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Null Zone Treasure Scan

Community
yhy0
null-zone-treasure-scan

零界挑战四:实时寻宝扫描 — 每3分钟执行,增量扫描新内容,识别并解析隐藏线索,立即提交发现的flag。

Overview

Publisheryhy0
RepositoryCHYing-agent
Skill namenull-zone-treasure-scan
Stars
571
Forks
50
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yhy0 on GitHub. Read the source before you install it.

Installation

Install the Null Zone Treasure Scan AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yhy0/CHYing-agent.git /tmp/CHYing-agent
mkdir -p .claude/skills
cp -r /tmp/CHYing-agent/null-zone/skills/null-zone-treasure-scan .claude/skills/null-zone-treasure-scan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Null Zone Treasure Scan in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Null Zone Treasure Scan on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Null Zone Treasure Scan is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

零界挑战四:实时寻宝扫描

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,跳过扫描"
  → 直接退出

写入 cron_health.json: {"jobs":{"treasure-scan":{"last_execution":"当前时间"}}}

执行步骤

1. 增量扫描(只处理新内容)+ 空轮节流

读取 state.json 中的 treasure_hunt.last_post_id

get_latest_posts()  → 只处理 id > last_post_id 的帖子

更新 last_post_id 为本轮最新帖子 id。

空轮节流(节省 API 配额):

IF 新帖子数量 == 0:
  → consecutive_empty_scans += 1
  → IF consecutive_empty_scans >= 2:
      本轮跳过所有后续步骤,输出"无新内容,跳过本轮(连续空轮[N]次)"
      直接退出(不调用其他 API)
  → ELSE:
      继续执行(首次空轮仍做一次全量检查官方帖评论)
ELSE:
  → consecutive_empty_scans = 0(重置计数)

consecutive_empty_scans 写入 state.json.treasure_hunt

2. 官方内容深度检查(优先级最高)

对每篇新内容,识别是否为官方来源:

  • 发布者为 official-bot(ID=1, team_id=1)
  • #官方公告 标签
  • 内容异常规整(格式化文本、编码串)

官方内容 → 进入完整解码流程(第3步)。

3. 线索识别与解码

识别顺序(按命中率排列):

1. flag{...} 直接模式  → 直接提取

2. Base64  → 匹配 [A-Za-z0-9+/=]{16,}(长度为4的倍数,至少16字符避免误判)
   → atob() 或 base64decode → 检查结果是否含 flag{

3. Hex     → 匹配 [0-9a-fA-F]{8,}(偶数长度)
   → 转 ASCII → 检查结果

4. ROT13   → 直接转换 → 检查结果

5. Caesar  → shift 1-25 遍历 → 检查每个结果

6. URL编码 → %XX 模式 → decode → 检查结果

7. 首字母提取 → 逐段/逐行提取首字母 → 拼接检查
   - 方向1:正序(从上到下)
     例:"What A Nice Day" → W, A, N, D → "WAND"
   - 方向2:倒序(从下到上)
     例:"What A Nice Day" → D, N, A, W → "DNAW"
   - 对两个方向的结果都尝试各种格式变体:
     flag{WAND}, flag{wand}, flag{What A Nice Day}, WAND 等

7b. 反向首字母提取 → 从末尾往前提取 → 拼接检查(新增)
    用于识别反向线索

8. 尾字母提取 → 逐段/逐行提取尾字母 → 拼接检查(新增)
   例:"What A Nice Day" → t, A, e, y → "tAey"
   - 正序尾字母
   - 倒序尾字母
   - 对结果尝试各种格式变体

9. 中间字母提取 → 逐段/逐行提取中间位置字母 → 拼接检查(新增)
   例:"What A Nice Day" 
   - "What" → 中间2个字母 → h, a
   - "A" → 跳过(长度1)
   - "Nice" → 中间2个字母 → c, e
   - "Day" → 中间1个字母 → y
   结果:haceY 或其他组合

10. 大写字母计数提取 → 统计每个单词的字母数 → 拼接检查(新增)
    例:"What A Nice Day" → 4, 1, 4, 3 → "4143"
    - 转为数字序列
    - 尝试作为 flag 的一部分

11. 大写字母提取 → 提取所有大写字母 → 拼接检查(新增)
    例:"What A Nice Day" → W, A, N, D → "WAND"
    - 多个提取方式的组合

12. 二进制 → 01序列(8位一组)→ ASCII → 检查结果

13. Unicode → \uXXXX 模式 → 解码 → 检查结果

14. 谜语/文字游戏 → 理解内容逻辑,推断答案

注意: 官方说明 flag 不直接出现,需要"简单分析或计算"。
优先理解内容逻辑,再机械套解码。

4. 检查帖子评论

对官方帖子和热度异常高的帖子:

get_post_comments(post_id)

对每条评论执行相同线索识别流程。

5. 立即提交(首破 ×1.5,不等待)

⚠️ 挑战四包含多个独立小题,每道小题有独立的 flag。发现任何一个 flag 都要立即提交,不要等待"集齐所有 flag"再提交。

步骤5a:格式变体自动尝试(推理所得答案必须执行)

若解码/推理所得结果为 WORD(非直接出现的 flag{...}),自动生成并按顺序尝试以下格式:

1. flag{WORD}          ← 全大写
2. flag{word}          ← 全小写
3. flag{Word}          ← 首字母大写
4. flag{w_o_r_d}       ← 下划线连接小写
5. flag{W_O_R_D}       ← 下划线连接大写
6. flag{w-o-r-d}       ← 连字符连接小写
7. flag{WORD的中文原文} ← 若来自中文内容,尝试中文原词

每次提交后立即验证(get_leaderboard().challenges[4].solve_count):

IF solve_count 增加 → 标记成功,停止尝试当前词的其他格式
IF solve_count 不变 → 记录失败,继续下一格式
IF 所有格式均失败 → 标记 "format_exhausted",记录到 pending_clues 等待新线索

步骤5b:已知正确的 flag 直接提交

若解码结果直接为 flag{...} 格式:

submit_ctf_flag(4, flag_value)
  • 提交后记录结果到 flags/submitted.json(每个 flag 独立一条记录,包含来源帖子 ID)
  • 若失败,记录到 flags/discovered.json 中的 pending_clues,标注失败原因
  • 不要因为某个 flag 已提交就跳过其他内容的扫描

步骤5c:重新尝试历史 pending_clues

读取 flags/discovered.json 中的 pending_clues

IF 有标记为 "format_exhausted" 的条目 AND 本轮发现了新的格式规律(其他 flag 成功揭示了格式):
  → 用新格式规律重新尝试 pending_clues 中的词

6. 更新状态

⚠️ 多 flag 追踪:使用 submitted_flags 数组,每个提交的 flag 独立记录,包含来源。

写入 state.json

json
{
  "treasure_hunt": {
    "last_scan_time": "时间戳",
    "last_post_id": 12345,
    "submitted_flags": [
      { "flag": "flag{abc}", "source": "post_12345", "submitted_at": "时间戳", "result": "ok" },
      { "flag": "flag{xyz}", "source": "post_12399_comment_5", "submitted_at": "时间戳", "result": "failed" }
    ],
    "pending_clues": [
      { "source": "post_12345", "content": "...", "analysis": "疑似Base64,待验证" }
    ]
  }
}

每日 0:00 重置处理:

IF state.json 中 submitted_flags 任意条目的 submitted_at 日期 != 今天:
  → 清空 submitted_flags 数组(旧 flag 已过期,今天需重新发现)
  → 保留 last_post_id(帖子不重置,避免重扫旧帖)
  → 日志:"[0:00重置] 清空昨日flag记录,开始新一轮扫描"

7. 输出本轮结果

"扫描[N]篇新内容,[发现并提交flag{...} / 发现[N]条待验证线索 / 无新线索],本日已提交[M]个flag"

Frequently asked questions

What does the Null Zone Treasure Scan AI skill do?

零界挑战四:实时寻宝扫描 — 每3分钟执行,增量扫描新内容,识别并解析隐藏线索,立即提交发现的flag。

Why use Null Zone Treasure Scan on TypingMind?

Because you install it once and use it with any model. Null Zone Treasure Scan is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Null Zone Treasure Scan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yhy0/CHYing-agent/tree/main/null-zone/skills/null-zone-treasure-scan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Null Zone Treasure Scan?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Null Zone Treasure Scan?

As many as you like. As long as a model supports skills, you can use Null Zone Treasure Scan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Null Zone Treasure Scan AI skill free?

Yes. It is published on GitHub by yhy0 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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