Usage Costs Skill#

Reports token usage and estimated costs from OpenClaw sessions.

---

Load Local Context#

bash

CONTEXT_FILE="/opt/ocana/openclaw/workspace/skills/usage-costs/.context"
[ -f "$CONTEXT_FILE" ] && source "$CONTEXT_FILE"
# Provides: $OWNER_PHONE, $PRICING_INPUT, $PRICING_OUTPUT, $PRICING_CACHE_READ

---

Data Sources#

1. Live sessions → openclaw status --deep (current token counts per session)
2. Cron run history → /opt/ocana/openclaw/cron/runs/*.jsonl (usage field per run)
3. Token history → /opt/ocana/openclaw/workspace/data/token-history.jsonl (daily aggregates)

---

Pricing (claude-sonnet-4-6, as of 2026-04)#

TypePriceInput$3.00 / 1M tokensOutput$15.00 / 1M tokensCache read$0.30 / 1M tokensCache write$3.75 / 1M tokens

---

Step 1 — Live Session Report#

bash

# Get current session token counts
openclaw status --deep 2>/dev/null | grep -E "agent:main|direct|cached" | head -20

Parse output: each row has session_key | kind | age | model | tokens.

---

Step 2 — Cron History Report#

python

#!/usr/bin/env python3
import json, glob, os
from datetime import datetime, timezone, timedelta

def get_cron_usage(days_back=1):
    cutoff = datetime.now(timezone.utc) - timedelta(days=days_back)
    cutoff_ts = cutoff.timestamp() * 1000

    total_input = 0
    total_output = 0
    runs = []

    for f in glob.glob('/opt/ocana/openclaw/cron/runs/*.jsonl'):
        job_name = os.path.basename(f).replace('.jsonl', '')
        with open(f) as fh:
            for line in fh:
                try:
                    d = json.loads(line)
                    if d.get('ts', 0) >= cutoff_ts and 'usage' in d:
                        inp = d['usage'].get('input_tokens', 0)
                        out = d['usage'].get('output_tokens', 0)
                        total_input += inp
                        total_output += out
                        runs.append({
                            'job': d.get('name', job_name),
                            'input': inp,
                            'output': out,
                            'ts': d['ts']
                        })
                except:
                    pass

    return total_input, total_output, runs

inp, out, runs = get_cron_usage(days_back=1)
cost = (inp / 1_000_000 * 3) + (out / 1_000_000 * 15)
print(f"Cron tokens (last 24h): {inp:,} in / {out:,} out")
print(f"Estimated cost: ${cost:.2f}")
print(f"Runs: {len(runs)}")

---

Report Formats#

"How much did today cost?"#

text

📊 Cost Report — 2026-04-04

Main session: ~276K tokens (100% cached)
Cron runs: 25 runs | X in / Y out tokens
Subagents: N sessions | X tokens

Estimated total: ~$Z
(Cron: $A | Subagents: $B | Main session: estimated $C)

Note: Main session cost is estimated — cache reduces actual cost by ~90%.

"How much this week?"#

• Read from /opt/ocana/openclaw/workspace/data/token-history.jsonl
• Sum daily entries for the last 7 days
• Show per-day breakdown + total

"How much was this session?"#

• Run openclaw status --deep
• Find agent:main:main row → tokens field
• Calculate: input_cost + output_cost (apply cache discount if cached%)

---

Save Daily Report#

Append to /opt/ocana/openclaw/workspace/data/token-history.jsonl:

json

{"date": "2026-04-04", "input": 133, "output": 17376, "cache_read": 900000, "cost_usd": 0.54, "cron_runs": 25, "subagent_runs": 4}

---

Cost Extraction Script (from session jsonl files)#

This is the authoritative method for extracting real costs — works for Anthropic/Claude models:

python

python3 -c "
import json, glob, os
from datetime import datetime, timezone

sessions_dir = '/opt/ocana/openclaw/agents/main/sessions'
files = glob.glob(f'{sessions_dir}/*.jsonl')
today = datetime.now(timezone.utc).date()
total_cost = 0
total_cache_write = 0
total_cache_read = 0
sessions_today = 0

for fpath in files:
    mtime = datetime.fromtimestamp(os.path.getmtime(fpath), tz=timezone.utc).date()
    if mtime != today:
        continue
    sessions_today += 1
    with open(fpath) as f:
        for line in f:
            try:
                l = json.loads(line)
                if l.get('type') == 'message' and l.get('message',{}).get('role') == 'assistant':
                    u = l['message'].get('usage',{})
                    total_cost += u.get('cost',{}).get('total',0)
                    total_cache_write += u.get('cacheWrite',0)
                    total_cache_read += u.get('cacheRead',0)
            except: pass

print(f'Today: {sessions_today} sessions — \${total_cost:.2f}')
print(f'Cache writes: {total_cache_write:,} tokens')
print(f'Cache reads: {total_cache_read:,} tokens')
"

⚠️ Provider compatibility:

• ✅ Works for: Anthropic Claude (sonnet, haiku, opus)
• ❌ Does NOT work for: Google Gemini, OpenAI GPT — cost field is empty
• For Google/OpenAI agents: use provider billing dashboard directly

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Trigger Phrases#

"how much did today cost?" "how much was this session?" "how much this week?" "show me costs"

• "usage report"
• "token usage"