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 agentmods add skills/evanluo42/bench-creator/bench-creatornpx skills add EvanLuo42/bench-creator --skill bench-creatorgit clone --depth 1 https://github.com/EvanLuo42/bench-creatorWrote 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/evanluo42/bench-creator/bench-creator)<a href="https://agentmods.dev/skills/evanluo42/bench-creator/bench-creator"><img src="https://agentmods.dev/badge/skills/evanluo42/bench-creator/bench-creator.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00116 | $0.00900 |
| Opus 5 | $0.00058 | $0.00450 |
| Sonnet 5 | $0.00023 | $0.00180 |
| Haiku 4.5 | $0.00012 | $0.00090 |
Grade A, and why
bench-creator 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 6d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capture Bench Cases
Maintain one global ai-work-bench/v1 hub. Treat repositories as sources, not Bench containers.
Resolve the command and hub
Resolve the absolute directory containing this SKILL.md; never assume the current working directory is the Skill directory.
- Windows: run
powershell -NoProfile -ExecutionPolicy Bypass -File <skill-root>/scripts/benchctl.ps1 ... - POSIX: run
sh <skill-root>/scripts/benchctl.sh ...
Use <benchctl> below as shorthand for that absolute launcher command. Set AI_WORK_BENCH_PYTHON only when an explicit Python interpreter override is needed.
Resolve the Bench from AI_WORK_BENCH_HOME, then $CODEX_HOME/benches/daily-work, then ~/.codex/benches/daily-work. Do not create a per-repository .bench. If bench.json is missing, initialize only when the user explicitly requests setup:
<benchctl> init --bench <global-path> --name "Daily Work Bench" --mode suggest --workspace-mode snapshot
Before repository work
Ask the CLI for the policy decision before the first edit or generated artifact:
<benchctl> policy decide --bench <global-path> --phase before
skip: continue without Bench work.checkpoint: read references/checkpointing.md, then runcheckpoint startwith the returned trigger.- For a user-requested capture, add
--explicitto the decision and use triggerexplicit.
Keep the returned checkpoint and task IDs. If checkpointing fails, continue the parent task without capture and never reconstruct input from the solved workspace unless a verified historical parent exists.
After repository work
Collect only signals that actually occurred, then ask for the final decision:
<benchctl> policy decide --bench <global-path> --phase after --signal <signal> ...
discard: runcheckpoint discard.suggest: ask for confirmation; capture with--confirmedonly after approval.capture: read references/case-authoring.md, create the case, and attach the checkpoint.
What ships with it
13 files 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.
- agents/openai.yaml 304 B
- assets/schema/bench-case.schema.json 12 KB
- assets/schema/bench-manifest.schema.json 2.4 KB
- references/bench-format.md 12 KB
- references/case-authoring.md 2.3 KB
- references/checkpointing.md 1.9 KB
- references/privacy-export.md 1.7 KB
- references/runner.md 2.3 KB
- scripts/benchctl.ps1 968 B runs code
- scripts/benchctl.py 121 KB runs code
- scripts/benchctl.sh 497 B runs code
- scripts/example_candidate.py 605 B runs code
- tests/test_benchctl.py 16 KB runs code
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.
- 6d ago First seen · 63 lines · 116 tokens per session scan A 10bbaa0ef0ec
bench-creator is a skill published in the GitHub repository EvanLuo42/bench-creator (5 stars, last pushed 1mo ago), licensed MIT. It adds 116 tokens to every session and 900 once invoked, about $0.0006 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 2026-08-31.
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