dependency-analysis
dependency-analysis is an code AI skill with a core value of Analyze internal and package dependencies using Ontoly graph traversal. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Analyze internal and package dependencies using Ontoly graph traversal. Use when asked which modules, packages, services, or files depend on each other.
Quick Facts
mkdir -p ./skills/dependency-analysis && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/dependency-analysis/SKILL.md -o ./skills/dependency-analysis/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
When to Use
- Use when this upstream workflow matches the user's stated goal.
- Use when the task requires the procedures documented in this skill.
# Dependency Analysis
Use this skill when the user asks for dependency analysis using Ontoly evidence.
Required Workflow
Follow [the shared Ontoly workflow. Also read [graph evidence rules, [MCP usage, [best practices, and [fallback rules when the task requires detail.
Ontoly Capabilities
Use these capabilities first: `FindDependencies`, `FindDependents`, `FindCycles`, `GraphStatistics`, `EvidencePack`.
Output Contract
Return:
- answer or plan
- capabilities invoked
- graph evidence with node ids, edge types, source spans, and graph hash when available
- confidence: high, medium, or low
- fallback reason if repository files were inspected
Boundaries
Do not implement compiler, query, MCP, SDK, or business logic in the skill. Do not search repository files until Ontoly cannot answer or evidence must be confirmed.
Resources
- [Examples
- [Prompt template
- [Capability notes
Learn more
- Documentation: https://ontoly.xyz/docs
- This skill on the web: https://ontoly.xyz/skills#dependency-analysis
- All Ontoly Agent Skills: https://ontoly.xyz/skills
- Install via skills.sh: https://www.skills.sh/?q=0xsarwagya/ontoly
Examples
User: Apply this skill to my current task.
Assistant: Follow the workflow in this skill, cite limitations, and ask before risky steps.Limitations
- Imported upstream skill; verify credentials, permissions, and safety boundaries before execution.
- Does not replace environment-specific validation, testing, or maintainer review.
🎯 Best For
- Data analysts
- Business intelligence teams
- Claude users
- Software engineers
- Development teams
💡 Use Cases
- Finding patterns in customer data
- Creating automated dashboards
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply dependency-analysis to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Can this connect to my database directly?
Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.
Is dependency-analysis compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for dependency-analysis?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install dependency-analysis?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/dependency-analysis/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not validating data quality
AI analysis is only as good as your input data. Profile and clean data before analysis.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.