context-optimizer

name	context-optimizer
description	Audits agent context window usage via debug logs, token profiling, and redundancy detection. USE FOR: context optimization, token waste analysis, debug log parsing, hand-off gap analysis. DO NOT USE FOR: Azure infrastructure, Bicep/Terraform code, architecture design, deployments.
compatibility	Requires Python 3.10+ for log parser script

Context Window Optimization Skill

Structured methodology for auditing how GitHub Copilot agents consume their context window. Identifies waste, recommends hand-off points, and produces prioritized optimization reports.

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When to Use This Skill

• Auditing context window efficiency across a multi-agent system
• Identifying where to introduce subagent hand-offs
• Reducing redundant file reads and skill loads
• Optimizing instruction file applyTo glob patterns
• Profiling per-turn token cost from debug logs
• Porting agent optimizations to a new project

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Quick Reference

Capability	Description
Log Parsing	Extract structured data from Copilot Chat debug logs
Turn-Cost Profiling	Estimate token spend per turn from timing and model metadata
Redundancy Detection	Find duplicate file reads, overlapping instructions
Hand-Off Gap Analysis	Identify agents that should delegate to subagents
Instruction Audit	Flag overly broad globs and oversized instruction files
Report Generation	Structured markdown report with prioritized recommendations

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Prerequisites

• Python 3.10+ (for log parser script)
• Access to VS Code Copilot Chat debug logs
• Agent definitions in .github/agents/*.agent.md (or equivalent)

Enabling Debug Logs

Copilot Chat writes debug logs automatically to the VS Code log directory. To find the latest logs:

find ~/.vscode-server/data/logs/ -name "GitHub Copilot Chat.log" -newer /tmp/marker 2>/dev/null \
  | sort | tail -5

For richer output, set github.copilot.advanced.debug.overrideLogLevels in VS Code settings to capture verbose tool-call data.

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Analysis Methodology

📋 Reference: Read references/analysis-methodology.md for the complete methodology including:

• Log Format Reference — ccreq line parsing, request types, latency heuristics
• Steps 1-5 — Log parsing, turn-cost profiling, agent definition audit, context growth mapping, optimization recommendations
• Common Optimization Patterns — Subagent extraction, instruction narrowing, progressive skill loading, prompt deduplication, context summarization
• Baseline Comparison — Automated snapshot/diff workflow (Phase 0 and Phase 6)

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Report Template

See templates/optimization-report.md for the full output template.

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Portability

This skill contains no project-specific logic. To use in another project:

1. Copy .github/skills/context-optimizer/ to the target repo
2. Copy .github/agents/11-context-optimizer.agent.md
3. Copy .github/instructions/context-optimization.instructions.md
4. Copy scripts/snapshot-agent-context.sh and scripts/diff-context-baseline.sh
5. Adjust agent numbering if needed (11 is the slot used in this repo)
6. The log parser auto-discovers VS Code log directories

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References

• scripts/parse-chat-logs.py — Log parser producing structured JSON
• templates/optimization-report.md — Report output template
• references/token-estimation.md — Detailed token cost heuristics

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Reference Index

Reference	When to Load
references/token-estimation.md	When estimating token counts for context optimization
references/analysis-methodology.md	Log format, 5-step methodology, optimization patterns, baseline comparison