# aiglare [Health: Active]

**Category:** 💻 Developer Tools  
**Repository:** https://github.com/nugehs/aiglare  
**GitHub Stars:** 1  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/aiglare

## Description
Audit AI/LLM features for governance guardrails: confidence, fallback, validation, human-in-loop.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "aiglare": {
    "command": "npx",
    "args": ["-y","@nugehs/aiglare"]
  }
}
```

## Documentation & README

# aiglare

**Lint your AI features for governance guardrails — where can the model do something you can't undo?**

[![npm](https://img.shields.io/npm/v/@nugehs/aiglare?style=flat-square)](https://www.npmjs.com/package/@nugehs/aiglare) [![CI](https://img.shields.io/github/actions/workflow/status/nugehs/aiglare/ci.yml?style=flat-square&label=CI)](https://github.com/nugehs/aiglare/actions/workflows/ci.yml) [![license: MIT](https://img.shields.io/badge/license-MIT-blue?style=flat-square)](LICENSE) [![node](https://img.shields.io/node/v/@nugehs/aiglare?style=flat-square)](https://www.npmjs.com/package/@nugehs/aiglare)

**Live site:** [nugehs.github.io/aiglare-web](https://nugehs.github.io/aiglare-web/)

![aiglare demo](https://raw.githubusercontent.com/nugehs/aiglare/HEAD/aiglare-demo.gif)

Point it at any JS/TS repo and it finds every place an LLM/AI output reaches a user or triggers a side-effect (payment, booking, email, database write) — then flags which of those have no confidence handling, no fallback, no output validation, and no human-in-the-loop.

Most AI incidents aren't model failures. They're governance failures: the model output flowed straight to a user or an irreversible action with nothing in between. This tool makes those paths visible, and lets you block them in CI.

```
npx @nugehs/aiglare                                        # audit current repo
npx @nugehs/aiglare ./src --ci                             # fail the build on a red side-effectful surface
npx @nugehs/aiglare --compliance all --format html         # HTML evidence report for auditors
npx @nugehs/aiglare --compliance soc2,eu-ai-act --format lint --ci  # compliance linter in CI
```

## What it reports

Each AI surface is classified by **sink** — where the output goes:

- `user-facing` — returned from a route/controller, or rendered in a component
- `side-effectful` — feeds a payment, booking, email, db/file write, or shell
- `internal` — logged or cached only

…and scored on five **guardrail** dimensions: confidence handling, fallback/uncertain path, output validation, human-in-the-loop (for side-effects), and error isolation.

| Severity | Meaning |
|----------|---------|
| 🔴 red   | model output hits a user or a side-effect with no guardrails — review now |
| 🟡 amber | partial coverage |
| 🟢 green | guardrails present |

The CI gate (`--ci`) fails only on **red + side-effectful** surfaces — the "AI auto-triggers an irreversible action with no confirmation" case — so it's safe to adopt without drowning a team in warnings.

## Compliance reports

Pass `--compliance` to map every finding to specific regulatory controls, then pick your output format:

```bash
# HTML evidence report — branded, collapsible per-framework sections, guardrail matrix
aiglare --compliance all --format html > report.html

# ESLint-style linter — pipe into CI, editors, or reviewers
aiglare --compliance soc2,eu-ai-act --format lint

# Markdown evidence doc — ready to hand to an auditor
aiglare --compliance all --format markdown > AUDIT.md

# Guardrail linter (no framework) — rule IDs like guardrail/human-in-loop
aiglare --format lint
```

**Supported frameworks:**

| Framework | Controls mapped |
|-----------|----------------|
| `soc2` | SOC 2 Trust Services Criteria — CC7.2, CC7.4, CC9.1, A1.2, PI1.2, PI1.3 |
| `eu-ai-act` | EU AI Act — Art. 9, 13, 14, 17 |
| `nist` | NIST AI RMF — GOVERN-1.2, MAP-2.3, MEASURE-2.5/2.6, MANAGE-1.3/2.2 |
| `owasp` | OWASP LLM Top 10 — LLM02, LLM04, LLM08, LLM09 |

Use `all` to include every framework. Each surface in `--json` output gains a `violations[]` array with the framework, control ID, description, and error/warning level.

**Multi-repo:** pass multiple paths for a single combined report:

```bash
aiglare ./api ./web --compliance all --format html > report.html
```

## Provider-agnostic

Detection is driven by a [provider registry](https://github.com/nugehs/aiglare/blob/HEAD/src/providers.js) covering OpenAI, Anthropic, Google, Cohere, Mistral, Replicate, the Vercel AI SDK, LangChain/LangGraph, Ollama, AWS Bedrock, Cloudflare Workers AI, and Hugging Face — plus raw `fetch`/`axios` calls to known inference hosts. Adding a provider is a one-line PR.

## Optional: repoctx acceleration

If a [repoctx](https://github.com/nugehs/repoctx) index (`.dev-context/index.json`) is present, the tool uses it automatically to prioritize likely AI files and sharpen sink classification via repoctx's `kind`/`domain` data (e.g. a file repoctx marks as a `controller` route is correctly treated as user-facing even when the native scanner can't see the call graph). Without it, a built-in TypeScript-compiler scanner does the same job at lower fidelity. **Same tool, two fidelity levels** — standalone for everyone, richer for repoctx users.

## MCP server

aiglare ships a built-in [Model Context Protocol](https://modelcontextprotocol.io) server so agents can run audits directly:

```bash
aiglare mcp        # stdio JSON-RPC server (no SDK dependency)
```

It exposes three tools:

| Tool | What it does |
|------|--------------|
| `ai_surface_audit` | Full audit of a repo (`path`, optional `sinks`, `severity`) → the same structured report as `--json` |
| `ai_surface_gate` | CI-gate verdict for a repo: `passed` + count of blocking red side-effectful surfaces |
| `list_providers` | The provider registry the scanner detects |

Register it with an MCP host (Claude Desktop, Cursor, VS Code, …):

```json
{
  "mcpServers": {
    "aiglare": {
      "command": "npx",
      "args": ["-y", "@nugehs/aiglare", "mcp"]
    }
  }
}
```

## aiglare vs alternatives

| Approach | What it does | Where aiglare differs |
| --- | --- | --- |
| guardrails-ai / NeMo Guardrails / runtime validators | Validate or correct each model output at runtime, per call | aiglare is static analysis: it finds the AI surfaces that have **no guardrail at all**, before anything runs — then you add a runtime validator there |
| semgrep / custom lint rules | General-purpose static rules you write and maintain yourself | aiglare ships the AI-specific knowledge out of the box: a provider registry, sink classification, and five guardrail dimensions — zero rule-writing |
| Manual AI-feature review | Catches nuance a scanner cannot | aiglare gives reviewers the complete inventory of AI surfaces and a severity triage, so review time goes where the risk is |

These are complementary: aiglare tells you *where* a guardrail is missing; runtime validators are *how* you add one.

## Run a 1-week pilot

Want to evaluate aiglare on a real codebase before adopting the CI gate? [PILOT.md](https://github.com/nugehs/aiglare/blob/HEAD/PILOT.md) is a step-by-step one-week runbook: install, first audit on a backend and a frontend repo, reading the report, tuning `--severity`/`--sinks`, and deciding whether to turn on `--ci`.

## Honest limitations

This is static, advisory analysis — a linter, not a verifier. It produces false positives (a guardrail two call-hops away can be missed) and false negatives (a `confidence` variable that doesn't actually gate anything reads as present). Treat output as *surfaces to review*, not *violations*. The single-file native scanner cannot follow the call graph; the repoctx adapter exists precisely to close that gap.

## Options

```
aiglare [path...] [options]
  --compliance <fw>   Map findings to compliance controls.
                      Comma-separated: soc2, eu-ai-act, nist, owasp, or "all"
  --format <fmt>      Output format: terminal (default), json, lint, markdown, html
  --json              JSON output (alias for --format json)
  --ci                Exit non-zero on a red side-effectful surface
  --severity <lvl>    Show only red, or amber-and-worse
  --sinks <list>      Filter: user-facing,side-effectful,internal

aiglare mcp           Start the MCP server (stdio)
```

The MCP tool `ai_surface_audit` accepts an optional `compliance` array — each surface in the response includes a `violations[]` array when set.

## License

MIT

---

## Part of the toolchain

**aiglare** is one of four tools that form a deterministic trust layer for AI-assisted development. Each answers a question people keep handing to an LLM — with static analysis instead.

- [repoctx](https://www.npmjs.com/package/@nugehs/repoctx) — context: what does this change actually touch?
- [tieline](https://www.npmjs.com/package/@nugehs/tieline) — contracts: did the front end and back end quietly stop agreeing?
- [bouncer](https://www.npmjs.com/package/@nugehs/bouncer) — compliance: could you defend this to Ofcom?
- **aiglare** (this tool) — governance: where can the model do something you can't undo?

More at [segunolumbe.com](https://segunolumbe.com). *static analysis, never the model.*