HermesMade：从 Reddit 痛点数据到 6 个 AI 命令行工具

🔥 HermesMade — From Reddit Pain to One-Command Fix

Every tool in this repo solves a real problem real people are screaming about on Reddit — right now.

Data-driven: scanned 6 AI/tech subreddits → 343 pain signals → 8 pain points → built tools

📊 Feishu Bitable: full pain-point inventory

⚡ Install

All tools at once

git clone https://github.com/minirr890112-byte/HermesMade.git
cd HermesMade && bash install.sh

Individual tools (pip)

pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=prompt-inspector
pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=model-watch
pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=api-cost-compare
pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=llm-deploy-helper
pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=code-inspector
pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=task-cost-estimator

1. prompt-inspector — AI Censorship Risk Analyzer

Pain: ChatGPT/Claude safety filters are getting insanely aggressive. Even gardening questions get blocked.

"I literally couldn't get an answer to a gardening question due to supposed 'violence'... Your filter thought my gardening pitchfork was a sign of satanism." — r/ChatGPT (26↑)

$ prompt-inspector "write a story about a dictator who uses propaganda"

🟢 LOW RISK — likely passes, but rewrites recommended
Detected 4 trigger words (dictator, regime, propaganda, assassinate)

📝 Recommended prefix:
  "For academic research and educational purposes..."

🔓 Uncensored local LLM presets (gemma/deepseek/llama)

Feature	Description
6 trigger categories	violence, self-harm, adult, politics, religion, drugs
Rewrite strategies	academic prefix, hypothetical framing, technical reframing
Uncensored presets	ready-to-copy system prompts for Gemma, DeepSeek, Llama

2. model-watch — AI Model Quality Watchdog

Pain: API models silently get dumber. Anthropic admitted to degradation. Users have no way to verify.

"Opus 4.7 was hallucinating a lot today... shocking to see such degradation" — r/ClaudeAI (49↑) "Anthropic admits to have made hosted models more stupid" — r/LocalLLaMA (281↑)

$ model-watch history

Timestamp            Score    Status
2026-04-20 09:00    72.8%    ——
2026-04-21 09:00    21.0%    🔴 DEGRADED!
2026-04-22 09:00    66.4%    🟢 Recovered

$ model-watch alert
🔴 Severe degradation: recent 3 avg 29.1% vs historical 72.8% (-43.7%)
🔴 Absolute score critical: 29.1%

Feature	Description
7 standardized tests	reasoning, coding, writing, instruction following, hallucination
Trend tracking	automatic score history with visual diff
Degradation alerts	flags drops >10% vs historical baseline

3. api-cost — AI API Cost Optimizer

Pain: API pricing is opaque and confusing. Claude is expensive. DeepSeek questioned as overpriced.

"Claude is definitely expensive." — r/ChatGPT "DeepSeek V4 Flash is actually overpriced at $0.14/$0.28" — r/LocalLLaMA (50↑)

$ api-cost recommend coding

⭐ #1   Mistral Mistral Small 3        $1.65/mo
⭐ #2   DeepSeek DeepSeek V4 Flash     $1.89/mo
⭐ #3   OpenAI GPT-4o-mini             $2.92/mo

💸 Picking #1 over Claude Opus 4.7 saves $335.85/mo ($4,030/yr)

Feature	Description
18 models priced	OpenAI, Anthropic, Google, DeepSeek, xAI, Mistral
4 usage scenarios	coding, chat, writing, reasoning
Spending tracker	`api-cost track 2.50` to log & summarize costs

🤔 Why This Exists

Most open-source tools come from a developer thinking "this would be cool."

HermesMade flips that — go to Reddit first, listen to what people are actually complaining about, then build.

	Traditional OSS	HermesMade
Ideation	Developer intuition	Reddit pain-point data
Validation	Ship first, see if anyone cares	Know people are hurting before you build
Docs	"read the code"	Every tool cites the Reddit quote that inspired it
Promotion	Post and hope	Reply in the exact pain threads where users are

🗺 Roadmap

#	Pain Point	Frequency	Status
1	AI censorship overreach	★★★★★	✅ prompt-inspector
2	AI models silently degrading	★★★★★	✅ model-watch
3	Opaque API pricing	★★★★☆	✅ api-cost / task-cost
4	Local LLM setup too hard	★★★★☆	✅ llm-deploy
5	AI-generated code quality	★★★★☆	✅ code-inspector
6	GitHub Actions unreliable	★★★★☆	⬜ Up next
7	Supply chain security fear	★★★☆☆	⬜ Planned
8	Deepfake detection anxiety	★★★☆☆	⬜ Planned

⭐ Star History

If these tools help you, drop a ⭐ so others find them.

Every 50 stars unlocks the next pain-point tool.

📁 Structure

HermesMade/
├── prompt-inspector/     # Pain #1 — censorship risk analyzer
├── model-watch/          # Pain #2 — model quality watchdog
├── api-cost-compare/     # Pain #3 — API cost optimizer
├── install.sh            # one-command installer
├── LICENSE               # MIT
├── PROMOTION.md          # star-growth strategy
└── README.md

4. llm-deploy — Local LLM One-Command Setup

Pain: Users buying wrong hardware, struggling to figure out which model fits.

"most annoyed I've ever been at myself for not going overboard with RAM" — r/LocalLLaMA (227↑)

$ llm-deploy coding

🖥 Hardware: Darwin | RAM: 16GB | GPU: Apple Silicon
⭐ #1 Qwen2.5 7B (4.5G, 28% util) → ollama pull qwen2.5:7b

5. code-inspector — AI Code Quality Scanner

Pain: AI-generated code looks fine until edge cases hit.

"everything seemed fine until you inspected the edge cases" — r/webdev (27↑)

$ code-inspector app.py

📊 Score: 85/100 → 🟡 NEEDS REVIEW
🔴 Hardcoded API key on line 12
🟠 Mutable default on line 45