awesome-agent-conventions
Standardized agent conventions reduce integration friction and miscommunication between teams deploying AI agents.
A curated guide to convention files AI agents read, write, and act on: AGENTS.md, CLAUDE.md, SKILL.md, llms.txt, MCP configs, rules, and examples.
- Generate standardized AGENTS.md files to define agent behaviors and capabilities consistently.
- Create CLAUDE.md configuration files that specify how Claude should interact with your codebase.
- Set up llms.txt and MCP configs to enable AI agents to work within your project conventions.
Standardized agent conventions reduce integration friction and miscommunication between teams deploying AI agents. Clear, documented conventions accelerate onboarding and ensure consistent behavior across distributed AI systems.
Platform teams and AI ops engineers establishing governance frameworks for multi-agent deployments.
https://github.com/ItamarZand88/awesome-agent-conventions
By ItamarZand88
How to Get It
claude plugins install ItamarZand88/awesome-agent-conventions
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate standardized AGENTS.md files to define agent behaviors and capabilities consistently
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 28 GitHub stars; 1 contributors; last commit 2d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- Single maintainer. Consider the risk if this person stops maintaining the project.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 28 GitHub stars; 1 contributors; last commit 2d ago; license MIT.