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