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wolverine-strategy(狼獾策略)

wolverine-strategy 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

wolverine-strategy 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在待分类类的 11,209 个 Skill 里排第 4,425。

总安装 745周安装 32GitHub Stars 75下载 261核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:wolverine-strategy(狼獾策略)
来源仓库:https://github.com/senpi-ai/senpi-skills
仓库路径:skills/wolverine-strategy
安装命令:
npx skills add https://github.com/senpi-ai/senpi-skills --skill wolverine-strategy
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/senpi-ai/senpi-skills --skill wolverine-strategy

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

wolverine-strategy 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。它可结合来源仓库、安装命令和原始 README 继续核验具体用法。

安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

安装命令:npx skills add https://github.com/senpi-ai/senpi-skills --skill wolverine-strategy 来源仓库:https://github.com/senpi-ai/senpi-skills

来源仓库内路径:skills/wolverine-strategy

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/senpi-ai/senpi-skills --skill wolverine-strate…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

wolverine-strategy 怎么安装?

在终端执行 npx skills add https://github.com/senpi-ai/senpi-skills --skill wolverine-strategy。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

wolverine-strategy 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

wolverine-strategy 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

wolverine-strategy 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

wolverine-strategy 用的人多吗?

公开数据是累计安装 745 次,近一周安装 32 次,下载 261 次,GitHub 75 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

wolverine-strategy 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
wolverine-strategy

狼獾策略 · 本页

74575接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

🦡 WOLVERINE v2.3 — HYPE Alpha Hunter

Single asset. One thesis. Smart money commits, Wolverine pounces, DSL trails the trend.

What it does

Wolverine scans HYPE on Hyperliquid every 3 minutes for a confluence of momentum signals:

  • Smart Money consensus — pct of top traders gain ≥ minimum, trader count above threshold
  • Velocity acceleration — 15m and 1h contribution change positive AND building (15m > 1h)
  • Price confirmation — 4H and 1H both moving in the signal direction
  • Volume confirmation — bonus when volume spikes above 6h average
  • 15m freshness gate — penalizes stale signals (15m velocity < 0)

When the score reaches 8+, Wolverine fires a HYPE entry at conviction-scaled leverage (7x or 10x) with FEE_OPTIMIZED_LIMIT order type. Position management is delegated entirely to the DSL exit engine — Wolverine never closes a position itself, only opens.

v2.3 — chop-hardened

On 2026-04-14 Wolverine v2.2 took 5 consecutive losing HYPE trades over ~3 hours of chop for -$113 total. Every entry was a "fresh signal" by v2.2's criteria, but the scanner had no concept of "we just lost N times on this same coin." v2.3 adds three protections:

1. Chop-detection lockout

After 2 losses on HYPE within 3 hours, Wolverine refuses any new entry on HYPE for 6 hours from the last loss. The scanner emits CHOP_LOCKED and waits out the chop. Constants:

  • CHOP_WINDOW_HOURS = 3
  • CHOP_MAX_LOSSES = 2
  • CHOP_LOCKOUT_HOURS = 6

2. Direction-flip hard gate

If the new signal is OPPOSITE direction to the most recent trade on HYPE within 2 hours AND that trade was a loss, Wolverine hard-gates the new entry. Catches the LONG → SHORT → LONG whipsaw pattern even before the chop-lockout threshold fires. Winning flips are still allowed (legit reversal catch); losing flips are blocked (chop chasing).

3. Persistent entry log

Every ENTRY, EXIT, CHOP_LOCKOUT, and FLIP_BLOCKED event writes a JSON line to state/entry-log.jsonl. This log survives openclaw sessions clear --current — the data lives on disk, not in LLM context. When you ask "what scored that trade?" after a session reset, you can answer by tailing the log:

tail -20 /data/workspace/skills/wolverine-strategy/state/entry-log.jsonl | jq

4. Exit tracking hook

sync_closed_positions(wallet, state) runs at the start of every scan, compares current positions to the previous scan's snapshot, and appends EXIT events linked to prior ENTRY metadata. Closes the loop on chop detection so the scanner sees its own realized PnL per trade.

Fleet-standard guardrails (all present)

  • Self-executing via create_position (no external action layer needed)
  • Dynamic P&L-aware daily entry cap (PR #176)
  • has_resting_orders() auto-cancels stale maker orders >10 min (PR #177)
  • Stale-date bug fix in load_trade_counter() (PR #177)
  • Per-asset cooldown (180 min default)
  • Conviction-scaled leverage capped at 10x (fleet H12 audit)

Key settings

SettingValueWhy
AssetHYPESingle-asset focus, no parallel bets
Max positions1Concentration
Margin per trade50%High conviction commits high capital
Max leverage10xFleet cap (H12 audit found >10x destroys edge via fees)
Min score8Above this, conviction is real
Per-asset cooldown180 minPatience between trades
DSL hard timeout240 minHYPE winners often need 2-4 hours to develop
DSL Phase 1 max loss25% ROEStandard fleet floor
DSL Phase 2 tier 1+8% / 25% lockEarlier than fleet standard

⛔ Critical agent rules

  1. Install path is /data/workspace/skills/wolverine-strategy/
  2. THE SCANNER DOES NOT EXIT POSITIONS — DSL handles all exits
  3. MAX 1 POSITION — HYPE only
  4. Do not modify scanner constants without testing — fleet-wide thresholds were tuned by audit
  5. Do not rebase the cron to a faster cadence than 3 min — 3 min is the right cadence for HYPE momentum signals

Setup

Step 1 — Install path

The skill must live at /data/workspace/skills/wolverine-strategy/. The package contains:

wolverine/
├── README.md                     # User-facing summary
├── SKILL.md                      # This file (LLM-facing)
├── runtime.yaml                  # OpenClaw runtime config + DSL preset
├── config/
│   └── wolverine-config.json     # Wallet, strategy ID, chat ID
└── scripts/
    ├── wolverine-scanner.py      # Main scanner (v2.3)
    └── wolverine_config.py       # Helper module (atomic write, MCP, state I/O)

Pull from the senpi-skills GitHub repo:

mkdir -p /data/workspace/skills/wolverine-strategy/{config,scripts,state}

curl -s https://raw.githubusercontent.com/Senpi-ai/senpi-skills/main/wolverine/runtime.yaml \
  -o /data/workspace/skills/wolverine-strategy/runtime.yaml

curl -s https://raw.githubusercontent.com/Senpi-ai/senpi-skills/main/wolverine/SKILL.md \
  -o /data/workspace/skills/wolverine-strategy/SKILL.md

curl -s https://raw.githubusercontent.com/Senpi-ai/senpi-skills/main/wolverine/config/wolverine-config.json \
  -o /data/workspace/skills/wolverine-strategy/config/wolverine-config.json

curl -s https://raw.githubusercontent.com/Senpi-ai/senpi-skills/main/wolverine/scripts/wolverine-scanner.py \
  -o /data/workspace/skills/wolverine-strategy/scripts/wolverine-scanner.py

curl -s https://raw.githubusercontent.com/Senpi-ai/senpi-skills/main/wolverine/scripts/wolverine_config.py \
  -o /data/workspace/skills/wolverine-strategy/scripts/wolverine_config.py

Step 2 — Set wallet and chat ID

Set your strategy wallet address in runtime.yaml:

sed -i 's/${WALLET_ADDRESS}/<YOUR_STRATEGY_WALLET>/' /data/workspace/skills/wolverine-strategy/runtime.yaml
sed -i 's/${TELEGRAM_CHAT_ID}/<YOUR_TELEGRAM_CHAT_ID>/' /data/workspace/skills/wolverine-strategy/runtime.yaml

OR set them in config/wolverine-config.json directly (the scanner will fall back to the JSON values if env vars / runtime placeholders aren't set):

{
  "strategyId": "your-strategy-id",
  "wallet": "0xYourStrategyWallet",
  "chatId": "your-telegram-chat-id"
}

The scanner also supports environment variables: WOLVERINE_WALLET and WOLVERINE_STRATEGY_ID.

Step 3 — Install the runtime in OpenClaw

openclaw senpi runtime create --path /data/workspace/skills/wolverine-strategy/runtime.yaml
openclaw senpi runtime list && openclaw senpi status

Step 4 — Verify with a single manual scan

python3 /data/workspace/skills/wolverine-strategy/scripts/wolverine-scanner.py

Expected: clean exit, JSON output includes "_wolverine_version": "2.3". Most likely first run shows a heartbeat (no signal) — that's normal. Wolverine fires on HYPE momentum confluence, which is rare.

Step 5 — Run the scanner on a recurring schedule

The recommended pattern is a detached bash loop with zero LLM wake cost (matches Turbine pattern):

nohup bash -c 'while true; do python3 /data/workspace/skills/wolverine-strategy/scripts/wolverine-scanner.py >> /tmp/wolverine-loop.log 2>&1; sleep 180; done' > /tmp/wolverine-nohup.log 2>&1 &

# Verify the loop is running
ps aux | grep wolverine-scanner | grep -v grep
tail -5 /tmp/wolverine-loop.log

Alternative: configure an OpenClaw cron with sessionTarget: isolated. Avoid sessionTarget: main — that pattern was a $287/day bug on Sentinel and a $200/day timer-bomb on Scorpion before we caught it.

Operational notes

Tailing the entry log

After Wolverine takes its first trade, you can read the structured entry log to see exact scores, reasons, leverage, and outcomes:

tail -10 /data/workspace/skills/wolverine-strategy/state/entry-log.jsonl | jq

The log persists across session clears, so you can answer "what was the last entry score?" even after openclaw sessions clear --current.

Checking chop-lockout state

If Wolverine outputs CHOP_LOCKED notes, it has detected 2+ losses on HYPE within 3 hours and is sitting out for 6 hours from the last loss. To see when it unlocks:

grep CHOP_LOCKOUT /data/workspace/skills/wolverine-strategy/state/entry-log.jsonl | tail -1 | jq '.unlock_ts'

This is intentional behavior, not a bug. Wolverine is protecting capital during chop.

Verifying the resting-order auto-cancel

Wolverine cancels stale FEE_OPTIMIZED_LIMIT maker orders older than 10 minutes. If you see Auto-cancel attempted on stale order... in the log, that's the safety mechanism working.

Best for

  • Operators who want a single-asset HYPE momentum specialist
  • Trading environments where 2-4 trades per day is the right cadence (not high-frequency)
  • Accounts where one bad chop day could be expensive without circuit breakers
  • Operators who want the agent to be self-managing — no manual position closing required

Not for

  • Multi-asset diversification (use Phoenix, Condor, or Polar instead)
  • High-frequency scalping
  • Anyone who wants the scanner to make exit decisions (DSL handles all exits)
  • XYZ DEX equities (Wolverine is HYPE-only)

License

MIT — Copyright 2026 Senpi (https://senpi.ai).

平台分布

Codex

33.34%
按下载量换算87

Claude

30.95%
按下载量换算81

Cursor

20.1%
按下载量换算52

Gemini CLI

9.69%
按下载量换算25

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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