complexity▌
boshu2/agentops · updated Apr 8, 2026
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YOU MUST EXECUTE THIS WORKFLOW. Do not just describe it.
Complexity Skill
YOU MUST EXECUTE THIS WORKFLOW. Do not just describe it.
Analyze code complexity to identify refactoring targets.
Execution Steps
Given /complexity [path]:
Step 1: Determine Target
If path provided: Use it directly.
If no path: Use current directory or recent changes:
git diff --name-only HEAD~5 2>/dev/null | grep -E '\.(py|go)$' | head -10
Step 2: Detect Language
# Check for Python files
ls *.py **/*.py 2>/dev/null | head -1 && echo "Python detected"
# Check for Go files
ls *.go **/*.go 2>/dev/null | head -1 && echo "Go detected"
Step 3: Run Complexity Analysis
For Python (using radon):
# Check if radon is installed
which radon || pip install radon
# Run cyclomatic complexity
radon cc <path> -a -s
# Run maintainability index
radon mi <path> -s
For Go (using gocyclo):
# Check if gocyclo is installed
which gocyclo || go install github.com/fzipp/gocyclo/cmd/gocyclo@latest
# Run complexity analysis
gocyclo -over 10 <path>
Step 4: Interpret Results
Cyclomatic Complexity Grades:
| Grade | CC Score | Meaning |
|---|---|---|
| A | 1-5 | Low risk, simple |
| B | 6-10 | Moderate, manageable |
| C | 11-20 | High risk, complex |
| D | 21-30 | Very high risk |
| F | 31+ | Untestable, refactor now |
Step 5: Identify Refactor Targets
List functions/methods that need attention:
- CC > 10: Should refactor
- CC > 20: Must refactor
- CC > 30: Critical, immediate action
Step 6: Write Complexity Report
Write to: .agents/complexity/YYYY-MM-DD-<target>.md
# Complexity Report: <Target>
**Date:** YYYY-MM-DD
**Language:** <Python/Go>
**Files Analyzed:** <count>
## Summary
- Average CC: <score>
- Highest CC: <score> in <function>
- Functions over threshold: <count>
## Refactor Targets
### Critical (CC > 20)
| Function | File | CC | Recommendation |
|----------|------|-----|----------------|
| <name> | <file:line> | <score> | <how to simplify> |
### High (CC 11-20)
| Function | File | CC | Recommendation |
|----------|------|-----|----------------|
| <name> | <file:line> | <score> | <how to simplify> |
## Refactoring Recommendations
1. **<Function>**: <specific suggestion>
- Extract: <what to extract>
- Simplify: <how to simplify>
## Next Steps
- [ ] Address critical complexity first
- [ ] Create issues for high complexity
- [ ] Consider refactoring sprint
Step 7: Report to User
Tell the user:
- Overall complexity summary
- Number of functions over threshold
- Top 3 refactoring targets
- Location of full report
- Run
/refactor <function>to address critical complexity targets
Key Rules
- Use the right tool - radon for Python, gocyclo for Go
- Focus on high CC - prioritize 10+
- Provide specific fixes - not just "refactor this"
- Write the report - always produce artifact
Quick Reference
Simplifying High Complexity:
- Extract helper functions
- Replace conditionals with polymorphism
- Use early returns
- Break up long functions
- Simplify nested loops
Examples
Analyzing Python Project
User says: /complexity src/
What happens:
- Agent detects Python files in
src/directory - Agent checks for radon installation, installs if missing
- Agent runs
radon cc src/ -a -sfor cyclomatic complexity - Agent runs
radon mi src/ -sfor maintainability index - Agent identifies 3 functions with CC > 20, 7 functions with CC 11-20
- Agent writes detailed report to
.agents/complexity/2026-02-13-src.md - Agent recommends extracting nested conditionals in
process_request()function
Result: Complexity report identifies process_request() (CC: 28) as critical refactor target with specific extraction recommendations.
Finding Refactor Targets in Go Module
User says: /complexity
What happens:
- Agent checks recent changes with
git diff --name-only HEAD~5 - Agent detects Go files, verifies gocyclo installation
- Agent runs
gocyclo -over 10 ./...on project - Agent finds
HandleWebhook()function with complexity 34 - Agent writes report with recommendation to extract validation logic
- Agent reports top 3 targets: HandleWebhook (34), ProcessBatch (22), ValidateInput (15)
Result: Critical function identified for immediate refactoring with actionable extraction plan.
See Also
- refactor — Safe, verified refactoring for complexity targets
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| Tool not installed (radon/gocyclo) | Missing dependency | Agent auto-installs: pip install radon for Python or go install github.com/fzipp/gocyclo/cmd/gocyclo@latest for Go. Verify install path in $PATH. |
| No complexity issues found | Threshold too high or genuinely simple code | Lower threshold: try gocyclo -over 5 or check if path includes actual implementation files vs tests. |
| Report shows functions without recommendations | Generic analysis without codebase context | Read the high-CC functions to understand structure, then provide specific refactoring suggestions based on actual code patterns. |
| Mixed language project | Multiple languages in target path | Run analysis separately per language: /complexity src/python/ then /complexity src/go/, combine reports manually. |
How to use complexity on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add complexity
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches complexity from GitHub repository boshu2/agentops and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate complexity. Access the skill through slash commands (e.g., /complexity) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★52 reviews- ★★★★★Liam Singh· Dec 20, 2024
Useful defaults in complexity — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Omar Anderson· Dec 12, 2024
Registry listing for complexity matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Henry Khanna· Dec 8, 2024
I recommend complexity for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Dec 4, 2024
Solid pick for teams standardizing on skills: complexity is focused, and the summary matches what you get after install.
- ★★★★★Sakura Mehta· Dec 4, 2024
complexity has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Nov 23, 2024
We added complexity from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Omar Diallo· Nov 23, 2024
complexity fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Advait Rao· Nov 11, 2024
complexity is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Chaitanya Patil· Oct 14, 2024
complexity fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hiroshi Harris· Oct 14, 2024
We added complexity from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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