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Cf Publish

根据帖子标题、正文和已有评论生成拟人化 Reddit 回复,支持纯价值与产品植入两种模式;流程包含语料少样本生成、七维 QA 迭代打分、反 AI 词汇清洗和错字注入,可输出文本或 JSON。

aces1up
aces1up
热度 49.13
最近更新 2026-05-10 17:46:05
分类:营销依赖:代码执行 / 第三方 API / 内部调用大模型 / 读写文件应用场景:社交媒体 / 文案创作

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add comment-forge
Metadata
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
["reddit","comments","ai-detection","content","marketing","copywriting"]
requires
["python>=3.10"]

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

Key Required Purpose
GEMINI_API_KEY Yes (or OpenRouter) Primary LLM for generation + QA
OPENROUTER_API_KEY Fallback Alternative LLM provider
CEREBRAS_API_KEY Optional Fast fit scoring (free tier)

QA Dimensions

Dimension Weight What It Checks
naturalness 15% Does it sound like a real person?
value_contribution 15% Does it help the thread?
subtlety 20% Is the product mention (if any) natural?
tone_match 10% Does it match thread + corpus tone?
detection_risk 10% Would redditors flag it as spam?
length_appropriate 10% Right length for this thread type?
ai_fingerprint 20% Em-dashes, AI vocab, perfect grammar?

Pass threshold: 7.0/10 composite score.