token-optimizer

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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