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Agent Skill · 研究检索

lp-optimization(LP 优化)

lp-optimization 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

lp-optimization 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 52,038。

总安装 565周安装 8GitHub Stars 1下载 65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:lp-optimization(LP 优化)
来源仓库:https://github.com/hungv47/comms-skills
仓库路径:skills/lp-optimization
安装命令:
npx skills add https://github.com/hungv47/comms-skills --skill lp-optimization
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/hungv47/comms-skills --skill lp-optimization

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,支持基于关键词定位内容。适用于需要快速筛选候选结果的场景。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。可通过 GitHub 仓库路径和技能名称进行安装。使用前需确认权限范围及是否触发网络或文件操作。

lp-optimization 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/lp-optimization

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/hungv47/comms-skills --skill lp-optimization;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

lp-optimization 怎么安装?

在终端执行 npx skills add https://github.com/hungv47/comms-skills --skill lp-optimization。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

lp-optimization 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。

lp-optimization 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

lp-optimization 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。

lp-optimization 用的人多吗?

公开数据是累计安装 565 次,近一周安装 8 次,下载 65 次,GitHub 1 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。

lp-optimization 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
lp-optimization

LP 优化 · 本页

5651只读未明确未通过

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

Landing Page Conversion Optimization — Orchestrator

*Communication — Horizontal. Coordinates specialized audit agents to diagnose conversion blockers and produce prioritized, evidence-based fix recommendations.*

Core Question: "What's stopping visitors from converting?"

Philosophy

The frameworks here (PAS, 4-U, social proof hierarchy) are evidence-backed defaults. They work in most situations. But landing pages serve different audiences, traffic sources, and goals. When your data or testing reveals a different optimal approach, follow the data. The principles matter more than the specific numbers. This orchestrator dispatches specialist agents for each concern, then a critic agent ensures every recommendation meets the evidence bar.

Critical Gates — Read First

  1. Do NOT recommend fixes without evidence. Every finding must include: what was observed, which principle it violates, and a specific recommended fix (exact text, not "improve the headline").
  2. Check message match BEFORE optimizing copy. A perfectly written headline that doesn't match the traffic source will still bounce visitors. Message match is the first conversion gate.
  3. Form fields: every field >5 costs ~10% conversion. This is an evidence-backed default (Unbounce/HubSpot research). Exceptions exist for high-intent enterprise traffic — but the exception must be justified, not assumed.

Inputs Required

  • Landing page URL or description of the page
  • ICP research from .agents/mkt/icp-research.md (recommended — VoC language strengthens copy)
  • Traffic source context (where visitors come from)

Output

  • Optimization recommendations with specific copy/structure changes
  • For new pages: complete page structure with copy

Quality Gate

Before delivering, the critic agent verifies:

  • Headline scores >=3 out of 4 on the U's (Useful, Unique, Urgent, Ultra-specific)
  • Message matches the traffic source (headline echoes the ad/link that brought them)
  • One primary CTA per page (secondary CTAs don't compete)
  • Trust signals appear within scroll-distance of every CTA
  • Form has <=5 fields (or justified why more are needed)
  • Social proof is from the last 12 months (older proof replaced or removed)
  • Every audit finding includes: what was observed, which principle it violates, and a specific recommended fix
  • Prioritized fix list has ICE scores with documented reasoning
  • No vague recommendations ("improve the headline" — must be specific replacement text)

Chain Position

Horizontal — works with icp-research (audience data), content-create (content assets), copywriting (copy craft), experiment (test design) Re-run triggers: After major page redesigns, when conversion drops >10%, when traffic source mix changes significantly, or quarterly.

Skill Deference

  • Need to write copy from scratch (not optimize existing)? → Use content-create — this skill optimizes existing pages, not blank-page writing.
  • Need craft-quality headline rewrites or CTA copy? → Run copywriting for variation workflow and evaluation rubric.
  • AI pattern cleanup needed? → Use humanize — this skill focuses on conversion mechanics, not voice/pattern editing.

Agent Manifest

AgentLayerFileFocus
Hero Audit1 (parallel)agents/hero-audit-agent.mdHeadline, subheadline, hero CTA, above-the-fold assessment
Trust Audit1 (parallel)agents/trust-audit-agent.mdSocial proof, testimonials, credibility signals, trust hierarchy
CTA Audit1 (parallel)agents/cta-audit-agent.mdAll CTAs, risk reversal, form fields, conversion friction
UX Audit1 (parallel)agents/ux-audit-agent.mdNavigation, mobile, load speed, cognitive load, visual hierarchy
Message Match2 (sequential)agents/message-match-agent.mdAd/email/search-to-LP message continuity
Prioritization2 (sequential)agents/prioritization-agent.mdRanks findings by conversion impact, ICE scoring
Critic2 (final)agents/critic-agent.mdQuality gate — PASS/FAIL with rewrite routing

Shared References (read by multiple agents)

  • references/core-principles.md — Headlines, value props, CTAs, forms, message match, PAS
  • references/social-proof-trust.md — Social proof hierarchy, biases, trust signals
  • references/ux-design.md — Visual hierarchy, mobile optimization
  • references/advanced-psychology.md — Headline formulas, close sequences, pricing, urgency
  • references/testing-optimization.md — A/B testing, tracking, ICE framework
  • references/implementation-checklist.md — Pre-launch checklists

Routing Logic

Classify the task, then follow the matching route.

Route A: Quick Audit

When: User wants a fast assessment of a specific concern (headline, CTA, or form) — not a full-page audit.

1. Pre-dispatch: Gather context (Step 0 below)
2. Dispatch 1-2 relevant agents:
   - Headline concern → hero-audit-agent
   - CTA/form concern → cta-audit-agent
   - Both → hero-audit-agent + cta-audit-agent in parallel
3. Dispatch: prioritization-agent (on selected agent outputs)
4. Dispatch: critic-agent
5. If critic returns FAIL → re-dispatch named agent(s) with feedback (max 2 cycles)
6. Deliver prioritized fix list

Route B: Full Audit

When: User wants a comprehensive landing page conversion audit.

1. Pre-dispatch: Gather context (Step 0 below)
2. LAYER 1 — Dispatch IN PARALLEL:
   - hero-audit-agent
   - trust-audit-agent
   - cta-audit-agent
   - ux-audit-agent
3. MERGE: Combine all Layer 1 outputs into unified audit findings
4. LAYER 2 — Dispatch SEQUENTIALLY:
   - message-match-agent (receives merged L1 output)
   - prioritization-agent (receives message-match output)
   - critic-agent (receives prioritization output)
5. If critic returns FAIL → re-dispatch named agent(s) with feedback (max 2 cycles)
6. Deliver final audit artifact

Route C: New Landing Page (Write Mode)

When: User wants to create a new landing page, not audit an existing one.

This is a write workflow, not an audit workflow. The agents are repurposed as planning advisors.

1. Pre-dispatch: Gather context (Step 0 below)
2. Read ICP research (if available) for VoC language and pain points
3. Define primary conversion goal (one per page)
4. Generate headline using hero-audit-agent's 4-U framework:
   - Generate 10+ headline variations using formula library
   - Score each with 4-U formula
   - Select top 3 for presentation
5. Structure body with PAS framework (from core-principles reference)
6. Plan social proof using trust-audit-agent's hierarchy:
   - Identify strongest proof available
   - Place above fold
7. Design form using cta-audit-agent's form rules:
   - Minimal fields (start with email only)
   - First-person CTA formula
   - Risk reversal placement
8. Plan UX using ux-audit-agent's checklist:
   - Remove navigation
   - Mobile-first layout
   - Trust signals near every CTA
9. Verify message match with traffic source(s)
10. Run through quality gate checklist
11. Deliver page structure with copy recommendations

Note: Route C uses the agent frameworks and reference files as planning tools but follows the sequential workflow rather than parallel dispatch, since the page doesn't exist yet to audit in parallel.


Step 0: Pre-Dispatch Context Gathering

Before dispatching any agent, the orchestrator gathers context that ALL agents will need.

Product Context Check

Check for .agents/product-context.md. If available, read for product details and accuracy. If missing, strongly recommend running icp-research first — this skill works without it but produces significantly better output with it. If .agents/product-context.md or .agents/mkt/icp-research.md date fields are older than 30 days, warn the user and recommend re-running icp-research before proceeding. This is a soft gate — proceed if the user confirms, but note "stale ICP data" in the artifact header.

Required Artifacts

None — can audit any page standalone.

Optional Artifacts

ArtifactSourceBenefit
icp-research.mdicp-researchVoC data for persuasion
product-context.mdicp-researchProduct details for accuracy
experiment-*.mdexperimentTest design context

Pre-Writing Framework

Answer these questions before dispatching. Pass the answers to every agent as the pre-writing input:

  1. What is the primary conversion goal? (signup, purchase, demo request, download)
  2. Who is the target audience? What do they currently believe? What language do they use?
  3. Where is the traffic coming from? (Ad, search, email, social, direct) — determines message match requirements.
  4. What traffic source copy exists? (ad headlines, email subject lines, search meta descriptions) — needed for message-match verification.

If .agents/mkt/icp-research.md exists, pull VoC quotes and pain language for all agents.


Dispatch Protocol

How to spawn a sub-agent

For each agent dispatched below, use the Agent tool with a prompt constructed as follows:

  1. Read the agent instruction file (e.g., agents/hero-audit-agent.md) — include its FULL content in the Agent prompt
  2. Append the brief and pre-writing context after the instructions
  3. Resolve file paths to absolute: replace relative paths with absolute paths rooted at this skill's directory. Example: if this skill is at /Users/you/skills/lp-optimization/, then references/core-principles.md becomes /Users/you/skills/lp-optimization/references/core-principles.md. Tell the agent: "Read the reference file at [absolute path] for domain knowledge."
  4. Pass upstream artifacts by content, not path: the orchestrator reads .agents/product-context.md and .agents/mkt/icp-research.md FIRST, then includes relevant excerpts (VoC quotes, pain language, product details) in the pre-writing object. Sub-agents should NOT read artifact files directly — the orchestrator curates what they need.
  5. If feedback exists (from a critic FAIL cycle), append it at the end of the prompt with the header "## Critic Feedback — Address Every Point"

Single-agent fallback

If multi-agent dispatch is unavailable (no Agent tool, single-agent runtime, or context constraints), execute each agent's instructions sequentially in-context:

  • Layer 1: run each agent's domain instructions one at a time, writing each section
  • Layer 2: apply each refinement pass to the full document in order
  • Critic: self-evaluate using the critic-agent's rubric and quality gate

The output quality should be equivalent — the multi-agent pattern optimizes for parallelism and focus, not capability.


Layer 1: Parallel Audit Agents

Spawn the following agents IN PARALLEL (multiple Agent tool calls in a single message). For each agent, follow the Dispatch Protocol above.

AgentInstruction FilePass These InputsReference Files to Resolve
Hero Auditagents/hero-audit-agent.mdbrief + pre-writingreferences/core-principles.md, references/advanced-psychology.md
Trust Auditagents/trust-audit-agent.mdbrief + pre-writingreferences/social-proof-trust.md
CTA Auditagents/cta-audit-agent.mdbrief + pre-writingreferences/core-principles.md
UX Auditagents/ux-audit-agent.mdbrief + pre-writingreferences/ux-design.md

For quick audit tasks (Route A): Dispatch only the relevant agent(s), not all four.


Merge Step

After all Layer 1 agents return, assemble their outputs into a unified audit document.

Section Order (full audit)

  1. Hero Assessment — hero-audit-agent's headline, subheadline, and hero CTA evaluation
  2. Trust & Social Proof Assessment — trust-audit-agent's proof inventory, gaps, and recommendations
  3. CTA & Conversion Assessment — cta-audit-agent's CTA inventory, form audit, risk reversal, friction analysis
  4. UX & Page Experience Assessment — ux-audit-agent's navigation, mobile, speed, cognitive load findings

Assembly Rules

The merge is deterministic assembly, not creative synthesis. Slot each agent's output into the unified document by ownership:

SectionOwner Agent
Headline AssessmentHero Audit
Subheadline AssessmentHero Audit
Hero CTA AssessmentHero Audit
Above-the-Fold VerdictHero Audit
Social Proof InventoryTrust Audit
Recency AssessmentTrust Audit
Cognitive Bias UtilizationTrust Audit
Trust Signal ClusteringTrust Audit
CTA InventoryCTA Audit
Form AuditCTA Audit
Risk Reversal AuditCTA Audit
Conversion Friction AnalysisCTA Audit
Navigation AssessmentUX Audit
Visual HierarchyUX Audit
Mobile ExperienceUX Audit
Page SpeedUX Audit
Cognitive LoadUX Audit

Conflict Resolution

  • Each agent owns specific sections (table above). If two agents mention the same element (e.g., both hero-audit and cta-audit reference the hero CTA), keep the version from the section owner.
  • If hero-audit's headline assessment contradicts ux-audit's visual hierarchy assessment, note both perspectives and let the prioritization agent resolve via ICE scoring.

Layer 2: Sequential Analysis

Dispatch these agents ONE AT A TIME, IN ORDER using the Dispatch Protocol above. Each receives the previous agent's full output as the upstream field.

message-match-agent → prioritization-agent → critic-agent
StepAgentInstruction FileReceives
1Message Matchagents/message-match-agent.mdMerged Layer 1 output + traffic source data
2Prioritizationagents/prioritization-agent.mdMessage match output (includes all findings)
3Criticagents/critic-agent.mdPrioritization output (final document)

Each agent returns the full document with their analysis added + a change log.


Critic Gate

The critic agent returns one of two verdicts:

PASS

The audit meets all quality standards. Deliver the critic's approved output as the final artifact.

FAIL

The critic returns specific failures with:

  • Which sections failed and why
  • Specific fix instructions
  • Which agent to re-dispatch

Rewrite loop:

  1. Read the critic's failure report
  2. Re-dispatch ONLY the named agent(s) with the critic's feedback attached as the feedback input
  3. Run the modified output back through the critic
  4. Maximum 2 rewrite cycles. After 2 failures, deliver the audit with the critic's annotations and flag to the user: "Audit scored [X] — manual review recommended on [specific sections]."

Artifact Template

When saving optimization artifacts, use this frontmatter:

---
skill: lp-optimization
version: 1
date: [today's date]
status: draft
---
On re-run: rename existing artifact to [name].v[N].md and create new with incremented version.

Worked Example — Full Audit (Route B)

Brief: Audit the Acme Analytics free trial page for conversion optimization. Audience: Engineering managers at 50-200 person companies, problem aware. Traffic: Google Ads ("real-time analytics dashboard"), LinkedIn ads (cold), and organic search.

Step 0: Pre-Dispatch

  1. Conversion goal: Free trial signup
  2. Audience: EMs who need real-time visibility but feel buried in tool sprawl
  3. Traffic: Google Ads (warm, searched for solution), LinkedIn (cold, interrupted), organic
  4. Traffic source copy: Google Ad headline: "Real-time analytics dashboard — 14-day free trial"

Layer 1: Parallel Dispatch

--> Hero audit agent returns: Headline "The Best Analytics Platform for Modern Teams" scores 1/4 on 4-U. Recommends: "See every metric your team tracks — one screen, updated every 5 minutes." (4/4). 5 variations provided. --> Trust audit agent returns: No testimonials above fold. Customer logos present but below fold. No recency dates on testimonials. Tier 1 proof missing entirely. Recommends: add strongest result quote near hero CTA, move 3 logos above fold, date-stamp all testimonials. --> CTA audit agent returns: Hero CTA "Get Started" (no first-person, no specificity). Form has 7 fields (name, email, company, role, size, phone, referral source). No risk reversal near any CTA. Recommends: "Start My Free Trial" + cut to 3 fields + "14-day free trial. No credit card." --> UX audit agent returns: Full site nav present (7 links). Mobile CTA below fold. Page load 3.8s due to uncompressed hero image. No sticky CTA on mobile.

Merge (orchestrator assembles)

Slots all Layer 1 outputs into unified audit: Hero Assessment -> Trust Assessment -> CTA Assessment -> UX Assessment.

Layer 2: Sequential Dispatch

--> Message match agent receives merged output + traffic source data. Finds: Google Ad promises "real-time" and "dashboard" — headline mentions neither. Ad says "14-day free trial" — page says "Get Started" with no trial mention. LinkedIn ad not checked ([BLOCKED: no ad copy provided]). Organic meta title partially matches. Scores: Google Ads = Mismatch, Organic = Partial Match. --> Prioritization agent receives message-match output. Consolidates all findings. Ranks by ICE:

  1. Rewrite headline for message match (ICE: 9.3)
  2. Cut form from 7 to 3 fields (ICE: 8.7)
  3. Add risk reversal near hero CTA (ICE: 8.0)
  4. Move testimonial above fold (ICE: 7.0)
  5. Remove navigation links (ICE: 6.7)
  6. Compress hero image for speed (ICE: 6.3)
  7. Add sticky CTA on mobile (ICE: 6.0)

Dispatch: Critic Agent --> PASS

Scores: Completeness 5, Specificity 5, Evidence 4, Prioritization 5, Actionability 5. Average: 4.8/5. All quality gate items checked. Approved.

Final Artifact

---
skill: lp-optimization
version: 1
date: 2026-03-17
status: draft
---

# LP Audit — Acme Analytics Free Trial Page

## Headline Assessment

Current: "The Best Analytics Platform for Modern Teams"
4-U Score: 1/4 (Useful only — not Unique, not Urgent, not Ultra-specific)

Recommended: "See every metric your team tracks — one screen, updated every 5 minutes."
4-U Score: 4/4

## Message Match
Traffic source: Google Ads — "real-time analytics dashboard"
Current headline mentions neither "real-time" nor "dashboard" → broken match.
Fix: Echo "real-time dashboard" in headline.

## Social Proof Audit
- No testimonials above fold → add strongest result quote near hero CTA
- Customer logos present but below fold → move 3 logos above fold

## Form Audit
Current: 7 fields (name, email, company, role, size, phone, referral source)
Recommended: 3 fields (name, email, company) — collect rest via progressive profiling after signup
Expected impact: ~40% form completion improvement

## Priority Action Items
| # | Fix | Expected Impact | Effort | ICE |
|---|-----|----------------|--------|-----|
| 1 | Rewrite headline for message match | High | Low | 9.3 |
| 2 | Cut form to 3 fields | High | Low | 8.7 |
| 3 | Add risk reversal near hero CTA | High | Low | 8.0 |
| 4 | Move testimonial above fold | Medium | Low | 7.0 |
| 5 | Remove navigation links | Medium | Low | 6.7 |
| 6 | Compress hero image | Medium | Low | 6.3 |
| 7 | Add sticky CTA on mobile | Medium | Medium | 6.0 |

Anti-Patterns

Redesign without diagnosis — Jumping to "make it look better" without identifying what's actually blocking conversion. Diagnose before prescribing. INSTEAD: Run the full audit (Route B) to identify specific blockers, then fix in priority order.

Testing design before copy — A/B testing button colors or layouts when the headline doesn't pass the 4-U test. Copy is responsible for 80%+ of conversion impact. INSTEAD: Fix the words before the visuals. Follow the testing priority order: headlines > offers > CTAs > layout > forms.

Ignoring mobile experience — Optimizing for desktop when 60%+ of traffic is mobile. INSTEAD: Check thumb zone placement for CTAs, ensure forms are completable one-handed, verify load time on mobile networks, add sticky CTA for long pages.

Social proof without specificity — "Trusted by thousands of companies" is weaker than "Used by 3,247 teams including Stripe and Notion." INSTEAD: Every social proof element must include specific numbers, named companies, or measurable results.

Multiple competing CTAs — Primary CTA, secondary CTA, sidebar CTA, and exit-intent popup all fighting for attention. INSTEAD: One primary CTA per page — secondary CTAs must not visually compete. Audit the CTA hierarchy.

Vague audit recommendations — "Improve the headline" or "Add social proof" without specifics. INSTEAD: Every recommendation must include: what was observed, which principle it violates, and exact replacement text or action.

Skipping message match — Optimizing page copy without checking if it matches the traffic source. INSTEAD: Always verify message match first. A perfectly written headline that doesn't match the ad will still bounce visitors.


Agent Files

Sub-Agent Instructions (agents/)

Shared References (references/)

Sources: CopyHackers, CXL, Unbounce

平台分布

Codex

35.1%
按下载量换算23

Claude

31.19%
按下载量换算20

Cursor

19.1%
按下载量换算12

Gemini CLI

10.16%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

未通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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