Neo4j Python Pandas Py2neo V3
Excel-to-Neo4j knowledge graph examples: legacy py2neo v3 plus modern Neo4j GraphRAG/vector search.
Supply asset profile
Coding and developer agents
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3
Maintenance
fresh
5d since push
Risk
Safe to try
Quality score needs review
GitHub quality
579
85/100 quality · 89/100 trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Production candidateStrong OpenAgentSkill Trust Score across adoption, recent maintenance, license clarity, documentation, dependency/runtime risk, install safety, permission surface, and install availability.
Audit
Safe to tryInstall readiness, security metadata, maintenance, and adoption risk.
Trust Score v3
Agent install candidate
Shortlist for production use, then run a normal repository and dependency review.
Stars
579 GitHub stars
Repo activity
579 stars, 187 forks
Maintenance
5d since push
License
MIT
Install
npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3
Install safety
standard package or runtime install path
Permission surface
filesystem or document access
Docs
Strong README/SKILL.md context
Risk summary
Low metadata risk
- Quality score needs review
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- 5d since push
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- RAG and knowledge workflows
- Claude Code teams
- teams that value GitHub adoption signals
- Chunk documents
- Create embeddings
Suited agents
Trust and risk
- Trust score
- 89/100
- Risk level
- Safe to try
- Auto install
- review
Install command
npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3Do not use when
- teams that need a vendor-supported SLA
- high-compliance environments without internal security review
- No OpenAgentSkill engagement data yet
- Quality score needs review
Agent safety v2
74/100 · Review before install
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
- Quality score needs review
Install targets
Install this skill in your agent workflow
Copy the registry command or an agent-specific install prompt for Codex, Claude Code, and Cursor.
OpenAgentSkill CLI
Use the registry command when your workflow supports the OpenAgentSkill installer.
$ npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3Agent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Resolve JSON
/api/agent/resolve?task=Use%20Neo4j%20Python%20Pandas%20Py2neo%20V3%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20Neo4j%20Python%20Pandas%20Py2neo%20V3%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/mazzawill-neo4j-python-pandas-py2neo-v3/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use Neo4j Python Pandas Py2neo V3 in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Neo4j%20Python%20Pandas%20Py2neo%20V3%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/mazzawill-neo4j-python-pandas-py2neo-v3/install
Install command: npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/mazzawill-neo4j-python-pandas-py2neo-v3/install
LLM text format
/api/skills/mazzawill-neo4j-python-pandas-py2neo-v3/install?format=text
Find alternatives
/api/skills/search?q=Neo4j%20Python%20Pandas%20Py2neo%20V3&limit=3
Agent prompt
Use Neo4j Python Pandas Py2neo V3 for this task. Review https://www.openagentskill.com/api/skills/mazzawill-neo4j-python-pandas-py2neo-v3/install, then install with: npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3Registry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/mazzawill-neo4j-python-pandas-py2neo-v3
LLM text
/api/registry/manifest/mazzawill-neo4j-python-pandas-py2neo-v3?format=text
Install alias
/api/registry/install/mazzawill-neo4j-python-pandas-py2neo-v3
Recommend
/api/registry/recommend?task=Use%20Neo4j%20Python%20Pandas%20Py2neo%20V3%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Python, AI Agents, Claude Code, OpenAI Agents
Audit report
Safe to try · 90/100
Review install readiness, maintenance, trust, quality, and metadata warnings before adding this skill to an agent workflow.
Agent decision cockpit
Primary pick for RAG and knowledge
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
RAG and knowledge
Trust label
Production-ready
Install path
Command ready
Use when
- RAG and knowledge workflows
- Claude Code teams
- teams that value GitHub adoption signals
Evidence
- 579 GitHub stars
- recent repository activity
- install command or GitHub repo available
- 85/100 quality profile
Review first
- No OpenAgentSkill engagement data yet
Implementation path
- 1Install it in a sandbox agent and run one RAG and knowledge task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Production candidate
Strong OpenAgentSkill Trust Score across adoption, recent maintenance, license clarity, documentation, dependency/runtime risk, install safety, permission surface, and install availability.
GitHub adoption
INFO579 GitHub stars
Stars/forks activity
INFO579 stars, 187 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Meaningful GitHub adoption signal
- Install command has no obvious high-risk pattern
Review before install
- Quality score needs review
Recommended action
Shortlist for production use, then run a normal repository and dependency review.
Quality profile
Excellent candidate for agent workflows
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Use this skill in these scenarios
Search private knowledge
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Stack fit
Add it to a complete workflow
Ingest, retrieve, and cite
RAG knowledge base
A stack for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Inspect, patch, and verify code
Coding review agent
A stack for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Turn skills into distribution
Content growth agent
A stack for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Alternative shortlist
Compare before you install
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Overview
Excel-to-Neo4j knowledge graph examples: legacy py2neo v3 plus modern Neo4j GraphRAG/vector search.
Imported by the skill-only GitHub discovery pipeline because it matches agent skill, automation, domain workflow, RAG, document-processing, data, finance, security, or developer-tool signals. Protocol-server projects are excluded from automated imports.
Platform Compatibility
Technical Details
- Version
- 1.0.0
- License
- MIT
- Last Updated
- 6/16/2026
- Published
- 6/15/2026
Frameworks & Tools
Decision snapshot
Primary pick
579 GitHub stars
Audit snapshot
Install review
Install and adoption review
- Security
- 90/100
- Maintenance
- 100/100
- Install
- 92/100
Install
Add to agent workflow
Free and open source. Review the audit before production use.
Growth loop
Share kit
Scenario-led draft for Neo4j Python Pandas Py2neo V3, ready for a manual X post.
OpenAgentSkill Update Today: Neo4j Python Pandas Py2neo V3 Use it when your agent needs to turn docs, data, or knowledge bases int... 579 stars - agent-frameworks Link: https://www.openagentskill.com/skills/mazzawill-neo4j-python-pandas-py2neo-v3?ref=x #AIAgents #OpenAgentSkill
Optional reply with install command
Link for Neo4j Python Pandas Py2neo V3: https://www.openagentskill.com/skills/mazzawill-neo4j-python-pandas-py2neo-v3?ref=x Install: npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3
Listing source
Community indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- MazzaWill
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This community indexed listing is attributed to MazzaWill but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
README badge
Add this badge to your GitHub README to show the listing, trust score, and install handoff.
[](https://www.openagentskill.com/skills/mazzawill-neo4j-python-pandas-py2neo-v3)Author
MazzaWill
@mazzawill
Platform Fit
Health Signals
- GitHub stars
- 579
- Quality score
- 53/100
- Last GitHub push
- Jun 12, 2026
- Framework hints
- 2
- OpenAgentSkill views
- 0
- Install copies
- 0
- Outbound clicks
- 0
Community Signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & Safety
Production candidate
- GitHub adoption579 GitHub starsINFO
- Stars/forks activity579 stars, 187 forks; issue activity unavailable in current metadataINFO
- Recent maintenance5d since pushPASS
- License clarityMITPASS
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime riskno major dependency risk hints in public metadataPASS
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