Best use case
Context Pruner Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
**Version:** 1.0.0
Teams using Context Pruner 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-context-pruner/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Context Pruner Skill Compares
| Feature / Agent | Context Pruner 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?
**Version:** 1.0.0
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
# Context Pruner Skill **Version:** 1.0.0 **Purpose:** Hierarchical context summarization for CFN Loop v3 iterations ## Overview Reduces context size across iterations by: - Keeping current iteration in full detail - Summarizing previous iterations - Extracting key themes from feedback - Maintaining deliverable tracking ## Goal Reduce context from 120 KB (iteration 10) to 15 KB (88% reduction) ## Usage ```bash PRUNED_CONTEXT=$(./.claude/skills/context-pruner/prune-context.sh \ --iteration 3 \ --full-history "$FULL_HISTORY" \ --current-context "$CURRENT_CONTEXT") echo "$PRUNED_CONTEXT" ``` ## Pruning Strategy ### Iteration 1 - Full detail (no pruning) - Context size: ~5 KB ### Iteration 2 - Iteration 1: Summary only - Iteration 2: Full detail - Context size: ~8 KB ### Iteration 3+ - Iterations 1-(N-1): Summary with key themes - Iteration N: Full detail - Context size: ~10-15 KB (stable) ## Summary Format ``` Iterations 1-2 Summary: - Initial confidence: 0.72 - Key feedback themes: * Add error handling * Improve test coverage * Address security concerns - Final confidence: 0.82 - Progress: +0.10 ``` ## Key Themes Extraction Analyze all feedback across iterations to extract recurring themes: ```bash # Count feedback occurrences "Add error handling" - 3 times (iterations 1, 2, 3) "Improve test coverage" - 2 times (iterations 1, 2) "Security concerns" - 2 times (iterations 2, 3) # Output top 5 themes ``` ## Integration Used by: - Main Chat loop orchestration - Coordinator for iteration context building
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