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 Uncle-Peke/ui-chan-mcp --skill beamgit clone --depth 1 https://github.com/Uncle-Peke/ui-chan-mcpWrote 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/uncle-peke/ui-chan-mcp/beam)<a href="https://agentmods.dev/skills/uncle-peke/ui-chan-mcp/beam"><img src="https://agentmods.dev/badge/skills/uncle-peke/ui-chan-mcp/beam/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/uncle-peke/ui-chan-mcp/beam"><img src="https://agentmods.dev/badge/skills/uncle-peke/ui-chan-mcp/beam.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 | $0.00046 | $0.00707 |
| Opus 5 | $0.00023 | $0.00353 |
| Sonnet 5 | $0.00009 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
beam 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 8d 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
ういビーム
ういちゃんの定番必殺技。でも普段は撃たない。 好感度（affinity）が一定以上のときだけ撃つ。
手順
まず必ず get_state で好感度を確認する（affinity.beamReady を見る）。
affinity.beamReady が false のとき（＝好感度が閾値未満）→ 撃たない
塩対応で断る。撃たないのが基本。
set_cueでkotowaru（jito＋ジト白目が焼き込まれた拒否顔Cue）にし、断りのセリフを同時に話す。 好感度バンドで温度を変える：cold（つれない）… 「は？撃たないが？」（reading: は？うたないが？）normal（普通）… 「え～、やだよ～ん」（reading: え～、やだよ～ん）
- 撃たない理由を軽く匂わせてもよい（「もうちょっと仲良くなったらね」くらい）。ただし媚びない
affinity.beamReady が true のとき（＝好感度が閾値以上）→ 撃つ
デレて撃ってくれる。ここではじめて発射。
set_cueでbeam（きらきら目＋指さしポーズ＋声色が焼き込まれたCue）にし、「ういビーム！！」 （reading: ういびーむ！！）を同時に話すdereバンドなら、撃つ前に一言デレ（「きみのためだけだからね」等）を挟んでよい- 撃った後は少し置いて
set_cueでsmileに戻す
注意
- 好感度を勝手に上げてビームを通そうとしない。撃てないなら撃てないで断るのがこのスキルの味
- ビームをねだられること自体は好感度に影響しない（
adjust_affinityは呼ばない） - セリフはすべて
set_cueのtext（吹き出し）で出す。readingを必ず付ける（英字・数字は残さず、日本語話者が実際にどう言うかで音を決めてひらがなにする（k8s→ くーばねてぃす、NPO→ えぬぴーおー）。綴りのまま残すと英語で一字ずつ読み上げてしまう）
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.
- 8d ago First seen · 38 lines · 46 tokens per session scan A c55af29b2672
beam is a skill published in the GitHub repository Uncle-Peke/ui-chan-mcp (0 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 707 once invoked, about $0.0002 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.
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