leaderboard: Skill for Claude Code

.claude/skills/benchmark-discovery/SKILL.md

benchmark-discovery is a skill for Claude Code from steel-dev/leaderboard. It costs 235 tokens per session (4,456 once invoked), scanned A, original, MIT.

A workflow for finding recent arXiv papers that may contain results for the Steel benchmark leaderboard. arXiv is a public website where researchers share papers, and a benchmark is a standard test used to compare systems.

In plain words
What is it for?
It helps run the paper-discovery process, search selected benchmarks or date ranges, remove already listed results, and create a Markdown summary for human review.
Why use it?
It filters irrelevant or duplicate papers and produces review material, so maintainers do not have to search and screen every result manually.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is steel-dev/leaderboard's own configuration. It tells Claude Code how to work on leaderboard itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything leaderboard configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/steel-dev/leaderboard/main/.claude/skills/benchmark-discovery/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/steel-dev/leaderboard

Made for: Claude Code.

Wrote 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.

agentmods badge for benchmark-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/steel-dev/leaderboard/benchmark-discovery/github.svg)](https://agentmods.dev/skills/steel-dev/leaderboard/benchmark-discovery)
Your own site
<a href="https://agentmods.dev/skills/steel-dev/leaderboard/benchmark-discovery"><img src="https://agentmods.dev/badge/skills/steel-dev/leaderboard/benchmark-discovery/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.

agentmods 80×15 button for benchmark-discovery

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 235 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,456 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00235 $0.04456
Opus 5.5 $0.00094 $0.01782
Sonnet 5.5 $0.00047 $0.00891
Haiku 4.5 $0.00023 $0.00446

Measured 24d ago against content hash 13239f085677, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 24d 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.

.claude/skills/benchmark-discovery/SKILL.md · 200 lines

How it starts

The opening of the file, as written. The whole thing — 200 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.

Automated weekly sweep (Atlas research → deductive judge → deterministic PR)

A scheduled GitHub Actions pipeline runs this discovery loop weekly so a maintainer doesn't have to. It is Propose-Dispose: the LLM stages only propose — they have no tools and never touch git — and a deterministic apply step validates, re-ranks, and opens the PR a human merges. The workflows live in .github/workflows/:

  • discovery-scheduler.yml — cron (Mondays ~07:27 UTC) that dispatches the sweep via gh workflow run with since_days=9 (weekly + 2-day margin). Kill-switch: set repo variable ENABLE_DISCOVERY=false to pause without editing YAML.
  • discover-results.yml — the sweep itself, four jobs:
    1. arxiv-baseline — free, deterministic pnpm run discover → .discovery/run.json (§1 below).
    2. discover — read-only. pnpm run discover:research (the Atlas driver at src/scripts/discover-research.ts: Exa search + Steel browser fetch, z.ai glm-5.2) produces cited, structured candidates in .discovery/atlas-candidates.json (plus atlas-report.md, atlas-trace.jsonl, atlas-errors.json). Then pnpm run discover:judge — a deductive generateObject pass at src/scripts/discover-judge.ts, with no tools and no fetches — reads the candidates + the FULL current leaderboard rows + CONTRIBUTING.md and emits one declarative proposal per benchmark to .discovery/proposals/<dataKey>-<week>.json (adds + dismissals, each with a reason and a category).
    3. apply — the ONLY job with contents:write + pull-requests:write. pnpm run discover:apply --write (deterministic TypeScript at src/scripts/discover-apply.ts, no LLM) hard-gates each add (src/data/schema.json, finite score, dedup, freshness), does a soft SSRF-guarded evidence re-fetch, stably re-ranks (existing rows keep their order; new rows slot in by score), writes src/data/<dataKey>.json + README.md, runs update-readme/lint/build, and opens one PR per leaderboard with per-row reasoning for every add AND dismissal.
    4. notify — on success posts PR links (or "no fresh results") to the rolling issue; on any failure — including a silent gate/build failure where survivors were proposed but no PR opened — it opens a discovery-alert issue. The alert label is deliberately separate from the rolling tracker's discovery label so the tracker's find-or-create never misroutes a comment into an alert.

Read the full file on GitHub · 200 lines

Changes

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.

  1. 24d ago Changed · +9 lines 13239f085677
  2. 1mo ago Changed · +21 lines · -11 tokens per session dc2bcae529af
  3. 2mo ago First seen · 170 lines · 246 tokens per session scan A 45d4f7155ac7

Subscribe to this mod's changes

benchmark-discovery is a skill published in the GitHub repository steel-dev/leaderboard (49 stars, last pushed 22d ago), licensed MIT. It adds 235 tokens to every session and 4,456 once invoked, about $0.0009 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.

Related

benchmark-discovery compared with skills from other repositories

benchmark-discovery next to 6 skills from other repositories
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benchmark-discoverysteel-dev/leaderboardSkill4922d agoMITA235Claude Code
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detecting-pv-signalsmaziyarpanahi/openmedSkill5.8kyesterdayApache-2.0A218Claude Code