build-with-dhiraj Token Optimizer
Audits a Claude Code or Codex setup, identifies context window waste, implements fixes, and measures savings.
Target: 5-15% context recovery through config cleanup, up to 25%+ with autocompact management.
Codex Runtime
If TOKEN_OPTIMIZER_RUNTIME=codex or Codex environment is detected, read references/codex-workflow.md and follow its chat-first workflow instead of the Claude Code phases below.
Phase 0: Initialize (Claude Code)
Resolve measure.py path:
MEASURE_PY=""
for f in "$HOME/.claude/skills/token-optimizer/scripts/measure.py" \
"$HOME/.claude/plugins/cache"/*/token-optimizer/*/skills/token-optimizer/scripts/measure.py; do
[ -f "$f" ] && MEASURE_PY="$f" && break
done
[ -z "$MEASURE_PY" ] && { echo "[Error] measure.py not found."; exit 1; }
Read references/phase0-setup.md for the full setup sequence: context window detection, pre-check, backup, coordination folder, hook checks, daemon setup, and smart compaction.
Phase 1: Quick Audit (Parallel Agents)
Read references/agent-prompts.md for all prompt templates.
Dispatch 6 agents in parallel:
| Agent | Output File | Model | Task |
|---|---|---|---|
| CLAUDE.md Auditor | audit/claudemd.md |
sonnet | Size, duplication, tiered content, cache structure |
| MEMORY.md Auditor | audit/memorymd.md |
sonnet | Size, overlap with CLAUDE.md |
| Skills Auditor | audit/skills.md |
sonnet | Count, frontmatter overhead, duplicates |
| MCP Auditor | audit/mcp.md |
sonnet | Deferred tools, broken/unused servers |
| Commands Auditor | audit/commands.md |
haiku | Count, menu overhead |
| Settings & Advanced | audit/advanced.md |
sonnet | Hooks, rules, settings, @imports, caching |
Pass COORD_PATH to each. Wait for all to complete. If any output file is missing, note the gap and proceed.
Phase 2: Analysis
Read the Synthesis Agent prompt from references/agent-prompts.md. Dispatch with model="opus" (fallback: sonnet). It reads all audit files and writes {COORD_PATH}/analysis/optimization-plan.md. If missing, present raw audit files instead.
Phase 3: Present Findings
Read references/presentation-workflow.md for the findings template, dashboard generation, and URL presentation logic. Generate the dashboard:
python3 $MEASURE_PY dashboard --coord-path $COORD_PATH
Wait for user decision before proceeding.
Phase 4: Implementation
Read references/implementation-playbook.md for detailed steps. Available actions: 4A-4P covering CLAUDE.md, MEMORY.md, Skills, File Exclusion, MCP, Hooks, Cache, Rules, Settings, Descriptions, Compact Instructions, Model Routing, Smart Compaction, Quality Check, Version-Aware Optimizations, and Smart Routing. Templates in examples/. Always backup before changes. Present diffs for approval.
Phase 5: Verification
Read the Verification Agent prompt from references/agent-prompts.md. Dispatch with model="haiku". Re-measures everything and calculates savings. Present before/after comparison and behavioral next steps.
Reference Files
| Context | Read |
|---|---|
| Codex runtime | references/codex-workflow.md |
| Phase 0 setup details | references/phase0-setup.md |
| Phase 1-2 agent prompts | references/agent-prompts.md, references/token-flow-architecture.md |
| Phase 3 presentation | references/presentation-workflow.md |
| Phase 4 implementation | references/implementation-playbook.md, examples/ |
| CLI commands | references/cli-reference.md |
| Phase 3 checklist | references/optimization-checklist.md |
| Error handling | references/error-recovery.md |
Core Rules
- Quantify everything (X tokens, Y%)
- Create backups before any changes
- Ask user before implementing
- Never delete files, always archive outside the skills directory
- Check dependencies before archiving (skills, MCP, deny rules can break other tools)
- Warn about side effects before each change
- Prefer project-level deny rules over global
- Show before/after diffs
- Frame savings as context budget (% of window), not dollar amounts