Install payload
Install payload is broadly covered in current results.
100% (4/4)
Browse the directory
/
Showing 4 resources for "data-science"
Saved
/ search · [ ] view density · esc clear input
Active
Select entries to compare install and trust signals side by side.
Trust snapshot
4 results in this view
Claimed
0%(0/4)
2 trust signals differ in this sample: Source provenance, Submitter
Signals differ on Source provenance, Submitter — add entries to compare before you install.
Rollout signal scan
2 rollout risk signals in current results
Biggest gaps: metadata review, package integrity. 0 entries have 2+ required gaps.
good
Adoption queue
Browse adoption queue · balanced
2/4 visible results are in hold tier and need mitigation before adoption.
ready 0caution 2hold 2
Jupyter MCP Server
1 blockers: Metadata review
caution
50/100
Request metadata review from maintainers or internal owners.
Collect package checksum or signed artifact information.
mcp/jupyter-mcp-server · trust review · confidence 67%
Neo4j MCP Server
1 blockers: Metadata review
caution
50/100
Request metadata review from maintainers or internal owners.
Collect package checksum or signed artifact information.
mcp/neo4j-mcp-server · trust review · confidence 67%
Pandas Data Wrangling - CLAUDE.md Rules for Claude Code
1 blockers: Metadata review
hold
38/100
Request metadata review from maintainers or internal owners.
Document privacy posture and data handling expectations.
Collect package checksum or signed artifact information.
rules/python-data-science · trust review · confidence 50%
Python Data Science Expert - CLAUDE.md Rules for Claude Code
2 blockers: Metadata review, Safety notes
hold
24/100
Request metadata review from maintainers or internal owners.
Capture safety notes with misuse/guardrail guidance.
Document privacy posture and data handling expectations.
rules/python-data-science-expert · trust review · confidence 33%
Decision confidence
Decision confidence scan · balanced
2/4 results are low-confidence and need review before adoption.
high 0medium 2low 2
Jupyter MCP Server
Address Metadata review, Package integrity before broader rollout.
medium
54/100
Missing: Metadata reviewMissing: Package integrity
mcp/jupyter-mcp-server · trust review
Neo4j MCP Server
Address Metadata review, Package integrity before broader rollout.
medium
54/100
Missing: Metadata reviewMissing: Package integrity
mcp/neo4j-mcp-server · trust review
Pandas Data Wrangling - CLAUDE.md Rules for Claude Code
Hold adoption until Metadata review, Privacy notes are resolved.
low
42/100
Missing: Metadata reviewMissing: Privacy notesMissing: Package integrity
rules/python-data-science · trust review
Python Data Science Expert - CLAUDE.md Rules for Claude Code
Hold adoption until Metadata review, Safety notes are resolved.
low
28/100
Missing: Metadata reviewMissing: Safety notesMissing: Privacy notes
rules/python-data-science-expert · trust review
Freshness distribution
50% of this view is aging or stale
Median age 184 days; 2 fresh of 4 scanned. Re-verify the oldest entries.
Theme distribution
Results center on python
75% of this view shares the top theme. Leading themes: python, data-science, code-execution.
- python75% (3)
- data-science50% (2)
- code-execution25% (1)
- cross-validation25% (1)
- cypher25% (1)
- data-leakage25% (1)
- data-wrangling25% (1)
- database25% (1)
18 distinct themes across 4 scanned
Jupyter MCP Serverby Datalayer · submitted by oktofeesh1
MCP server for connecting Claude to Jupyter notebooks, kernels, files, cells, multimodal outputs, and JupyterLab workflows over stdio or Streamable HTTP.
Turn Claude into a practical pandas data-wrangling partner for loading, cleaning, reshaping, and aggregating tabular data with the pandas DataFrame API
Safety ✓ Privacy ·
Neo4j MCP Serverby Neo4j · submitted by oktofeesh1
Official Neo4j MCP server for giving Claude structured access to Neo4j graph schema introspection, read-only Cypher, optional write Cypher, and Graph Data Science procedure discovery.
scikit-learn ML modeling rule that audits for data leakage, enforces Pipeline-based preprocessing, and validates cross-validation rigor (StratifiedKFold, GroupKFold, TimeSeriesSplit) for defensible model evaluation
Safety · Privacy ·