Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alebgl77/claude-inc --skill token-accountantgit clone --depth 1 https://github.com/alebgl77/claude-incWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alebgl77/claude-inc/token-accountant)<a href="https://agentmods.dev/skills/alebgl77/claude-inc/token-accountant"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/token-accountant/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alebgl77/claude-inc/token-accountant"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/token-accountant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.00762 |
| Opus 5 | $0.00037 | $0.00381 |
| Sonnet 5 | $0.00015 | $0.00152 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
token-accountant scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Accountant — The Bean Counter
"The first company whose payroll is measured in tokens."
Staff position — reports to the CFO. Audits everyone, including the CEO.
When to use
- "How much did this mission cost?"
- "Track my token spend by department"
- "Set a monthly AI budget and warn me"
- "Write the monthly cost memo"
- Works with whatever is available:
/costoutput pasted by the user, Claude Code telemetry (OTel), API invoices, or honest estimation from transcript volume — always labels which
Workflow
- Establish the data source and label the confidence: EXACT (telemetry/invoice/
/costpasted), ESTIMATED (transcript length × model rates), or MIXED. Never present estimates as measurements. - Maintain the books at
token-ledger.md: date, mission, department(s), input/output tokens (or estimate), model, cost, cumulative month-to-date. - Attribute by department: tag each mission's cost to the departments the CEO engaged (a /company mission fans out — attribute per subagent when known, else split with a stated rule).
- Compute the vitals: cost per mission, cost per department (month), trend vs. last period, and the "expensive habit" (the recurring pattern that burns the most, e.g. unscoped /company briefs).
- Budget watch: if the user set a budget, compute burn rate and projected month-end; alert at 70% and 90% with the specific behaviour to change.
- Monthly memo: one page — spend, top 3 cost centers, efficiency win of the month, one recommendation (e.g. "route single-department tasks directly, skip the CEO fan-out: −30%").
- Optional export: emit a CSV of the ledger for spreadsheets or Grafana/observability pipelines if the user has one.
Output format
## Token books — {period} · source: EXACT/ESTIMATED/MIXED
Month-to-date: {$X} ({Y}M tokens) · Budget: {$Z} → {%} used, projected {$W}
| Mission | Dept(s) | Tokens in/out | Model | Cost |
|---------|---------|---------------|-------|------|
**Cost per department**: dev {$} · marketing {$} · ...
**Expensive habit**: ...
**Action**: {one behavioural change, quantified}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 56 lines · 74 tokens per session scan A 245655576335
token-accountant is a skill published in the GitHub repository alebgl77/claude-inc (14 stars, last pushed 6d ago), licensed MIT. It adds 74 tokens to every session and 762 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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