What makes linuxdo-application notable?

1K downloads. Trust evidence available

How should linuxdo-application be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

linuxdo-application

Craft high-pass-rate, de-AI'd Chinese applications (小作文) for Linux.do registration. Conducts an adaptive survey to learn the applicant's real background, then generates natural, rule-compliant plain text ready to paste. All user interactions in Chinese. Skill: linuxdo-application Owner: clarezoe Summary: Craft high-pass-rate, de-AI'd Chinese applications (小作文) for Linux.do registration. Conducts an adaptive survey to learn the applicant's real background, then generates natural, rule-compliant plain text ready to paste. All user interactions in Chinese. Tags: chinese:1.0.0, latest:1.0.1, linuxdo:1.0.0 Version history: v1.0.1 | 2026-06-09T13:08:53.250Z | user Normali

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.1release · observed Jun 9, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s178sk484g47vwcbvvfcjpftfx8461fz:linuxdo-application

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-clarezoe-linuxdo-application/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

33,902 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "linuxdo-application"
description: "Craft high-pass-rate, de-AI'd Chinese applications (小作文) for Linux.do registration. Conducts an adaptive survey to learn the applicant's real background, then generates natural, rule-compliant plain text ready to paste. All user interactions in Chinese."
license: "MIT"
metadata: {"openclaw":{"emoji":"📝","homepage":"https://github.com/Fei2-Labs/skill-genie"},"version":"1.0.1","category":"content-generation","author":"Skill Genie","license":"MIT","language":{"backend":"English","user-facing":"Chinese"},"tags":["content-generation","chinese-writing","application-writing","de-ai"],"hermes":{"tags":["content-generation","chinese-writing","application-writing","de-ai"]}}
---

# Linux.do Application Writer

Generate a natural, high-pass-rate registration application for Linux.do through
an adaptive interview followed by rule-aware, de-AI'd text generation.

## Triggers

- "write linux.do application"
- "linux.do 小作文"
- "linux.do 注册申请"
- "help me apply to linux.do"

## Quick Reference

| Input | Output | Duration |
|-------|--------|----------|
| Answers to 5-8 survey questions | Plain text Chinese application (~80-150 chars) | 5-10 min |

## Process

### Phase 1: Applicant Survey

Conduct an adaptive interview in Chinese to collect the 4 required info blocks.
Ask **one question at a time**. Adapt follow-ups based on answers.

**Core questions (ask in Chinese, adapt order based on flow):**

1. 你平时主要做什么？（工作、学习、兴趣方向）
2. 你是怎么知道 Linux.do 的？（搜索、朋友推荐、看到某个帖子？）
3. 你在上面浏览过哪些内容？有没有印象深的帖子或话题？
4. 为什么现在想注册？有什么具体的需求或场景吗？
5. 注册之后你打算怎么用？（潜水、回帖、关注某类话题、分享经验？）

**Adaptive rules:**
- If an answer is vague (e.g., "想学习交流"), probe deeper: "具体想学什么？在哪看到过相关讨论？"
- If an answer already covers multiple blocks, skip redundant questions
- If the applicant mentions a specific post/topic, ask them to elaborate — this is gold
- Stop when all 4 info blocks are covered (see references/linuxdo-rules.md)

**Verification:** Before moving to Phase 2, confirm internally:
- [ ] Background covered (what they do/follow)
- [ ] Discovery path covered (how they found the site)
- [ ] Join reason covered (why register now, specific scenario)
- [ ] Usage plan covered (what they'll do with the account)

If any block is missing, ask one more targeted question.

### Phase 2: Draft Generation + Risk Check

Generate the application draft using collected info.

**Generation rules:**
1. Write in first person, casual Chinese — like telling a friend why you signed up
2. Target 80-150 characters. Not too short (looks lazy), not too long (looks try-hard)
3. Weave all 4 info blocks naturally — do NOT use a 4-paragraph structure
4. Use the applicant's own words and phrasing where possible
5. No greetings, no sign-offs, no "你好" or "谢谢" — just the substance
6. Allow sentence fragments, colloquialisms, and imperfect grammar
7. Vary sentence length: mix short punchy lines with longer ones

**Risk check — scan the draft against these (see references/linuxdo-rules.md):**

| R

README.md

# Linux.do Application Writer

Craft high-pass-rate, de-AI'd Chinese applications (小作文) for Linux.do registration through an adaptive survey and rule-aware text generation.

## Install

**OpenClaw / ClawHub:**
```bash
clawhub install linuxdo-application
```

**Manual:**
```bash
git clone https://github.com/Fei2-Labs/skill-genie.git
cp -r skill-genie/linuxdo-application ~/.openclaw/skills/linuxdo-application
```

## Usage

Trigger the skill with any of these phrases:
- "write linux.do application"
- "linux.do 小作文"
- "linux.do 注册申请"
- "help me apply to linux.do"

The agent will:
1. Conduct an adaptive Chinese-language survey (5-8 questions, one at a time)
2. Generate a draft application covering all 4 required info blocks
3. Run a risk check and de-AI polish pass before outputting plain text

## Compatibility

| Agent | Compatible |
|-------|-----------|
| OpenClaw | Yes |
| Hermes | Yes |
| Claude Code | Yes |
| Other SKILL.md agents | Yes |

No scripts or external dependencies — pure SKILL.md workflow.

## File Structure

```
linuxdo-application/
├── SKILL.md                          # Main workflow
├── README.md                         # This file
└── references/
    ├── linuxdo-rules.md              # Official rules + community signals
    └── de-ai-checklist.md            # 12 AI markers to detect and fix
```

## License

MIT

---

**Source**: [github.com/Fei2-Labs/skill-genie](https://github.com/Fei2-Labs/skill-genie)
**Author**: [@clarezoe](https://x.com/clarezoe)

_meta.json

{
  "ownerId": "kn71ntcwxg33gv53ryabqt0vw981g9my",
  "slug": "linuxdo-application",
  "version": "1.0.1",
  "publishedAt": 1781010533250
}

references/de-ai-checklist.md

# De-AI Checklist

12 markers that make Chinese text sound AI-generated. The agent must audit every draft
against these markers and rewrite any flagged sentences.

## Detection Markers

### Structural Markers

| # | Marker | Example | Fix |
|---|--------|---------|-----|
| 1 | **Parallel sentence structures** — 3+ sentences with identical grammar pattern | 我喜欢X，我热爱Y，我追求Z | Break the pattern; vary grammar |
| 2 | **Three-part formula (三段式)** — intro-body-conclusion with clear transitions | 首先...其次...最后 | Drop the scaffolding; just say it |
| 3 | **Uniform sentence length** — all sentences roughly the same character count | Every sentence 15-20 chars | Mix short (5-8) with medium (15-25) |
| 4 | **Bullet-point prose** — text reads like a formatted list without the bullets | Point 1. Point 2. Point 3. | Merge ideas into flowing text |

### Vocabulary Markers

| # | Marker | Example | Fix |
|---|--------|---------|-----|
| 5 | **AI cliché words** — 赋能、助力、深耕、沉淀、拥抱、赛道、底层逻辑、认知 | 深耕技术领域多年 | Use plain words: 做了好几年 |
| 6 | **Excessive connectives** — 此外、与此同时、不仅如此、值得一提的是 | 此外，我还关注了... | Drop the connector; start directly |
| 7 | **Grandiose metaphors** — 灯塔、宝藏、殿堂、星辰大海 | Linux.do 是技术人的灯塔 | Just say what it is plainly |
| 8 | **Negation-based emphasis** — 不是X而是Y pattern repeated | 不是简单的学习，而是深度的交流 | State it directly without the contrast |

### Tone Markers

| # | Marker | Example | Fix |
|---|--------|---------|-----|
| 9 | **Over-polished tone** — no colloquialisms, no casual markers | Reads like a press release | Add 吧、嘛、其实、反正、就是 |
| 10 | **Propaganda tone** — 共同进步、携手前行、共建生态 | 希望和大家共同进步 | Say what you'll actually do |
| 11 | **Perfect grammar throughout** — no fragments, no run-ons | Every sentence is grammatically flawless | Allow natural imperfections |
| 12 | **Flattery without substance** — 久仰大名、慕名而来、心向往之 | 久仰 Linux.do 大名 | Replace with how you actually found it |

## Audit Process

1. Read the draft sentence by sentence
2. Flag any sentence matching 1+ markers above
3. For each flagged sentence, rewrite using the "Fix" column guidance
4. Re-read the full text aloud — does it sound like someone talking, not writing an essay?
5. Final check: would a human reviewer think "this person actually wrote this"?

## Quick Smell Test

Ask these 3 questions about the final draft:
- **Could you swap the applicant's details and reuse this for someone else?** If yes → too generic, rewrite.
- **Does every sentence add new information?** If any sentence is pure filler → cut it.
- **Would you text this to a friend?** If it feels too formal → loosen the tone.

references/linuxdo-rules.md

# Linux.do Application Rules & Signals

Comprehensive rule set for crafting high-pass-rate Linux.do registration applications.
Sources: official announcements, FAQ, and public community experience posts.

## Hard Rules (Official — Pass/Fail)

### 1. Read FAQ/Guidelines First
Applicants must read the FAQ before registering. The registration page requires checking "已读".
Reviewers specifically flag applications that show the applicant didn't read the rules.

### 2. Self-Written Application
The application must be a ~50-character self-written text covering your background and why you want to join.
"Randomly filled" or "AI-generated" applications will not pass in principle.

### 3. Invited Users Are Also Reviewed
Even with an invitation link, the same application content is required and reviewed by admins.

### 4. Content Is Archived
Application content is not publicly displayed but is archived for future reference.
This means: don't treat it as throwaway text.

## Content Characteristics That Increase Pass Rate

### 1. Specificity > Enthusiasm
Easy-pass applications are concrete: what you tinker with, how you found Linux.do,
which topics you browsed, why you need an account, what you plan to participate in.
Facts + path + scenario = looks like a real person.

### 2. Site-Aligned Motivation
Generic phrases like "want to learn", "want to meet experts", "want to grow together"
carry zero information. Users report failing twice before rewriting with specific
site-related reasons and use cases.

### 3. Show You Know What the Site Is
If the application reveals you don't know what's discussed on Linux.do, or you treat it
as a generic "resource group / tech group / beginner site", pass rate drops.
This aligns with the official emphasis on "read the intro and rules first".

### 4. "How I'll Participate" Is a Bonus
Common pattern in successful applications: not just "I want to look around" but
"I've encountered similar problems", "I've seen solutions on the site",
"I want to reply, document, discuss X topic". This reads like a long-term user.

## Required Information Blocks (4 Blocks)

Every application should contain these 4 pieces:

| Block | What to Include |
|-------|----------------|
| **Real Background** | What you do, what fields you follow. Doesn't need to be complete. |
| **Discovery Path** | How you found Linux.do — search, friend, specific post, long-time lurker. |
| **Why Register Now** | Not abstract "want to join" but concrete: participate in X, reply to Y, follow Z. |
| **Account Usage Plan** | Read-only? Occasional replies? Follow specific topics? Be real and specific. |

## High-Risk Patterns (Likely Rejection)

| Pattern | Why It Fails |
|---------|-------------|
| No background/experience mentioned | Missing required info block |
| No explanation of how you found the site | Looks random |
| No reason for joining | Missing required info block |
| All fluff, no concrete facts | Template-like |
| Overly neat, uniform tone | Looks batch-generate

Github ReposUpdated 2d agoRank 70

AionUi

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

MCPOPENCLAW

Github ReposUpdated 6mo agoRank 70

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

OPENCLAW

Github ReposUpdated 6mo agoRank 70

cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants.