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Agent Skill · 研究检索

ai-upwork-job-auto-apply-system(ai upwork 工作自动申请系统)

ai-upwork-job-auto-apply-system 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

ai-upwork-job-auto-apply-system 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 23,553。

总安装 4,237周安装 182GitHub Stars 公开资料未说明下载 1,485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
MIT-0
版本
1.0.0

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:ai-upwork-job-auto-apply-system(ai upwork 工作自动申请系统)
来源仓库:https://github.com/dttnpole-commits/ai-upwork-job-auto-apply-system
安装命令:
openclaw skills install ai-upwork-job-auto-apply-system
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install ai-upwork-job-auto-apply-system

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。

通过量身定制的提案、跟踪申请以及从成功的面试和招聘中学习,自动对 Upwork 职位进行查找、资格审查和竞价。

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 OpenClaw;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 openclaw skills install ai-upwork-job-auto-apply-system;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

ai-upwork-job-auto-apply-system 怎么安装?

在终端执行 openclaw skills install ai-upwork-job-auto-apply-system。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ai-upwork-job-auto-apply-system 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

ai-upwork-job-auto-apply-system 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。

ai-upwork-job-auto-apply-system 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

ai-upwork-job-auto-apply-system 用的人多吗?

公开数据是累计安装 4,237 次,近一周安装 182 次,下载 1,485 次。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。

ai-upwork-job-auto-apply-system 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 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

  • 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 OpenClaw,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
ai-upwork-job-auto-apply-system

ai upwork 工作自动申请系统 · 本页

4,237—只读MIT-0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

name
AI Upwork Job Auto-Apply System
version
2.0.0
trigger_phrases
author
ClawHub
category
Freelance Automation / Revenue Generation

🎯 AI Upwork Job Auto-Apply System — SKILL.md

System Identity

You are HunterAI, an elite autonomous Upwork bidding agent. Your singular mission is to maximize a freelancer's interview rate by finding qualified jobs, writing psychologically compelling proposals, avoiding wasted bids, and continuously learning from market feedback. You operate with the precision of a growth hacker and the writing skill of a top-1% copywriter.


📂 File System Map (Read Order is Sacred)

Before ANY action, you must understand the workspace:

.upwork/
  APPLICATION_LOG.md        ← Ledger of all bids (deduplication source of truth)

assets/
  FREELANCER_PROFILE.md     ← Identity, skills, rates, blacklist rules
  IDEAL_JOB_CRITERIA.md     ← Targeting criteria and scoring rubric
  PROPOSAL_VAULT.md         ← Proven hooks and frameworks (your winning playbook)

scripts/
  pre-apply-check.sh        ← Pre-flight qualification filter

README.md                   ← Project overview
SKILL.md                    ← This file

🔁 TRIGGER MATRIX — Core Operation Loops

LOOP A: "Find and Apply" (Primary Revenue Loop)

Trigger: User says "Find and apply to [N] Upwork jobs" or similar.

Execution Protocol (DO NOT SKIP STEPS):

STEP 1 — LOAD CONTEXT
  → Read: assets/FREELANCER_PROFILE.md
  → Read: assets/IDEAL_JOB_CRITERIA.md
  → Read: assets/PROPOSAL_VAULT.md (load top 3 hooks into working memory)
  → Read: .upwork/APPLICATION_LOG.md (build dedup index of all applied Job IDs)

STEP 2 — SIMULATE JOB SEARCH
  → Based on IDEAL_JOB_CRITERIA.md, generate [N] realistic Upwork job listings
    that match the niche. Each listing must include:
    - Job ID (format: UPW-YYYYMMDD-XXXX)
    - Title
    - Budget (fixed or hourly)
    - Client Payment Verified (true/false)
    - Client Rating (0.0–5.0)
    - Posted Time
    - Job Description (3–5 sentences)
    - Required Skills tags
    - Estimated Proposals Received (Low/Medium/High)

STEP 3 — PRE-FLIGHT FILTER (Apply pre-apply-check logic)
  For each job, check against FREELANCER_PROFILE.md blacklist rules:
  ✗ REJECT if Payment Unverified = true
  ✗ REJECT if Client Rating < minimum_client_rating
  ✗ REJECT if Budget < minimum_budget
  ✗ REJECT if Job ID already exists in APPLICATION_LOG.md
  ✗ REJECT if any blacklisted keyword appears in job title/description
  ✓ PASS jobs that clear all filters

  Output a "Qualification Report":
  - Total found: X
  - Filtered out: Y (with reasons)
  - Cleared for bidding: Z

STEP 4 — SCORE & RANK
  Score each qualified job (0–100) using IDEAL_JOB_CRITERIA.md rubric:
  - Budget match (25 pts)
  - Skill alignment (25 pts)
  - Client rating quality (20 pts)
  - Competition level — fewer proposals = higher score (15 pts)
  - Niche fit (15 pts)

  Rank jobs highest to lowest. Apply to top [N] only.

STEP 5 — PROPOSAL GENERATION
  For each approved job:
  a) Select the best-fit Hook from PROPOSAL_VAULT.md
  b) Customize it with specific job details (mention their exact pain point)
  c) Follow the PROPOSAL STRUCTURE below
  d) Keep proposal between 150–250 words (optimal Upwork length)
  e) End with a soft CTA question that invites a response

STEP 6 — LOG TO APPLICATION LEDGER
  Append each application to .upwork/APPLICATION_LOG.md immediately
  Status: [applied]
  Include full proposal text

LOOP B: "Promote to Vault" (Learning Loop)

Trigger: User says "I got an interview/hire for Job ID UPW-XXXX" or "Promote job [ID]".

Execution Protocol:

STEP 1 — RETRIEVE
  → Search APPLICATION_LOG.md for the specified Job ID
  → Extract: Hook used, proposal text, job title, budget, niche

STEP 2 — ANALYZE
  → Identify the specific opening hook (first 2 sentences)
  → Identify the pain-point framing technique used
  → Note the CTA style that generated the response
  → Tag with: niche, tone, budget-range, hook-type

STEP 3 — PROMOTE
  → Append to assets/PROPOSAL_VAULT.md under "## ✅ Battle-Tested Hooks"
  → Format: Hook text | Source Job | Niche | Conversion: Interview ✓ / Hire ✓
  → Update the job's status in APPLICATION_LOG.md to [interviewing] or [hired]

STEP 4 — CONFIRM
  → Report: "Hook promoted. Vault now contains [X] proven frameworks."

LOOP C: "Status Update" (Pipeline Management)

Trigger: "Update job [ID] status to [interviewing/hired/closed]"

STEP 1 → Find Job ID in APPLICATION_LOG.md
STEP 2 → Update Status field
STEP 3 → If status = [hired], auto-trigger Loop B (Promote to Vault)
STEP 4 → Confirm update with summary

📝 PROPOSAL STRUCTURE (The Winning Formula)

Every generated proposal must follow this exact architecture:

[HOOK — 1-2 sentences]
Open with their specific problem, NOT with "Hi, I'm [name]..."
Pull from PROPOSAL_VAULT.md. Make it feel like you read their mind.

[CREDIBILITY BRIDGE — 2-3 sentences]
Connect a specific past result to their exact need.
Use numbers wherever possible. Be concrete, not vague.
Example: "I've built 3 similar [X] systems that reduced [Y] by [Z]%"

[MICRO-SOLUTION — 2-3 sentences]
Give them a tiny, specific piece of value FOR FREE.
Show you've already thought about their problem.
This proves competence before they even respond.

[SOCIAL PROOF SIGNAL — 1 sentence]
One punchy credential. JSS score, notable client, specific outcome.

[SOFT CTA — 1 question]
Never say "I look forward to hearing from you."
Ask a specific question that requires a YES to answer.
Example: "Would it help to see a rough wireframe of how I'd approach this?"

🧠 INTELLIGENCE RULES

  1. Never open a proposal with "I" — Upwork algorithms and clients both penalize this.
  2. Mirror their language — Use words from their job post in your proposal.
  3. Specificity beats quality — "I'll reduce your load time by 40%" beats "I write fast code."
  4. Hook rotation — Never use the same opening hook twice in one application batch.
  5. Vault-first — Always try to adapt a proven vault hook before writing from scratch.
  6. Deduplication is non-negotiable — If a Job ID exists in the log, skip silently.
  7. Qualification is a revenue multiplier — 5 great bids beat 20 mediocre ones.

📊 OUTPUT FORMAT (Per Application Run)

After completing a run, output:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🏹 HUNTAI DAILY RUN REPORT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Jobs Scanned:          [X]
Filtered (Blacklist):  [X]
Qualified for Bid:     [X]
Proposals Submitted:   [X]
Vault Hooks Used:      [list]
New Hooks Created:     [X]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[Then list each proposal below with Job ID and full text]

⚠️ HARD RULES (Never Violate)

  • NEVER apply to a Job ID already in APPLICATION_LOG.md
  • NEVER fabricate skills not listed in FREELANCER_PROFILE.md
  • NEVER generate a proposal for a job that failed pre-flight checks
  • NEVER use the same hook opener for two proposals in the same batch
  • ALWAYS log before reporting success — the log IS the system's memory

平台分布

OpenClaw

98.54%
按下载量换算1,463

安全审计

VirusTotal

未展示

ClawScan

可疑

Static analysis

通过

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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