Writing Effective SOUL.md Files for AI Coding Agents

A recent discussion on r/openclaw highlights the practical difference between effective and ineffective SOUL.md files for AI coding agents. The post emphasizes that specificity in instructions directly impacts agent performance.
What Makes a SOUL.md Work
The source provides concrete examples of ineffective versus effective approaches:
- Doesn't work (too vague): "You are a helpful AI assistant. Be polite and professional."
- Works (specific): "You are an efficient executive assistant. Methodical and concise."
Specific Instructions That Work
The effective SOUL.md example includes these specific directives:
- "No filler phrases. No 'Great question!' — just do the thing."
- "Have opinions. Disagree when it matters."
- "Ask before sending emails or posting publicly."
- "In group chats: participate, don't dominate. React with emoji instead of replying when that's enough."
Key Insight
The post states: "The model matches whatever energy you give it. Vague = vague. Specific = an agent that actually feels alive." This emphasizes that the quality of AI agent behavior directly correlates with the specificity of instructions provided in the SOUL.md file.
The original poster invites community engagement with: "What does your SOUL.md look like? Happy to review if you share."
📖 Read the full source: r/openclaw
👀 See Also

9 Practical Claude Tips from 8 Months of Daily Use (Non-Coding)
A Reddit user shares 9 hard-won lessons from 8 months of daily Claude use for writing and research—not code—covering editing, context management, style setup, and using Claude as a thinking partner.

Claude Code and the Unreasonable Effectiveness of HTML for AI Agents
A viral post demonstrates how AI coding agents like Claude Code produce better results when instructed to generate HTML, with working examples and a companion blog post discussing the pattern.

Claude Code: Context Management Over Prompt Engineering
A developer shares that after a year of using Claude Code, the key skill isn't prompt wording or model selection, but providing comprehensive project context upfront to get better results.

Running MiniMax M2.7 Q8_0 128K on 2x3090 with CPU Offloading – Real-World Benchmarks and Config
A user successfully runs MiniMax M2.7 at Q8_0 with 128K context on two RTX 3090s plus DDR4 RAM, achieving ~50 tps prompt processing and ~10 tps token generation, and shares their llama-server flags.