# Dataset Benchmark Discovery > Use when the user asks for latest datasets, benchmark suites, leaderboards, baseline or SOTA comparisons, dataset selection for experiments, or evidence that a paper needs complete experimental validation. This skill routes to the dataset_benchmark_discovery tool and keeps access/license limits explicit. - Skill: `tomevault-io/dataset-benchmark-discovery` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/dataset-benchmark-discovery` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/dataset-benchmark-discovery/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/dataset-benchmark-discovery --- # Dataset Benchmark Discovery Use this skill before experiment planning or manuscript drafting when the user needs datasets, benchmarks, metrics, baselines, or leaderboard context. ## Core Rules - Prefer the `dataset_benchmark_discovery` tool over generic browsing for dataset and benchmark mapping. - Do not download restricted, login-gated, license-gated, or private data unless the user has provided access and the environment is configured for it. - Treat `dataset_benchmark_map.json` as a candidate map. It can support dataset selection and experiment planning, but it is not executed experimental evidence. - Verify version/date, license, standard split, metric definition, and official leaderboard status before writing strong manuscript claims. - Route actual evaluation to `experiment_lab`, then use `claim_support_matrix.json` for manuscript claim support. ## Workflow 1. Call `dataset_benchmark_discovery(topic=..., scope=...)`. 2. Review candidates by task fit, recency/version, license/access, metric alignment, and reproducibility risk. 3. If the user wants experiments, choose feasible datasets and pass local paths or uploaded files into `experiment_lab`. 4. If writing a paper before experiments are complete, describe benchmark choices as planned validation, not completed results. ## Output Discipline When summarizing results, report: - strongest candidate datasets/benchmarks - access and license risks - standard metrics and splits when detected - baseline/SOTA hints when available - what still needs manual verification --- > Source: [Citrus-bit/Anaxa](https://github.com/Citrus-bit/Anaxa) — distributed by [TomeVault](https://tomevault.io).