Data Prep
SkillDev toolsInternal Harness instruction source for data-prep. Route through visible Harness aliases instead of invoking directly.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Data Prep skill
What this skill tells your AI
The instructions your AI receives, as published by linzhe001/harness-research in .agents/skills/data-prep/SKILL.md and read by ahel’s review.
Use this Skill for WF4 dataset analysis, subset design, and data-pipeline preparation.
Read First
- Workflow, language, and documentation rules under
../../../.agents/references/ ./references/dataset-stats.mdPROJECT_STATE.json,CLAUDE.md,AGENTS.mdwhen presentdocs/Refined_Idea.mddocs/20_facts/Execution_Contract.mdwhen presentdocs/30_evidence/Dataset_Table.mdwhen present
Required Work
- Before writing, check existing WF4 docs/artifacts. Archive current Markdown
docs under
docs/90_legacy/<YYYY-MM-DD>/; recordarchive_existing_data_docs_or_NOT_RUN. - Resolve dataset name, path, remote source, and subset strategy from state,
docs/Refined_Idea.md, execution contract, existing Dataset Table, and the user request. - If local data is missing or only a remote source is known, run the Dataset Acquisition Gate before final stats.
- Auto-detect dataset format and task type.
- Produce canonical stats and reproducible subset strategy. For NVS/3DGS, preserve task-valid scene, resolution, point-cloud, smoke, dehaze/clean, and evaluation-reference needs.
- Write
docs/Dataset_Stats.mdand concisedocs/30_evidence/Dataset_Table.mdas Conclusion Evidence. - Write expected config/script artifacts and update
PROJECT_STATE.json,CLAUDE.mddataset paths, and stableAGENTS.mdpointer when appropriate.
Dataset Acquisition Gate
Do not stop at “dataset missing” as the first response. First perform Remote Repository Selection, then ask for only the missing decision: download/mount choice and target directory, target archive/slice, network/disk approval, or existing local path.
If target path and approval are already unambiguous, proceed without another
question. Otherwise ask before large transfers or writing data outside the
repo. Record dataset_acquisition_decision_request_or_NOT_RUN.
Gate Evidence must include source URL, target path, command, result, observed
bytes/checksum when available, extraction path, and stats follow-up. With
multiple Grill/supervisor candidates, try the next executable candidate
after logging failures; skip rejected, deferred, or requires_approval
unless separately approved.
Remote Repository Selection
Inspect source-native listings before download: API, repository tree,
manifest, README, HTTP metadata, or file-list command. For Hugging Face, check
dataset API and relevant tree/main/... listings.
Build a small candidate matrix with remote path/archive, content role
(smoke, dehaze, clean reference, depth, COLMAP, point cloud, metadata),
resolution/layout, size/checksum when known, required/optional/excluded status,
and selection rationale. Do not silently fall back to full data or unrelated
conditions.
Output Rules
- Use
./references/dataset-stats.md. - Separate verified dataset facts from inferred properties.
- Keep
Dataset_Table.mdsource-artifact oriented; do not hand-edit.evidence/**. - Dataset path sync into
CLAUDE.mdis required WF4 output. AGENTS.mdshould point toCLAUDE.md, not duplicate volatile paths.- Report Gate ledger for acquisition, stats, configs, pipeline files, guidance,
and state writes, including
dataset_acquisition_or_NOT_RUN.
Durable Docs Render
temporary drafts.
Signals
- GitHub stars
- 86
- Forks
- 9
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
data-prep- Source
- github.com/linzhe001/harness-research