Skill: aces1up/comment-forge (version 1.1.0)

--- 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 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 ```json { "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.


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()