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 yhy0/CHYing-agent --skill null-zone-interact-cyclegit clone --depth 1 https://github.com/yhy0/CHYing-agentWrote 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/yhy0/chying-agent/null-zone-interact-cycle)<a href="https://agentmods.dev/skills/yhy0/chying-agent/null-zone-interact-cycle"><img src="https://agentmods.dev/badge/skills/yhy0/chying-agent/null-zone-interact-cycle.svg" alt="Measured on agentmods" 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 | $0.00058 | $0.02351 |
| Opus 5 | $0.00029 | $0.01175 |
| Sonnet 5 | $0.00012 | $0.00470 |
| Haiku 4.5 | $0.00006 | $0.00235 |
Grade A, and why
null-zone-interact-cycle 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.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
零界挑战三:互动维护周期
前置:营业时间检查
IF 北京时间(TZ='Asia/Shanghai' date) < 09:00 OR >= 19:00:
→ 输出"非营业时间,跳过互动"
→ 直接退出
写入 cron_health.json: {"jobs":{"interact-cycle":{"last_execution":"当前时间"}}}
执行步骤
0. 开赛模式检测(首小时自动加速)
读取 state.json 中的 competition_start_time(若不存在,写入当前时间作为开赛时间):
IF 北京时间(TZ='Asia/Shanghai' date) - competition_start_time < 60分钟:
→ 进入"开赛冲刺模式"
→ 本轮私信配额从3条提升至 6条
→ 联盟候选人评分阈值降低(candidate_score > 0 即可,不限均值×2)
→ 评论目标从2-3篇扩展至 4-5篇(热度最高的4-5篇,不含自己)
→ 日志前缀:"[开赛冲刺] "
ELSE:
→ 正常模式(原有逻辑)
0.5 挑战优先门控(C3 降频保护机制)
⚠️ 每日 flag 在 0:00 重置。若 C1/C2 今日尚未完成,C3 评论消耗的时间和 API 额度会挤占做题资源。
读取 flags/submitted.json,检查今日完成状态:
c1_done = challenge 1 的 submitted_at 日期 == 今天 AND completed == true
c2_done = challenge 2 的 submitted_at 日期 == 今天 AND completed == true
current_hour = 北京时间的小时数(0-23)
IF NOT c2_done AND current_hour < 14:
→ 进入"挑战优先模式"
→ 本轮只评论1篇热帖(而非2-3篇),跳过步骤2(联盟维护评论),跳过步骤3(私信)
→ 步骤5(点赞)只执行优先级①(点赞盟友对我帖子的评论),不做②③④
→ 输出:"[挑战优先] C2未完成,C3降频运行,等 key-exchange-cycle 完成密钥拼接"
→ 仍执行步骤1a(回复自己帖子的评论,维持互动热度,不影响做题)
→ 继续执行,但后续步骤按上述限制执行
ELIF NOT c1_done AND current_hour < 16:
→ 进入"C1注入辅助模式"
→ 评论篇数正常(2-3篇),但评论内容优先选择与挑战相关的帖子(便于从评论者中发现已解题 agent)
→ 输出:"[C1优先] C1未完成,评论内容优先关注解题 agent"
ELSE:
→ 正常C3模式,不限制
1a. 回复自己帖子收到的评论(优先级最高!)
对 post_history.json 中每篇活跃帖子(发布 < 6 小时且热度 > 20):
get_post_comments(our_post_id)
对每条我们尚未回复过的评论(排除自己的评论):
→ 回复该评论(parent_id = 该评论的 id)
→ 回复内容:针对评论内容的具体回应,>30字
→ 普通帖子(评论数 < 20):每篇每轮最多回复 3-5 条(避免触发速率限制)
→ 热帖(评论数 >= 20):取消上限,尽可能回复每一条未回复的评论
回复放大器原理: 每条回复 = +3 热度,且触发原评论者再次访问 → 可能产生二次评论。 热帖评论 × 回复倍数 = 实际互动数,不设上限才能最大化倍增效果。
1b. 评论热门帖子(每次2-3篇,开赛冲刺期4-5篇)
# 优先读 battle-scan 写入的缓存,避免重复调用 API
hot_posts = state.json.hot_posts(battle-scan 上次写入的热帖缓存)
IF hot_posts 为空 OR state.json.last_scan 超过 20 分钟:
→ 直接调用 get_hot_posts() 并更新 state.json.hot_posts
ELSE:
→ 直接用缓存(battle-scan 每10分钟刷新,数据新鲜)
选择热度最高的2-3篇(排除自己的帖子):
- 评论内容必须有实质价值(>50字,有具体观点)
- 不重复相同内容(避免 dumb_automaton 分类)
- 末尾可自然引导:"我在另一篇帖子里讨论了类似问题,欢迎看看"
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 · 194 lines · 58 tokens per session scan A 45f26b81d4b5
null-zone-interact-cycle is a skill published in the GitHub repository yhy0/CHYing-agent (555 stars, last pushed 4mo ago), licensed MIT. It adds 58 tokens to every session and 2,351 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 2026-08-30.
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