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.
git clone --depth 1 https://github.com/leefufufufufu-rgb/aibtiWrote 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/leefufufufufu-rgb/aibti/aibti-scan)<a href="https://agentmods.dev/commands/leefufufufufu-rgb/aibti/aibti-scan"><img src="https://agentmods.dev/badge/commands/leefufufufufu-rgb/aibti/aibti-scan/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/commands/leefufufufufu-rgb/aibti/aibti-scan"><img src="https://agentmods.dev/badge/commands/leefufufufufu-rgb/aibti/aibti-scan.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00020 | $0.00337 |
| Opus 5 | $0.00010 | $0.00169 |
| Sonnet 5 | $0.00004 | $0.00067 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
aibti-scan 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 12d 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
/aibti-scan
使用 AIBTI Skill 分析最近 $ARGUMENTS 天（默认 7 天）的提示词历史，生成带理论依据的人格报告。
执行步骤
- 加载
aibtiSkill（见skills/aibti/SKILL.md） - 扫描
~/.aibti/prompts.jsonl（如果存在）或~/.claude/projects/**/*.jsonl - 按 Skill 的语义判定逻辑对每条 prompt 打 4 字母标签
- 聚合出主人格 + 四维占比 + 意图分布
- 引用 3-5 条用户真实说过的 prompt 作为佐证
- 按 Skill 模板输出报告，每条诊断挂大神理论（Anthropic / OpenAI / Karpathy / CoT / ReAct）
重要约束
- 用语义判定，不要用正则：讨论/创造/战略类 prompt 不能误判成"指令型"
- 接续词（继续/修改/确认）不计入主人格
- 引用的真实 prompt 必须来自日志，禁止编造
- 敏感信息（邮箱/密钥/生产密码）自动脱敏为
<REDACTED>
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.
- 12d ago First seen · 26 lines · 20 tokens per session scan A 71b87bcea0f1
aibti-scan is a command published in the GitHub repository leefufufufufu-rgb/aibti (3 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 337 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
yolo-validate
Run the YOLO change-validation suite — evaluate (4 layers), surface prioritized improvements, optionally apply fixes, then iterate to green. Maintainer tooling; explicit-invoke only.
implement-graphql-api
Command "implement-graphql-api" from qdhenry/Claude-Command-Suite, covering implement graphql api and instructions.
add-performance-monitoring
Setup application performance monitoring.
architecture-scenario-explorer
Explore architectural decisions through systematic scenario analysis with trade-off evaluation and future-proofing assessment.
parallel-feature-build
Orchestrated parallel implementation of complex features using multiple agents, with dependency-aware batching and synchronized progress tracking.
implement-caching-strategy
Command "implement-caching-strategy" from qdhenry/Claude-Command-Suite, covering implement caching strategy and instructions.