LinkedIn Auto-Commenter — Project Standards

## Comment Quality

9 stars

Best use case

LinkedIn Auto-Commenter — Project Standards is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

## Comment Quality

Teams using LinkedIn Auto-Commenter — Project Standards should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/skills/SKILL.md --create-dirs "https://raw.githubusercontent.com/JienWeng/yappy/main/.claude/skills/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/skills/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How LinkedIn Auto-Commenter — Project Standards Compares

Feature / AgentLinkedIn Auto-Commenter — Project StandardsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

## Comment Quality

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# LinkedIn Auto-Commenter — Project Standards

## Comment Quality

### Tone
- Conversational and direct — write like a knowledgeable peer, not a marketing department
- 2–4 sentences per comment
- Use contractions naturally (it's, I'm, that's, we've, etc.)
- Reference specific details from the post — generic comments are not acceptable
- Add value: a question, a relevant experience, a constructive perspective

### Prohibited Content
- Never start a comment with a compliment about the post ("Great post!", "So insightful!")
- Never include any phrase from src/ai/banned_phrases.py
- Never reveal AI, automated, or generated origins
- No corporate buzzwords: leverage, synergy, paradigm shift, game-changer, etc.

## Operational Limits

### Daily Cap
- Hard limit: 20 comments per UTC day
- Enforced by RateLimiter.assert_can_post() before every comment attempt
- DailyLimitExceededError is raised — do not bypass or catch and ignore

### Timing
- Minimum delay between comments: 15 seconds
- Maximum delay between comments: 45 seconds
- Typing speed: 55–80 WPM with +/-30% per-character jitter

## Architecture Constraints

### Stealth Requirements
- Browser must run headful (headless=False) — LinkedIn detects headless via GPU/font fingerprinting
- stealth_async(context) must be called AFTER launch_persistent_context() returns
- Use STEALTH_BROWSER_ARGS from browser_factory.py — do not modify without testing

### Data Immutability
- All models use frozen=True (@dataclass(frozen=True) or Pydantic frozen=True)
- Never mutate model instances — create new ones instead

### Duplicate Prevention
- ActivityLog.was_commented(post_url) is checked before adding any post to the scrape results
- Do not comment on the same post URL twice across sessions

### Persistent Session
- Browser profile stored at data/browser_profile/ (gitignored)
- First run: log into LinkedIn manually in the opened browser
- Subsequent runs: session is restored automatically — no re-login needed

## File Organization
- Max 800 lines per file, target 200–400 lines
- One concern per module — no mega-files
- All error handling must be explicit — never swallow exceptions silently

Related Skills

plan-creating-project-plans

9
from wahidyankf/open-sharia-enterprise

Comprehensive project planning standards for plans/ directory including folder structure (ideas.md, backlog/, in-progress/, done/), stage-aware naming convention (backlog/done use YYYY-MM-DD__identifier/, in-progress uses identifier/ with no date prefix), five-document file organization (README.md, brd.md, prd.md, tech-docs.md, delivery.md for multi-file default; single README.md for trivially-small single-file exception), BRD/PRD content-placement rules, Gherkin acceptance criteria, and the mandatory structured multiple-choice grilling gates (pre-write and post-write) for resolving design decisions with the user. Essential for creating structured, executable project plans.

ci-standards

9
from wahidyankf/open-sharia-enterprise

CI/CD standards knowledge for validating project compliance with CI conventions

team-linkedin-profiles

9
from orthogonal-sh/skills

Find LinkedIn profiles of a specific team or department at a company. Use when asked to get LinkedIn profiles, find team members, or look up people in a particular team/department/group at a company.

linkedin-scraper

9
from orthogonal-sh/skills

Get LinkedIn profiles, company pages, and posts

~aod-project-plan

9
from davidmatousek/tachi

Validates architecture documentation completeness by checking for technology stack, API specifications, database schema, security architecture, and alignment with feature specification. Use this skill when you need to check if plan.md is complete before implementation, validate architecture documentation, or review technical plans for completeness.

django-project-setup

9
from jpoutrin/product-forge

Set up a new Django 6.0 project with modern tooling (uv, direnv, HTMX, OAuth, DRF, testing). Use when the user wants to create a Django project from scratch with production-ready configuration.

propose-project-learning

9
from jpoutrin/product-forge

Propose additions to project CLAUDE.md based on session learnings

performing-ioc-enrichment-automation

9
from killvxk/cybersecurity-skills-zh

通过编排 VirusTotal、AbuseIPDB、Shodan、MISP 和其他情报源的查询, 自动化入侵指标(IOC)丰富化,提供上下文评分和处置建议。 适用于 SOC 分析师在告警分诊或事件调查期间需要对 IP、域名、URL 和文件哈希 进行快速多源丰富化时。

performing-automated-malware-analysis-with-cape

9
from killvxk/cybersecurity-skills-zh

部署和操作 CAPEv2 沙箱,进行自动化恶意软件分析,具备行为监控、载荷提取、配置解析和反规避能力。

implementing-soar-automation-with-phantom

9
from killvxk/cybersecurity-skills-zh

使用 Splunk SOAR(原 Phantom)实施安全编排、自动化和响应(SOAR)工作流, 自动化告警分诊、IOC 富化、遏制动作和事件响应剧本。 适用于 SOC 团队需要减少分析师手工工作、标准化响应流程, 或将多种安全工具集成到自动化工作流中时。

implementing-kubernetes-pod-security-standards

9
from killvxk/cybersecurity-skills-zh

Pod 安全标准(PSS)定义了三个安全策略级别——特权级、基线级和受限级——由 Kubernetes 1.25+ 内置的 Pod 安全准入(PSA)控制器强制执行。

detecting-aws-guardduty-findings-automation

9
from killvxk/cybersecurity-skills-zh

使用 EventBridge 和 Lambda 自动化处理 AWS GuardDuty 威胁检测发现,实现实时事件响应、自动隔离受损资源和安全通知工作流。