Borrowing it
Nothing to install: this file belongs to steel-dev/leaderboard. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/steel-dev/leaderboard/main/.claude/skills/benchmark-discovery/SKILL.mdgit clone --depth 1 https://github.com/steel-dev/leaderboardWrote 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/steel-dev/leaderboard/benchmark-discovery)<a href="https://agentmods.dev/skills/steel-dev/leaderboard/benchmark-discovery"><img src="https://agentmods.dev/badge/skills/steel-dev/leaderboard/benchmark-discovery.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 | $0.00246 | $0.03155 |
| Opus 5 | $0.00123 | $0.01577 |
| Sonnet 5 | $0.00049 | $0.00631 |
| Haiku 4.5 | $0.00025 | $0.00315 |
Grade A, and why
benchmark-discovery 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 7d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark discovery for the Steel leaderboard
Surface candidate benchmark results for maintainer review. The skill drives the deterministic arxiv discovery script in this repo, applies project-specific filtering rules, and renders a standardised markdown summary that a maintainer can scan and selectively add to src/data/<slug>.json.
This skill never modifies leaderboard data on its own. The output is review material; merging is always a human step. The discovery issue exists so candidates accumulate in one inbox rather than getting lost across notebooks and tabs.
Process
1. Run the discovery script
The script lives at src/scripts/discover-results.ts and is exposed as npm run discover. It hits arxiv's public API (no auth, ~1 req per 3 seconds per benchmark), filters candidates by date, dedupes by arxiv URL against existing rows, and writes a consolidated .discovery/run.json.
Default invocation:
npm run discover -- --since-days 10
Adjust to user intent:
- Specific benchmarks:
--slug browsecomp,gaia,webvoyager - Catching up after weeks off:
--since-days 60 - Available slugs match those declared in
src/lib/benchmark-hub.ts(e.g.webvoyager,browsecomp,webarena,swe-bench-verified,osworld,gaia,clawbench,online-mind2web,tau-bench,agentbench).
2. Read .discovery/run.json
Each benchmark entry contains:
slug,benchmarkNameexistingTopSystems— the top 8 rows currently tracked, used for the "Current top" line in the outputcandidates[]— papers witharxivId,url,title,authors,publishedAt,summary(full abstract)
The script has already dropped anything whose arxiv ID matches an existing sourceUrl. Anything in candidates is genuinely unmatched at the URL level — but may still be a duplicate by system name (many existing rows source to blog posts/HF, not arxiv), or off-topic entirely. Classification is the skill's job.
3. Classify each candidate by reading its abstract
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.
- 7d ago First seen · 170 lines · 246 tokens per session scan A 45d4f7155ac7
benchmark-discovery is a skill published in the GitHub repository steel-dev/leaderboard (48 stars, last pushed yesterday), licensed MIT. It adds 246 tokens to every session and 3,155 once invoked, about $0.0012 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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