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token-optimizer

token-optimizer

github.com/alexgreenshAgent workflows2.8k998 installsUpdated today
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About token-optimizer

Token Optimizer audits an AI setup's context-window usage, proposes changes, and measures the result after approved edits. It is designed for users optimizing agent configurations, with backups and before-and-after measurements for verification.

Key features

  1. 01Measure context-window usage
  2. 02Audit configuration across setup areas
  3. 03Propose optimization changes
  4. 04Back up and show diffs before edits
  5. 05Re-measure approved changes

Use cases

  1. 01Find context overhead in agent configuration
  2. 02Review MCP servers and skills for cleanup
  3. 03Verify context savings after approved edits

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alexgreensh/token-optimizer --skill token-optimizer

After installing, type/token-optimizerin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
active-compression-hero.svg
automated-flow.svg
bash-compression.svg
before-after.svg
dashboard.html
delta-mode.svg
fleet-demo.html
hero-terminal.svg
how-it-works.svg
logo.svg
logo.txt
quality-example.svg
quality-nudges-loops.svg
real-savings.svg
session-database-flow.svg
status-bar.svg
user-profiles.svg
assets/platforms/
antigravity.svg
claude-code.svg
codex.svg
copilot.svg
cursor.svg
grok.svg
hermes.svg
openclaw.svg
opencode.svg
pi.svg
row-break-none.svg
row-break.svg
examples/
claude-md-optimized.md

Details

From the project README

Token Optimizer

Measures where a setup spends its context window, proposes fixes, applies the ones the user approves, and re-measures. The deliverable is a before-and-after the user can verify, with every change backed up. Typical recovery is 5-15% of the window from config cleanup, more when compaction and caching settings are wrong for the way the user works.

Token Optimizer's own skills (token-optimizer, token-coach, token-dashboard, fleet-auditor, resume-checkpoint) are the measurement layer, so leave them out of every unused-skill, archive or consolidation suggestion, however rarely they run.

Step 0: pick the right workflow for this runtime

The audit below reads and edits ~/.claude, which is the wrong target anywhere but Claude Code, so settle the runtime before touching any path. Run exactly this, in this order.

a. Environment check (touches no file; an explicit TOKEN_OPTIMIZER_RUNTIME wins, and Claude plugin variables are checked before stray OPENCODE_* exports, the same order detect_runtime() uses):

if [ "${TOKEN_OPTIMIZER_RUNTIME:-}" = "claude" ] || [ "${TOKEN_OPTIMIZER_RUNTIME:-}" = "codex" ]; then
  :
elif [ "${TOKEN_OPTIMIZER_RUNTIME:-}" = "opencode" ]; then
  echo "Token Optimizer — OpenCode runtime detected."
elif [ "${TOKEN_OPTIMIZER_RUNTIME:-}" = "copilot" ]; then
  echo "Token Optimizer — GitHub Copilot runtime detected."
elif [ "${TOKEN_OPTIMIZER_RUNTIME:-}" = "cursor" ]; then
  echo "Token Optimizer — Cursor runtime detected."
elif [ "${TOKEN_OPTIMIZER_RUNTIME:-}" = "antigravity" ]; then
  echo "Token Optimizer — Google Antigravity runtime detected."
elif [ -n "${CLAUDE_PLUGIN_ROOT:-}${CLAUDE_PLUGIN_DATA:-}" ]; then
  :
elif [ -n "${OPENCODE_BIN:-}${OPENCODE_CONFIG_DIR:-}${OPENCODE_DATA_DIR:-}${OPENCODE_CONFIG:-}${OPENCODE_CLIENT:-}" ]; then
  echo "Token Optimizer — OpenCode runtime detected."
elif [ -n "${COPILOT_HOME:-}${TOKEN_OPTIMIZER_COPILOT_HOME:-}" ]; then
  echo "Token Optimizer — GitHub Copilot runtime detected."
elif [ -n "${TOKEN_OPTIMIZER_CURSOR_HOME:-}" ]; then
  echo "Token Optimizer — Cursor runtime detected."
elif [ -n "${CURSOR_PROJECT_DIR:-}" ] && [ -n "${CURSOR_VERSION:-}" ]; then
  echo "Token Optimizer — Cursor runtime detected."
elif [ -n "${TOKEN_OPTIMIZER_ANTIGRAVITY_HOME:-}" ]; then
  echo "Token Optimizer — Google Antigravity runtime detected."
fi
It printsDo this
"OpenCode runtime detected."Stop. Follow references/opencode-workflow.md (Token Optimizer runs as a native plugin there).
"Cursor runtime detected."Stop. Follow references/cursor-workflow.md (it runs through the Cursor hook bridge).
"GitHub Copilot runtime detected."Stop. Follow the Copilot guidance in docs/copilot.md.
"Google Antigravity runtime detected."Stop. Follow docs/antigravity.md.
nothingContinue to b.

b. Resolve $MEASURE_PY once. Every later command uses it:

# Resolve measure.py to the NEWEST installed copy across channels so a stale
# plugin-cache copy never shadows a fresh install. find -L follows the
# install.sh symlink under ~/.claude/skills; cd -P resolves it before reading each
# copy's plugin.json for its version. find (not bare globs) never errors under zsh.
MEASURE_PY=""; _best_ver=""
while IFS= read -r _cand; do
  [ -f "$_cand" ] || continue
  _root="$(cd -P -- "$(dirname -- "$_cand")/../../.." 2>/dev/null && pwd)"
  _ver="$(sed -n 's/.*"version"[[:space:]]*:[[:space:]]*"\([^"]*\)".*/\1/p' "$_root/.claude-plugin/plugin.json" 2>/dev/null | head -1)"
  [ -n "$_ver" ] || _ver="0.0.0"
  if [ -z "$_best_ver" ] || [ "$(printf '%s\n%s\n' "$_ver" "$_best_ver" | sort -t. -k1,1n -k2,2n -k3,3n -k4,4n | tail -n1)" = "$_ver" ]; then
    _best_ver="$_ver"; MEASURE_PY="$_cand"
  fi
done <<EOF
$(find -L "$HOME/.claude/skills" "$HOME/.claude/plugins/cache" "$HOME/.claude/token-optimizer" "$HOME/.codex/skills" "$HOME/.codex/plugins/cache" "$HOME/.config/opencode/plugins" -type f -name measure.py -path '*token-optimizer*/scripts/measure.py' 2>/dev/null)
EOF
if [ -z "$MEASURE_PY" ]; then echo "[Error] measure.py not found. Is Token Optimizer installed?"; exit 1; fi

c. The authoritative gate. The environment check cannot see an OpenCode started without its variables; this one scans the process tree:

python3 "$MEASURE_PY" report 2>&1 | head -1

Any "... runtime detected." line means stop and follow that runtime's file from the table above. On Codex (TOKEN_OPTIMIZER_RUNTIME=codex or a Codex environment) follow references/codex-workflow.md. Claude Code continues below.

The audit (Claude Code)

Work through these in order and tick each off; a skipped phase stays in the list as skip: <reason>.

- [ ] 0. Setup            references/phase0-setup.md
- [ ] 1. Parallel audit   references/agent-prompts.md
- [ ] 2. Synthesis        references/agent-prompts.md (Synthesis Agent)
- [ ] 3. Present, then wait for the user's decision   references/presentation-workflow.md
- [ ] 4. Implement the approved changes               references/implementation-playbook.md
- [ ] 5. Verify and report before/after               references/agent-prompts.md (Verification Agent)

0. Setup. Context window detection, pre-check, backup, the coordination folder (COORD_PATH), hook checks, daemon, smart compaction. Then, only when python3 "$MEASURE_PY" keepwarm-consent-status returns should_ask: true, follow references/keepwarm-consent.md exactly; it spends the user's money, so its wording and command order are fixed.

1. Parallel audit. Dispatch six agents in one message, each with COORD_PATH and its prompt from references/agent-prompts.md. They gather and count, so they run on the cheapest capable model; the synthesis is where judgement is spent.

AgentWritesLooks at
CLAUDE.mdaudit/claudemd.mdSize, duplication, tiering, cache structure
MEMORY.mdaudit/memorymd.mdSize, overlap with CLAUDE.md
Skillsaudit/skills.mdCount, frontmatter overhead, duplicates
MCPaudit/mcp.mdDeferred tools, broken or unused servers
Commandsaudit/commands.mdCount, menu overhead
Settings and advancedaudit/advanced.mdHooks, rules, settings, @imports, cache lifetime, compaction window

A missing output file is a gap to note, not a reason to stop. 2. Synthesis. One agent on the strongest available model reads every audit file and writes {COORD_PATH}/analysis/optimization-plan.md. If it fails, present the raw audit files.

3. Present. Generate the dashboard and show the findings as the presentation workflow describes, then wait:

python3 "$MEASURE_PY" dashboard --coord-path "$COORD_PATH"

4. Implement. Only what the user approved, one change at a time, following the playbook (actions 4A-4P: CLAUDE.md, MEMORY.md, skills, file exclusion, MCP, hooks, cache, rules, settings, descriptions, compact instructions, model routing, smart compaction, quality check, version-aware optimizations, smart routing). Templates are in examples/. Also offer, with the measured numbers:

  • python3 "$MEASURE_PY" recommendations: the daily-measured usage recommendations (compact window, subagent cache lifetime), each with the user's own numbers and the exact command when the state is recommend. Relay those lines verbatim; Token Optimizer recommends, it never sets either key.
  • python3 "$MEASURE_PY" subagent-cache status / compact-advice: the single-measurement detail behind each item. Both are estimates from the user's own history; the user decides.
  • Repeated work a script could do instead of a model: hand over to token-coach, goal e.

5. Verify. The verification agent re-measures and computes the saving. Show before and after, then the habits that keep it.

If a verification number is worse than the baseline, find which change caused it, restore that file from the backup, and return to step 4.

Rules for changing a user's setup

These protect work the user cannot easily get back:

  • Back up before every change, and show the diff before applying it.
  • Archive, never delete, and archive outside the skills directory so the archive does not load.
  • Check what depends on a skill, MCP server or deny rule before archiving it, and name the side effect first.
  • Prefer a project-level deny rule to a global one.
  • Leave the user's own settings values alone unless they approve the specific change.
  • Quote measured numbers (tokens and percent of the window). Frame savings as context budget; give dollars only where measure.py computed them.

References

NeedRead
Codex, Cursor, OpenCode workflowsreferences/codex-workflow.md, references/cursor-workflow.md, references/opencode-workflow.md
Setup details, Keep-Warm consentreferences/phase0-setup.md, references/keepwarm-consent.md
Agent prompts, how tokens flowreferences/agent-prompts.md, references/token-flow-architecture.md
Presenting findingsreferences/presentation-workflow.md
Implementation steps, checklistreferences/implementation-playbook.md, references/optimization-checklist.md, examples/
CLI commands, session continuity, errorsreferences/cli-reference.md, references/session-continuity.md, references/error-recovery.md

Written for and checked on Claude Opus 5.5 and Sonnet 5.5, and GPT-5.6 on Codex.

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