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Agent Skill · 待分类

spider-strategy(蜘蛛策略)

spider-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。

spider-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,权限为需要联网;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 11,157。

总安装 39周安装 12GitHub Stars 75下载 97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:spider-strategy(蜘蛛策略)
来源仓库:https://github.com/senpi-ai/senpi-skills
仓库路径:skills/spider-strategy
安装命令:
npx skills add https://github.com/senpi-ai/senpi-skills --skill spider-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 spider-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。

用于处理 GitHub 仓库、Issue 和 Pull Request 信息。适合围绕仓库状态、代码变更或协作事项进行整理。可结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态。spider-strategy 属于待分类类 Skill,可作为该场景下的辅助能力补充。

注意是否会触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/spider-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 spider-strategy;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

    它需要需要联网,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

spider-strategy 怎么安装?

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

spider-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。

spider-strategy 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

spider-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。

spider-strategy 用的人多吗?

公开数据是累计安装 39 次,近一周安装 12 次,下载 97 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。

spider-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

  • 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

  • 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
spider-strategy

蜘蛛策略 · 本页

3975需要联网未明确有提醒

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

🕷️ SPIDER v2.0 — Agentic Portfolio Operator

The first Senpi agent that operates above the position level.

Every other Predator decides "open or close this trade?" every few minutes. Spider builds a web — one anchor point, several radial threads — then waits. The web does the work. Vibrations come to it.

What changed in v2.0

v1.0 was an Elite-convergence single-position scanner. It contributed to the same fleet pathology that v2.0 is designed to correct: too many fills, weak signals, repeated DSL stops. v2.0 is a structural rewrite from a different thesis entirely.

The shape of the strategy

Spider runs a two-leg portfolio:

  1. Anchor — a single high-conviction long (e.g. HYPE 3x), held minimum 7 days
  2. Basket — 3–5 shorts on the highest-funding alt-coins, risk-parity sized to 40% of anchor notional

The basket isn't a directional hedge — it's a funding harvest. Shorts on coins paying the most positive funding to longs collect rate while providing partial downside cushion to the anchor.

Net portfolio: long-biased, partially hedged, funding-positive. Designed to throw off carry whether the anchor moves up modestly, sideways, or even slightly down.

Why this shape sidesteps three fleet diseases

DiseaseWhy Spider is immune
Fee disease~10–20 fills/week vs. hundreds. Fees become a rounding error.
Chop disease7-day horizons don't care about 4h chop.
Trailing-stop mismatchNo per-leg trailing stop — basket *is* the position.

What Spider captures that no other Predator does: funding harvest. Over 7-day holds, funding rates dwarf intraday price noise on most alts.

v2.0 fleet-learning constraints

The Apr 2026 fleet hypothesis was concrete:

"Bleeders are over-trading — relying on weak signals and hitting DSL repeatedly. The way to win big is high conviction + longer hold times. Most bleeders have positive gross edge eaten by fees."

Cheetah: $1,317 gross, $1,600 fees, $283 net loss. Roach: $1,720 gross, ~$2,000 fees, $280 net loss. Same pattern across 6+ agents. Spider v2.0 bakes the fix into the runtime:

1. Trade frequency ceiling

Max 12 fills per rolling 7d window. Soft warning at 8. Beyond 8 the agent must justify every additional fill in its rationale. Beyond 12 is technically not blocked but every fill demands written justification — behavioral constraint.

2. Basket member strictness gate

Basket members must individually score ≥ 6.5 to qualify. If fewer than 3 candidates clear the floor, Spider runs anchor-only mode — no basket that week. Better to hold a pure anchor than pad with weak-signal shorts.

3. Fee health monitoring

Every weekly ATTRIBUTION record carries gross_pnl, fees_paid, funding_harvested, and fee_pct_of_gross as the primary health metric. If rolling 4-week fee_pct_of_gross exceeds 25%, a FEE_CEILING_WARNING event fires and the agent must address it in next week's rationale.

4. Forced-exit tracking

Every leg close writes an EXIT event with a typed exit_reason:

CategoryReasons
Agent-controlled (good)agent_chosen, thesis_break, funding_flip, weekly_review, min_hold_expiry
Forced (bad)safety_stop, circuit_breaker

If more than 1 safety_stop fires in any rolling 30d window, a SAFETY_STOP_WARNING event fires — the agent has lost control of position sizing or is ignoring score collapses until the hard stop bails it out (the bleeder signature).

The daily decision loop

Each cycle (13:00 UTC, skipping Thursday morning UTC because Arena resets) Spider:

  1. Reads four scanner outputs:

- anchor_candidates — top 5 from top-15-volume universe, scored on Arena leader exposure (40%) + SM consensus delta (30%) + funding favorability (15%) + 30d relative strength (15%) - basket_candidates — top 10 from top-50-volume universe, scored on funding descending (40%) + SM rotation negative (25%) + relative weakness vs anchor (20%) + squeeze risk inverse (15%) - portfolio_state — current legs, days held, P&L, funding accrued, fees_paid_per_leg, fills_in_rolling_7d - fleet_overlap — what other Predators are positioned in (concentration awareness, never a hard veto)

  1. Outputs one of five decisions with a reported fill_budget_remaining_7d:
DecisionWhen
HOLDPortfolio still scoring well, no member's thesis broken
SWAP_BASKET_MEMBEROne basket member's score collapsed (funding flipped, squeeze risk, SM rotation)
RESIZEDrawdown trigger or thesis strengthening
FULL_REBALANCEWeekly review or regime shift detected
GO_TO_CASHNo high-conviction anchor available, or circuit-breaker fired
  1. Writes a DECISION record to state/spider-log.jsonl with full rationale + counterfactuals (alternatives considered + why rejected).

GO_TO_CASH is a first-class output. Spider is allowed — and expected — to refuse to trade when conviction is insufficient.

Cold start — the most important section

Spider does not trade for the first 7 days of its life.

Phase A — Warmup (days 1–7)

Spider boots in GO_TO_CASH. Runs the full decision loop daily. Produces zero trades. Every day it logs to spider-log.jsonl: *"I would have picked anchor=X, basket=[A,B,C,D], conviction=7.2, because..."*

The operator reviews these records before any capital moves. The agent earns trust through observable judgment, not through marketing.

Phase B — First strike

Strike condition (all three required):

  1. Warmup complete (7 days observed)
  2. Top anchor candidate scores ≥ 7.0 for 2 consecutive daily scans (persistence — never a single-day spike)
  3. No catastrophic regime signal (BTC down >10% in 48h, funding regime broadly flipping, vol-regime expansion spike)

When strike fires, Spider enters in three staggered steps:

DayAnchor sizeBasket sizeCondition
150%0%Strike fires
275%25%Thesis confirming (anchor up or flat, score still ≥ 7.0)
3100%40%Thesis confirming

If at any pilot day the anchor drawdown exceeds 5% or the score collapses below 6.5 → abort: close everything, return to GO_TO_CASH, reset warmup to 3 days, retry.

The first trade Spider ever makes has the worst signal-to-noise ratio of any decision it will ever make. Pilot sizing is the agent encoding epistemic humility into the strategy.

Phase C — Steady state

Day 4 onward. Normal daily decision loop with full positions and real P&L feedback.

Signal source philosophy

Three populations, three uses:

PopulationUse
Arena leaders (7d ROE, top 10)Primary positive signal for anchor selection
Smart Money (broader HL leaderboard)Foundation signal for both anchor and basket
Predators fleet (incl. Spider itself, self-excluded)Concentration awareness — leverage modifier only, never a hard veto

The Predators read is a portfolio-risk discipline, not a quality judgment. Same check a real fund runs: *"before I add HYPE long, what's the firm's existing HYPE exposure across all books?"* Concentration only modifies sizing — it never blocks a trade.

The rationale log

Every scanner output, decision, pilot ramp, exit, abort, weekly attribution, and fleet-learning warning writes a typed JSONL record to state/spider-log.jsonl. The log:

  • Survives session clears (lives on disk, not in LLM context)
  • Is append-only (no record is ever mutated; attributions reference prior decisions by id)
  • Captures counterfactuals on every decision (alternatives + score gap + why rejected)
  • Calibrates the conviction threshold during warmup (top score percentiles)
  • Surfaces fee, fill, and forced-exit pathologies via dedicated WARNING events
  • Powers weekly retrospective scoring (ATTRIBUTION event with gross/fees/funding/net + benchmark comparison)

See references/rationale-log-schema.md for the full event taxonomy.

What's not yet built (and that's the point)

Spider v2.0 is the agent layer. Two runtime additions are needed for it to operate at full quality:

  1. dsl_portfolio exit engine — portfolio-level exits with gross_exposure_cap, net_delta_target, weekly_drawdown_circuit. Until this ships, Spider enforces these in the LLM action layer (fragile but functional).
  2. Data-layer primitives — funding regime context, vol regime classifier, fleet overlap aggregator, trade journal API. Spider is the first agent designed *for* these abstractions; without them it has to compute equivalents on the fly.

Spider will get strictly better as both ship. No code change required.

Install

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

gh repo clone Senpi-ai/senpi-skills /tmp/senpi-skills
cp -r /tmp/senpi-skills/spider/* /data/workspace/skills/spider-strategy/

cp /data/workspace/skills/spider-strategy/config/spider-config.example.json \
   /data/workspace/skills/spider-strategy/config/spider-config.json

openclaw runtime start spider-tracker

Operator checklist for the first 7 days

  • Day 1: confirm spider-log.jsonl is being written (one SCAN + one DECISION per cycle)
  • Day 3: read the rationale text on each DECISION record. Does the agent's reasoning match what you'd want?
  • Day 5: check warmup_status() — how many qualifying strike windows has the agent observed?
  • Day 7: review WARMUP_END summary. Are the threshold-calibration percentiles in a sane range? If top score never exceeded 6.5, either the threshold is wrong or the regime is bad — both useful facts.
  • After day 7: Spider can strike when the next qualifying signal appears. Could be day 8, could be day 30. Patience is the strategy.

Benchmark

Spider only justifies its complexity if it beats *"buy and hold the anchor it picks"* on a risk-adjusted basis. If it doesn't, the basket is noise and we kill it. Track this benchmark explicitly in weekly ATTRIBUTION records (benchmark_buy_and_hold_pnl field).

Secondary success criteria — Spider should also:

  • Stay under 12 fills per rolling 7d window (no FILL_CAP_WARNING events)
  • Keep fee_pct_of_gross under 25% on rolling 4w (no FEE_CEILING_WARNING events)
  • Fire safety_stop no more than once per 30d (no SAFETY_STOP_WARNING events)

If all three guardrails hold, Spider is operating in the Vulture / Kodiak quadrant: low turnover, strict gates, asymmetric payoffs. If any breach, Spider is drifting toward the Cheetah / Roach quadrant: bleeding fees through positive gross edge.

平台分布

Codex

34.86%
按下载量换算34

Claude

29.07%
按下载量换算28

Cursor

18.95%
按下载量换算18

Gemini CLI

9.13%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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