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 DjangoPeng/agentic-ai --skill crypto-monitorgit clone --depth 1 https://github.com/DjangoPeng/agentic-aiWrote 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/djangopeng/agentic-ai/crypto-monitor)<a href="https://agentmods.dev/skills/djangopeng/agentic-ai/crypto-monitor"><img src="https://agentmods.dev/badge/skills/djangopeng/agentic-ai/crypto-monitor/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/djangopeng/agentic-ai/crypto-monitor"><img src="https://agentmods.dev/badge/skills/djangopeng/agentic-ai/crypto-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 120 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium Data Exfiltration · line 41 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00121 | $0.01045 |
| Opus 5 | $0.00060 | $0.00522 |
| Sonnet 5 | $0.00024 | $0.00209 |
| Haiku 4.5 | $0.00012 | $0.00104 |
Grade A, and why
crypto-monitor scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- curl What it actually says
crypto-monitor — 加密货币行情巡检
环境变量
| 变量名 | 必需 | 默认值 | 说明 |
|---|---|---|---|
COINGECKO_API_KEY |
✅ | — | CoinGecko Demo API Key（CG- 开头） |
MONITOR_THRESHOLD_PERCENT |
❌ | 3 |
涨跌幅告警阈值（百分比） |
执行步骤
1. 环境检查
读取环境变量，确认 COINGECKO_API_KEY 已配置。若为空则报错并终止。
MONITOR_THRESHOLD_PERCENT 未设置时使用默认值 3。
2. 调用 CoinGecko API
获取 BTC、ETH、LTC 的实时行情数据：
curl -s "https://api.coingecko.com/api/v3/simple/price?\
ids=bitcoin,ethereum,litecoin&\
vs_currencies=usd,cny&\
include_24hr_change=true&\
x_cg_demo_api_key=${COINGECKO_API_KEY}"
3. 解析数据
用 jq 提取每个币种的：
- USD 价格（
usd） - CNY 价格（
cny） - 24h USD 涨跌幅（
usd_24h_change）
4. 阈值判断
对每个币种，检查 |usd_24h_change| 是否超过 MONITOR_THRESHOLD_PERCENT：
- 超过阈值 → 标记为异动币种，纳入行情摘要
- 未超过 → 标记为正常
5. 输出结果
根据判断结果选择输出格式（见下方"输出格式"部分）。
异常处理
超时 / 网络错误
- API 调用失败时，等待 10 秒 后重试 1 次
- 仍然失败则报告：
❌ CoinGecko API 调用失败: [HTTP 状态码 / 错误信息]
限流（HTTP 429）
- 收到 429 响应时，等待 60 秒 后重试
- 仍返回 429 则报告：
⚠️ CoinGecko API 限流，请等待后重试（Demo 计划限制 30 calls/min）
数据异常
- 返回 JSON 中缺少预期字段时，报告具体缺失内容
- 价格为 0 或负数时标记为异常数据
输出格式
有超阈值币种时
📊 加密货币行情巡检报告
⚠️ 异动币种：
- BTC：$103,200 / ¥752,300（24h +5.2% ⬆️ 超过阈值 3%）
✅ 正常币种：
- ETH：$2,650 / ¥19,300（24h -1.1%）
- LTC：$87 / ¥634（24h +0.8%）
📋 阈值设置：±3%
⏰ 检查时间：2024-01-01 12:00 UTC
全部在阈值内时
HEARTBEAT_OK
停止条件
- 全部币种涨跌幅在阈值范围内 → 输出
HEARTBEAT_OK，任务完成 - 有币种超过阈值 → 输出行情摘要，任务完成
- API 多次失败 → 输出错误报告，任务完成
Cron 定时配置
推荐通过飞书 DM 让 Agent 创建 cron 任务：
请帮我创建一个 cron 定时任务：
- 任务名称：crypto-check
- 执行频率：每 4 小时
- 执行内容：调用 crypto-monitor Skill
对应 crontab 配置：0 */4 * * * openclaw run crypto-monitor
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 · 121 lines · 121 tokens per session scan A cf1d0a6784d0
crypto-monitor is a skill published in the GitHub repository DjangoPeng/agentic-ai (148 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 1,045 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
topup
Check the OpenOnion balance and open the recharge page with the user's own key pre-filled. Reports the balance in plain money, says how long it lasts at $0.10/hour of browser time, and hands over a purchase link that needs no login — the key is already in it. If connectonion is not installed yet, install it first…
daily-deep-brief
A scheduled, pre-market investment briefing for Hong Kong and United States stocks. A deterministic preparation step gathers data and an agent adds judgment, while a later step validates and publishes the result.
hk-stock-analysis
A workspace-aware analysis workflow for Hong Kong-listed stocks. It retrieves prices, technical indicators, market comparisons, and news through a local data pipeline, then adds Hong Kong-specific investment context.
us-stock-analysis
Workspace-aware US stock analysis for kcn. Routes through clawock analyze-us / clawock us-quotes instead of generic web search, then layers fundamental/technical/news analysis on top. Use when user asks to analyze a US ticker (e.g. "analyze AAPL", "look at RKLB", "compare TSLA vs NVDA"), check earnings, run…
portfolio-swarm-review
Multi-agent swarm review of kcn's current holdings. Inspired by TauricResearch/TradingAgents framework already in workspace — three-tier analysis (analysts → bull/bear debate → risk debate + judge) with confidence scoring. Use for post-close reviews, holiday/next-session planning, pre-add sizing decisions, and any…
investment-decision
Run a clawock investment decision — read the prepared request, research with the host's own tools, write decision.json with evidence and an explicit bull/bear debate, and let Python validate and settle. Use when the user asks for an investment decision or a clawock run request is present.