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comment-forge评论伪造

Agent Skill

comment-forge 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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。

总安装

6,015

周安装

241

GitHub Stars

公开资料未说明

下载量

1,947
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:comment-forge(评论伪造)
来源仓库:https://github.com/aces1up/comment-forge
安装命令:
openclaw skills install comment-forge
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 comment-forge

简介

基于语料库的 Reddit 评论引擎。生成通过 AI 检测的自然回复,由真实评论语料库和 7 维 QA 评分提供支持。

SKILL.md

name
Comment Forge
slug
comment-forge
version
1.0.0
description
Corpus-grounded Reddit comment engine. Generate natural replies that pass AI detection, powered by real comment corpus and 7-dimension QA scoring.
author
OpenClaw
license
MIT
tags
requires

Comment Forge

Generate Reddit-native comments that sound like a real person wrote them. Powered by a real Reddit comment corpus and a 7-dimension QA pipeline that catches AI fingerprints.

What It Does

Feed it a post title, body, and existing comments. Get back a natural reply that:

  • Matches the thread tone using corpus-informed few-shot prompting
  • Passes AI detection via 7-dimension QA scoring (naturalness, value, subtlety, tone, detection risk, length, AI fingerprint)
  • Strips AI tells with deterministic anti-AI cleaning (em-dashes, smart quotes, 50+ AI vocabulary swaps)
  • Adds subtle humanness with smart typo injection (40% chance, max 1 per draft, never on product names)

Two Modes

Value-First: Pure tactical advice. No product mention. Great for building karma and credibility.

Product-Drop: Mention a product naturally in the reply. Auto-fit scoring determines if the product fits the thread (1-10 score). If it doesn't fit naturally, falls back to value-first.

Pipeline

  1. Corpus Sampling - Stratified, score-weighted real Reddit comment examples
  2. Fit Scoring - Classify thread intent, recommend mode (optional, for product-drop)
  3. Draft Generation - Corpus-informed few-shot prompting via Gemini or OpenRouter
  4. QA Pipeline - Score, revise, re-score loop (3 attempts for product-drop, 7 for value-first)
  5. Anti-AI Cleaning - Deterministic post-processing strips AI vocabulary, em-dashes, smart quotes
  6. Human Touch - Smart typo injection for believable imperfections

Quick Start

bash setup.sh
source .venv/bin/activate

# Value-first (no product)
python3 comment_forge.py --post "Best CRM for small teams?"

# Product-drop
python3 comment_forge.py --post "What tools do you use for email?" \
  --product "Acme Mail" --product-desc "Email automation for small teams"

# With existing comments for tone matching
python3 comment_forge.py --post "How do you handle cold outreach?" \
  --comments "I use Apollo" "LinkedIn works best imo"

# From JSON file
python3 comment_forge.py --file post.json --json

# Skip QA (faster)
python3 comment_forge.py --post "..." --skip-qa

JSON File Format

{
  "title": "Best CRM for small teams?",
  "body": "Looking for something simple...",
  "comments": [
    "I use HubSpot free tier",
    "Notion works if you're small"
  ],
  "product": "Acme CRM",
  "product_url": "https://acme.com",
  "product_description": "Simple CRM for small teams",
  "category": "saas",
  "mode": "product_drop"
}

API Keys

KeyRequiredPurpose
GEMINI_API_KEYYes (or OpenRouter)Primary LLM for generation + QA
OPENROUTER_API_KEYFallbackAlternative LLM provider
CEREBRAS_API_KEYOptionalFast fit scoring (free tier)

QA Dimensions

DimensionWeightWhat It Checks
naturalness15%Does it sound like a real person?
value_contribution15%Does it help the thread?
subtlety20%Is the product mention (if any) natural?
tone_match10%Does it match thread + corpus tone?
detection_risk10%Would redditors flag it as spam?
length_appropriate10%Right length for this thread type?
ai_fingerprint20%Em-dashes, AI vocab, perfect grammar?

Pass threshold: 7.0/10 composite score.

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

97.24%
按下载量换算1,893

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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