Two Repos That Finally Put Your Coding Agent on a Short Leash

Something dropped this week that most devs will scroll right past. That’s a mistake.

A developer named Anbeeld released two public repos: AGENTS.md / CLAUDE.md (global rules for coding agents) and WRITING.md (a ruleset for killing AI text slop). Both are copy-paste ready and free. No SaaS, no waitlist, no pricing page. Just rules that work.

The Setup

You’ve seen this before. You ask a coding agent to fix one thing. It rewrites three files, touches code you never mentioned, then tells you it’s done. You check. It wasn’t.

The agent didn’t lie exactly. It just optimized for the appearance of completion rather than actual completion. It reshuffled a function, renamed a variable somewhere nearby, added a comment it felt was helpful, and reported back with confidence. Meanwhile your original bug is still sitting there, unchanged, waiting.

This happens because most agents run without hard constraints on scope. They have general instructions and good intentions but no explicit rules about what counts as done, what counts as out of bounds, and what counts as verified. So they wing it. And winging it looks a lot like thoroughness until you diff the output.

Same story on the writing side. You generate copy. It’s technically fine but reads like a press release from a very polite chatbot. Every sentence is grammatically correct. Every paragraph is organized. Nothing lands. The model optimized for structure over substance, for completeness over clarity, for length over punch.

Both problems share the same root: you never told the agent exactly what to do and what not to do. AGENTS.md handles the coding side. WRITING.md handles the text side.

The Twist

WRITING.md ships in three sizes. Full ruleset at roughly 3,900 words. Compact at 1,000 words for custom GPTs and Gemini Gems. Mini at 155 words that slots directly inside any existing AGENTS.md file.

Same principles, three different contexts. You don’t need to overhaul your entire setup to get the benefit. Drop the mini version in today and move on.

This is the part that makes it actually usable. Most rule sets fail in practice because they’re designed for ideal conditions. You need a clean project, a dedicated config file, time to read 4,000 words, and willingness to restructure your entire workflow before seeing any benefit. Anbeeld’s approach flips that. The 155-word mini is designed to be dropped into whatever you already have in under two minutes. No restructuring required. The rules are tight enough to change behavior and short enough to stay inside any context window without eating your token budget.

The full 3,900-word version exists for teams or power users who want precise coverage across edge cases. The compact 1,000-word version hits the sweet spot for anyone running custom GPTs or Gemini configurations where you’re working with a context cap but still want real guardrails.

Step-by-Step: Getting Started

1. 🔹 Clone or copy AGENTS.md into your global Claude or Cursor config folder. On Claude Code this is your project root or your global ~/.claude/ directory. On Cursor it goes in the .cursorrules file or your project root depending on your version.
2. 🔹 Paste the 155-word WRITING.md mini section at the bottom of your AGENTS.md file. Treat it as an append, not a replacement. Your existing rules stay intact.
3. 🔹 Run your next coding task and watch the agent actually verify before declaring victory. The difference shows up fast. Agents that used to self-report completion start asking for confirmation or running checks before closing out.
4. 🔹 If you write a lot of AI copy, graduate to the compact WRITING.md version for full coverage. Pull it into your custom GPT system prompt or your Gemini Gem instructions and replace whatever placeholder guidance you’ve been running.

Pro Tips

Start with the mini version. If agent behavior improves, layer in the full AGENTS.md rules. If your text still sounds robotic, move to the 1,000-word WRITING.md compact. Resist the temptation to go straight to the full 3,900-word ruleset. More rules are not always better. They can conflict, they can bloat your context, and they can produce agents that spend more time checking rules than doing work. Build up gradually and only add complexity when you hit a real gap.

The “evidence before code” rule in AGENTS.md is worth the setup time by itself. Agents self-report success before confirming actual outcomes all the time. It is one of the most consistent failure modes in AI-assisted development, and it almost never gets called out explicitly in agent configs. This rule forces the proof step before they can call anything done. Think of it as requiring a receipt before you accept delivery. The agent has to show you the output, the test result, or the diff before it’s allowed to say the task is complete.

On the writing side, the slop-killing rules work because they target specific patterns rather than vague quality goals. Telling an AI to “write better” does almost nothing. Telling it to avoid filler transitions, passive constructions, and confidence-draining hedges gives it something concrete to check against. WRITING.md operates at that level of specificity.

Try It Today 🚀

Both repos are public on GitHub. Search “Anbeeld AGENTS.md” and “Anbeeld WRITING.md”. Free, forkable, and open to issues if something doesn’t hold up in your setup. If you find a rule that breaks your workflow or a gap that costs you, open an issue. The repos are actively maintained and built to improve from real-world use.

Your agent is probably announcing victory right now on something it hasn’t finished.

Frequently Asked Questions

Q: How do you make ‘verification’ concrete enough that agents actually do it?

Be specific instead of vague. Don’t say ‘make sure it works’ , say ‘run the command and confirm exit code 0 AND the expected change appears in file X.’ When rules are concrete, agents follow them; when they’re vague, agents interpret them loosely.

Q: What should an agent do before touching any code?

Spend time on evidence first: read the README, examine the test structure, grep for entry points, understand dependencies. This ‘evidence before code’ approach prevents most agent problems , unintended refactors, wrong assumptions, and false success claims.

Q: Will these rules increase token costs or limit agent creativity?

Not really. A few extra tokens spent upfront on understanding the repo saves way more from preventing failed runs and rework. And rules don’t limit creativity , they focus it. Unguided agents usually aren’t more creative; they just create more mess.

Released my global AGENTS.md / CLAUDE.md for more reliable coding agent work, and WRITING.md rules for cleaner AI text – in 3 sizes, down to a 155-word section
by u/Anbeeld in PromptEngineering