aigis-cli 2.1.0

AI governance guardrails for coding agents. Framework-aligned security and compliance patterns from NIST AI RMF, OWASP Top 10 for LLMs, and ISO/IEC 42001.

Aigis

AI governance that ships with your code.

Aigis is a command-line tool that gives coding agents like Cursor, Claude Code, GitHub Copilot, and Windsurf curated AI governance patterns mapped to NIST AI RMF, OWASP Top 10 for LLMs, and ISO/IEC 42001.

You describe what you're building. Aigis classifies it against the frameworks that apply. Your agent pulls the right governance patterns and implements them. One command verifies the result.

Install

npm install -g @aigis-ai/cli
# or
pip install aigis-cli

Verify:

aigis --version
# 2.0.1

Quick start

aigis build "customer support chatbot with order history lookup"

Aigis will classify the description against NIST, OWASP, and ISO frameworks, identify relevant governance areas (PII handling, rate limiting, audit logging, prompt security), and produce a consolidated brief your agent can implement from.

Paste the brief into your coding agent's chat. The agent implements the patterns. Then:

aigis verify <area> --auto .

Runs deterministic checks on the implementation.

What's new in v2.0

• aigis build — new command that produces one consolidated governance brief per project
• Infrastructure content — production-ready patterns for rate limiting, secrets management, and structured logging
• Curated resolver — 39 high-signal triggers mapped to traits, with interactive confirmation for low-confidence matches
• Content architecture redesign — areas, workflows, and infrastructure separated into layered skills, built around how modern AI agents actually read context

How Aigis is different

Structured. Aigis is a compiler for agent instructions, not a prompt. The resolver uses deterministic rules. The procedures have explicit verification checkpoints. Brief generation is fully deterministic — same description produces byte-identical output.

Honest. Every iteration of v2.0 was benchmarked. The final numbers, against the same 10 descriptions:

• Baseline (no Aigis): P=0.737, R=0.905, F1=0.790
• v2.0 (aigis build): P=0.847, R=0.851, F1=0.837

F1 beats baseline by +0.047. Precision improved by +15%. Methodology and per-run tables live in benchmarks/.

Open source. Local. Never calls external LLMs. Never writes files to your project. Your code, your tools, your accountability.

The principle behind Aigis

Governance isn't a content problem. It's an interface problem.

AI governance frameworks exist. NIST AI RMF, OWASP Top 10 for LLMs, ISO/IEC 42001 — all rigorous, all published, all sitting in documents that engineering teams never read.

Aigis treats governance as an agent-computer interface problem. Context layered for on-demand loading. Deterministic rules where accuracy isn't negotiable. Flexible reasoning where real projects don't fit rigid templates.

Inspired by SWE-agent's work on agent-computer interfaces — the idea that how information reaches an LM agent matters as much as what reaches it.

Commands

aigis build "<description>"        # Generate governance brief (the main command)
aigis build "..." --list           # List areas without full brief
aigis build "..." --compact        # Pointer-only brief

aigis classify "<text>"            # Classify traits from description
aigis get <area>                   # Fetch governance procedure for an area
aigis infra <area>                 # Fetch infrastructure pattern (rate-limiting, secrets, logging)
aigis workflow <type>              # Fetch workflow template

aigis verify <area> --auto .       # Run deterministic checks on your implementation
aigis audit .                      # Summary audit of all implemented areas
aigis search --list                # List all available areas

Run aigis --help for full options.

Supported agents

• Cursor
• Claude Code
• GitHub Copilot
• Windsurf

Run aigis init <ide> to set up your IDE rules. The resolver's trigger reference is embedded in the rules file with a checksum that automatically refreshes on aigis init --refresh.

Contributing

Aigis is built around a curated trigger map for classification. Trigger contributions are welcome, with one-sentence use-case justification per the template in .github/PULL_REQUEST_TEMPLATE/trigger_mapping.md.

See CONTRIBUTING.md for the full contributor workflow, including how to add new governance areas, workflows, or infrastructure patterns.

License

MIT