Best use case
Improvement Recommender Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Purpose
Teams using Improvement Recommender Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cfn-improvement-recommender/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Improvement Recommender Skill Compares
| Feature / Agent | Improvement Recommender Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Purpose
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Improvement Recommender Skill ## Purpose Generate actionable improvement suggestions based on sprint retrospective data. ## Core Capabilities - Analyze sprint performance metrics - Identify bottlenecks and inefficiencies - Recommend targeted improvements - Provide confidence scores for recommendations ## Recommendation Categories - Agent Selection - Feedback Processing - Validation Criteria - Iteration Management - Complexity Estimation ## Evaluation Criteria - Frequency of issue occurrence - Impact on sprint velocity - Cost of implementing recommendation - Potential performance improvement ## Improvement Scoring Model - Confidence Level (0.0 - 1.0) - Expected Benefit Quantification - Implementation Complexity Rating ## Continuous Learning - Track recommendation effectiveness - Update recommendation strategies - Learn from successful interventions
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