2668 Context Optimizeprompt C1dd481e

Context Window Optimization Audit

Mission

Perform a comprehensive context window utilization audit across all agents, skills, and instruction files in this repository. Identify wasted tokens, redundant context loads, missing hand-off points, and overly broad globs. Produce a structured optimization report with prioritized, actionable recommendations.

Scope & Preconditions

• Analyze the latest Copilot Chat debug log session(s)
• Audit every agent definition in .github/agents/*.agent.md
• Audit every skill in .github/skills/*/SKILL.md
• Audit every instruction file in .github/instructions/*.instructions.md
• Save the report to agent-output/{project}/11-context-optimization-report.md

Inputs

• Chat debug logs: auto-discovered from ~/.vscode-server/data/logs/
• Agent definitions: .github/agents/*.agent.md
• Skills: .github/skills/*/SKILL.md
• Instructions: .github/instructions/*.instructions.md

Workflow

Phase 1 — Discovery & Log Collection

1. Read AGENTS.md for the agent roster and project map
2. Read .github/skills/context-optimizer/SKILL.md for analysis methodology
3. Locate Copilot Chat debug logs using:

   find ~/.vscode-server/data/logs/ -name "GitHub Copilot Chat.log" 2>/dev/null | sort | tail -10
   

4. Run the log parser to extract structured data:

   python3 .github/skills/context-optimizer/scripts/parse-chat-logs.py \
     --log-dir ~/.vscode-server/data/logs/ \
     --output /tmp/context-audit.json
   

5. Present a session summary (total requests, models, time range)
6. Ask which session(s) to analyze in depth (default: all recent)

Phase 2 — Turn-Cost Profiling

For each session:

• Count total ccreq entries and group by session ID
• Calculate average latency per model (Opus, GPT-5.3-Codex, gpt-4o-mini)
• Identify long-tail turns (> 15s latency) — flag as context-heavy
• Detect burst patterns (< 2s gap between calls = likely tool-call loop)
• Map latency escalation trends (context growing without hand-offs)
• Classify request types (editAgent, title, progressMessages, subagent)

Phase 3 — Agent Definition Audit

For each agent file, check and report:

Phase 4 — Instruction & Skill Audit

For each instruction and skill file:

Phase 5 — Report Generation

Write the report to agent-output/{project}/11-context-optimization-report.md using the template from the skill. Include:

• Executive summary table (current vs target metrics)
• Findings by severity (Critical → Low)
• Recommended hand-off points with estimated token savings
• Instruction consolidation opportunities
• Agent-specific recommendations
• Implementation priority matrix (effort vs impact)

Output Expectations

• File: agent-output/{project}/11-context-optimization-report.md
• Format: Markdown with tables, severity-coded sections
• Content: Every finding must include issue, recommendation, and estimated impact
• Actionable: Each recommendation should specify which file to change and how

Quality Assurance

• All agent definitions were analyzed (count matches .github/agents/)
• All instruction files were audited (count matches .github/instructions/)
• All skills were audited (count matches .github/skills/)
• Log parser ran successfully or manual analysis was performed
• Report follows the template in the context-optimizer skill
• Findings are prioritized (P0 → P3)
• Token savings estimates are included for each recommendation