Claude Codepasses structure
Checker 0.1.0 · agent-skills-2026-08-13+claude-code-docs-2026-08-13 · 8/29/2026.claude/skills/competitive-data-scienceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Real Skill packageSource verifiedClawHub registry
Competitive Data Science
Use when working on data science competitions (Kaggle, CCF BDCI, DCIC, etc.) to build from baseline to topline solutions, covering data processing, feature engineering, model building, training techniques, and model fusion strategies
Identity and source
Publisher attributionzhouzy-creatorregistry owner unverified by skillvetai
Functional categoryData, Spreadsheets & Analyticsautomatically inferred · 54% rule confidence
Package forminstruction with code4 recorded files
Canonical sourceClawHub registryclawhub:zhouzy-creator:datacompetition-blueprint
Platform declarations
These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.
OpenClawnative officialProvenance: registry distribution
Independent structural checks
These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
OpenAI Codexpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+codex-docs-2026-08-13 · 8/29/2026.agents/skills/competitive-data-scienceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
OpenClawpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+openclaw-docs-2026-08-13 · 8/29/2026skills/competitive-data-scienceRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Installation and inspection
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @zhouzy-creator/datacompetition-blueprintclawhub inspect @zhouzy-creator/datacompetition-blueprint --version 0.1.0Security evidence
SkillVetAI static result: no findings detected
This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
- Status
- completed
- Coverage
- full text content
- Files
- 4 / 4 inspected as text
- Checked
- 8/29/2026, 4:46:57 PM
- Scanner
- 0.1.3
- Policy
- 1.0.3
2 inferred permission indicators
- network access — automatically inferred
- filesystem write — automatically inferred
2 dependency and API indicators
- api: clawhub.ai
- api: github.com
External clawhub result: clean
This is registry-supplied evidence for the recorded release, not an independent SkillVetAI scan. Check the canonical source for the full report, scanner versions, scope, and current moderation state.
- Evidence checked
- 8/29/2026, 8:52:37 AM
- Release binding
- Matches this record
- vt: clean
- skillspector: clean
- llm: clean
Recorded files
The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.
Package content hash
sha256:4be50df3b218b970a20a543be6c3e96c0c2116fca284d1a193759b3c8134f4a9Show up to 4 recorded paths
🏆 Competitive Data Science.mdexample.pyskill-card.mdSKILL.md
Source changelog
Initial release: introduces a comprehensive blueprint for data science competitions from baseline to advanced solutions. - Covers end-to-end workflow: data processing, feature engineering, model building, training, and model fusion. - Includes practical techniques for data cleaning, feature creation, augmentation, and model optimization. - Provides code snippets for popular models (LightGBM, XGBoost, Transformer-based deep learning). - Details advanced training strategies such as cross-validation schemes, learning rate scheduling, and adversarial training. - Presents model fusion approaches, including weighted rank averaging and stacking. - Suitable as a reference for participants in competitions like Kaggle, CCF BDCI, and DCIC.