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 agentmods add commands/fatihkan/badi/ai-tokengit clone --depth 1 https://github.com/fatihkan/badiWrote 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/commands/fatihkan/badi/ai-token)<a href="https://agentmods.dev/commands/fatihkan/badi/ai-token"><img src="https://agentmods.dev/badge/commands/fatihkan/badi/ai-token.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.00283 |
| Opus 5 | $0.00000 | $0.00142 |
| Sonnet 5 | $0.00000 | $0.00057 |
| Haiku 4.5 | $0.00000 | $0.00028 |
Grade A, and why
ai-token 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 5d 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.
What it actually says
Badi/.claude/ token usage analysis command. Categorized token counts, largest files, optimization suggestions.
Required Tools
- Bash (badi ai token)
Procedure
Step 1: Run
badi ai token
Step 2: Interpret the Results
Category breakdown:
- agents — Agent definitions
- commands — Slash commands
- hooks — Shell hooks
- skills — Skill library (usually the largest)
- references — Project guides
- memory/workspace — Project notes
Total token thresholds:
< 80K— Healthy80-150K— Needs monitoring> 150K— Optimization mandatory
Step 3: Optimization Suggestions
If the total is high:
- Split large SKILL.md files — move into a
references/subdirectory - Remove unused commands — slash commands nobody invokes
- Minimize CLAUDE.md — 1.2KB target
- Log rotation — automatic cap on .claude/logs/
Step 4: Follow-up
Weekly check:
/ai-tokenevery Monday morning- Watch the trend (investigate on 10%+ weekly growth)
Example
/ai-token
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.
- 5d ago First seen · 47 lines · 0 tokens per session scan A 43f6d75a5b8b
ai-token is a command published in the GitHub repository fatihkan/badi (7 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 283 tokens. 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 2026-08-31.
Other commands, from other repositories
audit-agents-skills
Audit quality of agents, skills, and commands in a Claude Code project.
land-and-deploy
Merge PR, wait for CI, verify deploy, run canary — the complete landing pipeline.
check-cache-bugs
Audit Claude Code setup for cache bugs (CC#40524) — sentinel, --resume/--continue, attribution header + ArkNill B3/B4/B5.
scaffold
Interactive coach that asks 4-5 questions to determine whether you need an agent, command, skill, hook, or rule — then generates a ready-to-use template. Usage: /scaffold (no arguments — starts the coaching session).
investigate
Systematic root-cause debugging — find the cause before writing any fix.
sonarqube
Analyze SonarCloud quality issues for a specific PR.