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 Lingtai-AI/lingtai --skill token-usagegit clone --depth 1 https://github.com/Lingtai-AI/lingtaiWrote 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/lingtai-ai/lingtai/token-usage)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/token-usage"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/token-usage.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 102 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00050 | $0.01380 |
| Opus 5 | $0.00025 | $0.00690 |
| Sonnet 5 | $0.00010 | $0.00276 |
| Haiku 4.5 | $0.00005 | $0.00138 |
Grade A, and why
token-usage 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 4d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Usage, Cost, and Agentic-Intensity Reports v2
Network-wide token cost analysis powered by litellm's model pricing database (2700+ models), plus runtime-event trend analysis for tool calls per API call.
Quick Usage
Run the bundled cost script:
~/.lingtai-tui/runtime/venv/bin/python3 ~/.lingtai-tui/utilities/swiss-knife/reference/token-usage/scripts/cost_report.py /path/to/.lingtai
Cost-report optional flags:
--json— output as JSON instead of table--by-model— group by model instead of by agent--since YYYY-MM-DD— only count entries after this date--top N— show only top N agents (default: all)--custom-pricing FILE— load custom pricing overrides from JSON
Tool-call/API-call trend
Use this when the human asks for “tool calls per API call”, “tools per API call”, “agentic intensity”, or how tool-heavy the local LingTai network has been over time. The script reads LingTai logs/events.jsonl files, not token_ledger.jsonl, because it needs runtime event IDs.
~/.lingtai-tui/runtime/venv/bin/python3 ~/.lingtai-tui/utilities/swiss-knife/reference/token-usage/scripts/tool_calls_per_api_call_trend.py \
/path/to/project/.lingtai \
--days 5 \
--timezone America/Los_Angeles \
--model gpt-5.5
Optional flags:
--out-prefix PREFIX— writePREFIX.md,PREFIX.json, andPREFIX.csv--model MODEL— also emit a model-specific table for an exact model name; repeatable--include-daemons— include daemon event logs instead of excluding them
Metric definitions:
- API calls: unique successful LLM response events (
type == "llm_response"), deduplicated by(events log path, api_call_id). - Tool calls: unique model-requested top-level tool calls (
type == "tool_call_received"), deduplicated by(events log path, api_call_id, tool_call_id)and assigned to the local-day bin of the producing API response. - Tool calls per API call:
tool_calls / api_calls. - Tool-using API-call rate: fraction of API calls that produced at least one tool call.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 126 lines · 50 tokens per session scan A 034110d2aafb
token-usage is a skill published in the GitHub repository Lingtai-AI/lingtai (696 stars, last pushed today), licensed Apache-2.0. It adds 50 tokens to every session and 1,380 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
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budget
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smart-cost-tracker
Track AI agent spending in real time. Shows cost per message, per conversation, per day. Budget alerts, daily/weekly reports, cost-per-task breakdown. Use when the user asks about spending, costs, tokens, budget, or billing. Triggers on: "how much did that cost", "show my spending", "token usage", "budget", "cost…
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Enforce and inspect token/dollar budgets for OpenMesha agents. Use when the user asks about agent spend, budgets, cost ledgers, or runaway usage.
cost-control
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playwright
Use when the task requires capturing or automating a real browser from the terminal.