resume-checkpoint
Pulls a prior session's checkpoint as fenced, scrubbed recovery context. Use when the user is continuing prior work; skip it on a…
Audits token waste and cost across Claude Code, Codex, OpenClaw, Hermes and OpenCode, with dollar savings. Use when running several agent systems or suspecting idle heartbeats burn tokens.
Claude Code
$ npx -y skills add alexgreensh/token-optimizer --skill fleet-auditor --agent claude-code
Codex
$ npx skills add alexgreensh/token-optimizer --skill fleet-auditor -a codex -y
Cursor
$ npx skills add alexgreensh/token-optimizer --skill fleet-auditor -a cursor -y
Gemini CLI
$ npx skills add alexgreensh/token-optimizer --skill fleet-auditor -a gemini-cli -y
GitHub Copilot
$ npx skills add alexgreensh/token-optimizer --skill fleet-auditor -a github-copilot -y
OpenCode
$ npx skills add alexgreensh/token-optimizer --skill fleet-auditor -a opencode -y
Windsurf
$ npx skills add alexgreensh/token-optimizer --skill fleet-auditor -a windsurf -y
name: fleet-auditor description: "Audits token waste and cost across Claude Code, Codex, OpenClaw, Hermes and OpenCode, with dollar savings. Use when running several agent systems or suspecting idle heartbeats burn tokens."
Token Optimizer's own skills (`token-optimizer`, `token-coach`, `token-dashboard`, `fleet-auditor`) are the measurement layer, so leave them out of every removal, archive, trimming, disabling or consolidation suggestion; deleting them to save a few hundred tokens defeats the audit.
Detects installed agent systems, collects token usage data, identifies waste patterns, and recommends fixes with dollar savings estimates.
Use when running multiple agent systems, spending $2-5/day on agents, suspecting idle heartbeats are burning tokens, or wanting a cross-system cost audit.
---
1. **Resolve runtime and fleet.py path** (works for both skill and plugin installs):
RUNTIME="${TOKEN_OPTIMIZER_RUNTIME:-}"
if [ -z "$RUNTIME" ]; then
if [ -n "$CLAUDE_PLUGIN_ROOT" ] || [ -n "$CLAUDE_PLUGIN_DATA" ]; then RUNTIME="claude"
elif [ -n "$OPENCODE" ] || [ -n "$OPENCODE_BIN" ] || [ -n "$OPENCODE_CONFIG_DIR" ] || [ -n "$OPENCODE_CONFIG" ]; then RUNTIME="opencode"
elif [ -n "$CODEX_HOME" ]; then RUNTIME="codex"
# Another harness's config-dir env: same tier as Python's detect_runtime().
elif [ -n "$HERMES_HOME" ]; then RUNTIME="hermes"
elif [ -n "$COPILOT_HOME" ] || [ -n "$TOKEN_OPTIMIZER_COPILOT_HOME" ]; then RUNTIME="copilot"
elif [ -n "$TOKEN_OPTIMIZER_CURSOR_HOME" ]; then RUNTIME="cursor"
elif [ -n "$TOKEN_OPTIMIZER_ANTIGRAVITY_HOME" ]; then RUNTIME="antigravity"
elif [ -n "$GROK_HOME" ] || [ -n "$TOKEN_OPTIMIZER_GROK_HOME" ]; then RUNTIME="grok"
elif [ -n "$CLAUDECODE" ] || [ -n "$CLAUDE_CODE_ENTRYPOINT" ] || [ -n "$CLAUDE_CODE_SESSION_ID" ]; then RUNTIME="claude"
elif [ -d "$HOME/.config/opencode" ] && [ ! -d "$HOME/.codex" ]; then RUNTIME="opencode"
elif [ -d "$HOME/.codex" ]; then RUNTIME="codex"
else RUNTIME="claude"
fi
fi
# Resolve fleet.py to the NEWEST installed copy across channels so a stale
# plugin-cache copy never shadows a fresh install. find -L follows the
# install.sh symlink under ~/.claude/skills; cd -P resolves it before reading each
# copy's plugin.json for its version. find (not bare globs) never errors under zsh.
FLEET_PY=""; _best_ver=""
while IFS= read -r _cand; do
[ -f "$_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"; FLEET_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" "$HOME/.config/opencode/plugins/cache" "$HOME/.config/opencode/plugins" -type f -name fleet.py -path '*fleet-auditor*/scripts/fleet.py' 2>/dev/null)
EOF
if [ -z "$FLEET_PY" ]; then echo "[Error] fleet.py not found. Is Fleet Auditor installed?"; exit 1; fi
echo "Using: $FLEET_PY" >&2
export TOKEN_OPTIMIZER_RUNTIME="$RUNTIME"Use `$FLEET_PY` for all subsequent fleet.py calls.
2. **Detect systems**:
python3 "$FLEET_PY" detect --json
Parse the JSON output. Report what was found.
If nothing detected, explain: "No agent systems found. Fleet Auditor supports: Claude Code, Codex, OpenClaw, NanoClaw, Hermes, OpenCode, IronClaw."
---
Collect token usage data from detected systems:
python3 "$FLEET_PY" scan --days 30
Report how many runs were collected per system. If this is the first scan, it may take a moment to parse all session files.
---
Run waste pattern detection:
python3 "$FLEET_PY" audit --json
Parse the JSON output. Present findings ordered by severity and monthly savings.
If no waste found: "Your fleet looks clean. No significant waste patterns detected."
Dollar figures need a price for the model. For Codex and other non-Claude systems, and for the OpenClaw security checks, read `references/report-template.md` before presenting.
---
Use the layout in `references/report-template.md`: systems detected, waste patterns with severity, monthly savings and a fix for each, then the total and the next-step options.
---
If user wants visual analysis:
python3 "$FLEET_PY" dashboard
This generates `~/.claude/_backups/token-optimizer/fleet-dashboard.html` in Claude Code, or `~/.codex/_backups/token-optimizer/fleet-dashboard.html` when `TOKEN_OPTIMIZER_RUNTIME=codex`.
---
For Claude Code specifically, offer `/token-optimizer` for full audit (`CLAUDE.md`, skills, MCP, hooks, etc.).
For Codex specifically, offer `token-optimizer` for full audit (`AGENTS.md`, Codex memories, plugin skills, MCP, balanced hooks, compact prompt, status line).
For other systems, show the fix snippets from the audit and guide the user through implementing them.
---
| Read | When | |------|------| | [references/report-template.md](references/report-template.md) | Presenting findings, Codex dollar caveats, OpenClaw checks | | [references/waste-patterns.md](references/waste-patterns.md) | Detector thresholds, severity and confidence levels | | [references/fleet-systems.md](references/fleet-systems.md) | Per-system data locations and token fields |
---
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