manifest and do not use them for delta calculations until clean reruns replace them.
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.
- 20d ago Changed · -1 lines ecc3f1ea1370
- 28d ago Changed · +1 lines fcc1a17b725f
- 1mo ago First seen · 78 lines · 11 tokens per session scan A 5d81dcb6ca79
evals-run is a skill published in the GitHub repository HoangNguyen0403/agent-skills-standard (583 stars, last pushed today), licensed MIT. It adds 11 tokens to every session and 877 once invoked, about $0.0000 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 2026-09-03.
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