seb1n/awesome-ai-agent-skills

• **Archetype**: domain-skill-pack
• **Stars**: 58
• **Last pushed**: 2026-03-02
• **License**: MIT
• **Discovered**: 2026-04-13
• **Source**: backlog evaluation
• **Skills found**: 90+ across multiple domains

Summary

Comprehensive collection of 90+ universal, self-contained skills organized by domain (code-development, data-analytics, security, communication, devops, etc.). Each skill includes systematic workflows with concrete steps, multi-language support, and practical usage guidance. Claims to be "complete, ready-to-use instruction sets" rather than just a link directory.

Assessment

High transferable value despite relatively low star count. Skills contain detailed procedural workflows rather than just expert personas. The refactoring skill demonstrates systematic 6-step process (identify smells → select patterns → plan order → apply → test → document) with concrete patterns and verification steps. Organized domain structure allows for easy extraction of domain-specific processes. Includes SKILL_TEMPLATE.md suggesting standardized skill creation methodology.

Extraction Priority

• Medium-High
• Rationale: Systematic workflows with concrete procedures across multiple domains relevant to babysitter specializations. Quality over quantity - each skill appears to contain extractable procedural knowledge rather than just prompts.

Skills Inventory

Processes

- Source: code-and-development/refactoring/SKILL.md (complete workflow section) - Placement: specializations/shared/systematic-refactoring - Inputs/Outputs: Code with quality issues → Improved code + change documentation - Complexity: moderate - Notes: Covers code smell identification, refactoring pattern selection, safe change ordering, verification

• **Systematic Code Refactoring**: 6-phase refactoring workflow with smell identification, pattern selection, and verification

- Source: security/dependency-scanning/SKILL.md - Placement: specializations/security-compliance/dependency-scanning - Inputs/Outputs: Project dependencies → Vulnerability report + remediation plan - Complexity: simple - Notes: Vulnerability identification, risk assessment, update prioritization

• **Security Dependency Scanning**: Systematic process for identifying and addressing security vulnerabilities in dependencies

- Source: data-and-analytics/data-cleaning/SKILL.md - Placement: specializations/data-science-ml/data-cleaning - Inputs/Outputs: Raw data → Clean, validated dataset + quality report - Complexity: moderate - Notes: Data profiling, anomaly detection, validation rules, cleaning procedures

• **Data Cleaning and Validation**: Structured approach to data quality assessment and improvement

- Source: context-engineering/context-injection/SKILL.md - Placement: specializations/shared/context-engineering - Inputs/Outputs: Base prompt + context requirements → Optimized prompt + injection strategy - Complexity: moderate - Notes: Context analysis, injection point identification, optimization techniques

• **Context Engineering Methodology**: Process for optimizing AI prompt context and information injection

Plugin Ideas

- What install.md would do: Install refactoring workflows, dependency scanning processes, code smell detection, quality gate configs - Processes it would copy: systematic-refactoring, dependency-scanning - Configs/hooks it would create: Pre-commit hooks for code quality, refactoring checklists, security scanning automation - Source evidence: Systematic refactoring workflow and security dependency scanning processes

• **Code Quality Suite**: Plugin providing systematic code improvement processes including refactoring, security scanning, and quality gates

- What install.md would do: Set up data cleaning processes, validation frameworks, quality assessment tools - Processes it would copy: data-cleaning, data validation workflows - Configs/hooks it would create: Data quality gates, validation rules, cleaning pipeline configs - Source evidence: Comprehensive data cleaning and analytics workflow skills

• **Data Engineering Toolkit**: Plugin for data science projects providing data quality and processing workflows

- What install.md would do: Set up file organization systems, meeting coordination workflows, communication templates - Processes it would copy: file-organization, meeting-scheduler, email-drafting - Configs/hooks it would create: File organization rules, meeting templates, communication standards - Source evidence: Productivity and workflow skills with systematic organization approaches

• **Productivity Automation**: Plugin installing workflow automation and organization processes for general productivity

Harness Integration Ideas

N/A - This is not a harness framework repository.

Implicit Procedural Knowledge

- Source: Refactoring skill's code smell identification methodology - Placement: specializations/shared/code-quality-assessment - Why codify: Reusable pattern for any code analysis task, not just refactoring - Sketch: Scan code → Identify patterns → Classify smells → Prioritize fixes → Document findings

• **Code Smell Detection Strategy**: Systematic approach to identifying code quality issues across languages

- Source: SKILL_TEMPLATE.md and consistent skill structure across domains - Placement: specializations/shared/skill-creation - Why codify: Template for creating high-quality, procedural skills rather than just expert personas - Sketch: Define workflow steps → Add concrete guidance → Include verification → Document usage patterns

• **Multi-Domain Skill Template**: Standardized approach to creating systematic skill definitions

- Source: Systematic organization and procedural content across 90+ skills - Placement: specializations/shared/process-extraction - Why codify: Enables extraction of procedural knowledge from narrative skill descriptions - Sketch: Analyze skill content → Identify procedural steps → Extract workflow → Classify domain → Package as process

• **Domain-Specific Process Extraction**: Method for identifying transferable procedures within domain-specific skills

Library Mapping

Plugin Marketplace Mapping