qte77/designing-backend
plugins/backend-design/skills/designing-backend/SKILL.md
Designs concise, streamlined backend systems matching exact task requirements. Use when planning APIs, data models, system architecture, or when the user requests backend design work.
$ install --global
skillsauthnpx skillsauth add qte77/claude-code-plugins designing-backendInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 17, 2026, 11:28 AM5.8s1 file scanned
SKILL.md
- name:
- designing-backend
- description:
- Designs concise, streamlined backend systems matching exact task requirements. Use when planning APIs, data models, system architecture, or when the user requests backend design work.
- compatibility:
- Designed for Claude Code
- allowed-tools:
- Read, Grep, Glob, WebSearch, WebFetch
- argument-hint:
- [component-name]
- context:
- fork
- agent:
- Explore
- stability:
- stable
- content-hash:
- sha256:a0726df430d19c4dc24580413b05a81905470ed5af7006efaef7f96e226b1c59
- last-verified-cc-version:
- 1.0.34
Git Context
- Recent changes: !
git log --oneline -3 - Current branch: !
git branch --show-current
Backend Architecture
Target: $ARGUMENTS
Creates focused, streamlined backend system designs matching stated requirements exactly. No over-engineering.
Workflow
- Read backend requirements from specified documents
- Validate scope - Simple data processing vs Complex system architecture
- Design minimal solution matching stated complexity
- Create focused deliverables - single doc for simple, multiple for complex
- Use make recipes for all commands
Architecture Strategy
Simple Processing: Basic functions, lightweight integration, existing patterns Complex Systems: Multi-tiered pipelines, PydanticAI orchestration, async patterns Performance targets: <1s simple operations, scalable for complex systems
Output Standards
Simple Tasks: Single focused backend specification Complex Tasks: Multiple targeted architecture files All outputs: Concise, streamlined, no unnecessary complexity
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