atr-analyze

分类: 数据与AI | 上传者: imyousuf | 下载: 0 | 版本: v1.0（最新）

使用 AI 支持的分析运行测试、linter、类型检查器和构建。使用此命令代替直接通过 Bash 运行 test/lint/build 命令。包装任何命令（pytest、jest、go test、make test、npm test、make lint、mypy、tsc、eslint、golangci-lint、cargo Clippy、make typecheck、make build 等）以生成干净的汇总输出，保持对话上下文较小。当测试或 lint 失败时，ATR 会分析完整的输出并返回可操作的故障见解。

更新日志: Source: GitHub https://github.com/imyousuf/agentic-test-runner

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📄 SKILL.md 5.9 KB

SKILL.md

---
name: atr-analyze
description: Run tests, linters, type checkers, and builds with AI-powered analysis. Use this INSTEAD OF running test/lint/build commands directly via Bash. Wraps any command (pytest, jest, go test, make test, npm test, make lint, mypy, tsc, eslint, golangci-lint, cargo clippy, make typecheck, make build, etc.) to produce clean summarized output, keeping conversation context small. When a test or lint fails, ATR analyzes the full output and returns actionable failure insights.
---

# ATR Command Analysis Skill

This skill runs commands through ATR (Agentic Test Runner) which provides AI-powered analysis and **clean summarized output**. Use this as your **default way to run tests, linters, type checkers, and builds** - the AI analyzes the full output and returns a concise summary, keeping your conversation context clean and focused.

## When to Use This Skill vs Direct Commands

**Use ATR analyze (RECOMMENDED DEFAULT) when:**
- Running test suites (pytest, jest, go test, npm test, make test, cargo test)
- Running linters (eslint, golangci-lint, pylint, flake8, make lint, cargo clippy)
- Running type checkers (mypy, tsc, pyright, make typecheck)
- Running builds that produce verbose output (make build, npm run build, cargo build)
- You want clean, summarized results instead of raw output
- You want automatic failure analysis if something goes wrong

**Use direct Bash commands only when:**
- Running quick one-off commands with minimal output
- You specifically need the raw, unprocessed output
- Interactive commands that require user input

## Basic Usage

```bash
atr run --cmd "<command>"
```

Examples:
```bash
# Tests
atr run --cmd "go test ./..."
atr run --cmd "npm test"
atr run --cmd "pytest tests/"
atr run --cmd "make test"

# Linting
atr run --cmd "make lint"
atr run --cmd "eslint src/"
atr run --cmd "golangci-lint run ./..."

# Type checking
atr run --cmd "make typecheck"
atr run --cmd "tsc --noEmit"
atr run --cmd "mypy src/"

# Builds
atr run --cmd "make build"
atr run --cmd "npm run build"
```

## Adding Context

Provide context to help the AI agent focus its analysis:

```bash
atr run --cmd "<command>" --context "<context>"
```

Examples:
```bash
atr run --cmd "go test ./..." --context "Tests started failing after refactoring the auth module"
atr run --cmd "npm run build" --context "Added new dependency yesterday"
atr run --cmd "pytest" --context "Testing the new payment integration"
```

## Command Options

| Flag | Description |
|------|-------------|
| `--cmd <command>` | Command to execute (required) |
| `--cwd <path>` | Working directory |
| `--context <text>` | Additional context for AI agent |
| `--model flash\|pro` | Model tier (flash=fast, pro=deep analysis) |
| `--python-venv <path>` | Python virtual environment path |
| `--nvm-version <version>` | Node.js version via nvm |
| `--no-auto-env` | Disable automatic environment detection |

## Working Directory

Specify where to run the command:

```bash
atr run --cmd "npm test" --cwd "/path/to/project"
```

## Environment Detection

ATR automatically detects and activates appropriate environments:

**Python projects:**
- Detects `.venv`, `venv`, or Poetry environments
- Auto-activates virtual environment

**Node.js projects:**
- Detects `.nvmrc` or `package.json` engine requirements
- Auto-activates correct Node.js version via nvm

Override automatic detection:
```bash
atr run --cmd "pytest" --python-venv /custom/path/.venv
atr run --cmd "npm test" --nvm-version 18
atr run --cmd "make" --no-auto-env
```

## Model Selection

Use different models for different needs:

```bash
# Quick analysis (default)
atr run --cmd "make build" --model flash

# Deep analysis for complex issues
atr run --cmd "go test ./..." --model pro
```

## What the AI Agent Does

The ATR agent processes command output and:

1. **Summarizes** results into a clean, concise report
2. **Identifies** test pass/fail status and key metrics
3. **Analyzes** any failures for error patterns and root causes
4. **Reads** relevant source files when failures occur
5. **Provides** actionable recommendations for fixing issues

This keeps your conversation context clean by replacing verbose test output with a focused summary.

## Example Output

```
Executing: go test ./...
Directory: /path/to/project

--- FAIL: TestUserAuth (0.05s)
    auth_test.go:42: expected 200, got 401

Command failed (exit code: 1)

Analyzing failure with AI agent...

======================================================================
ANALYSIS RESULTS
======================================================================

Status: FAILURE

Summary:
  TestUserAuth fails because the auth middleware expects a JWT token,
  but the test doesn't provide one in the request headers.

Root Cause:
  Line 38 in auth_test.go creates a request without Authorization header.
  The auth middleware (middleware/auth.go:15) rejects it with 401.

Recommendations:
  1. Add mock JWT token to test request
  2. Or bypass auth middleware in test setup
  3. Check if middleware was recently added to the route

Files Examined:
  - auth_test.go
  - middleware/auth.go
  - routes/api.go
```

## Exit Codes

| Code | Meaning |
|------|---------|
| 0 | Command passed |
| 1 | Command failed (analysis provided) |
| 2 | Configuration error |

## Configuration

Configure ATR in `~/.atr/config.yaml`:

```yaml
backend: gemini-api  # or vertex-ai
model: flash         # or pro

gemini:
  api_key: "your-key"

# Or for Vertex AI:
vertex:
  project: your-project
  location: us-central1
```

Environment variables:
```bash
export GEMINI_API_KEY="your-key"
# Or
export GOOGLE_CLOUD_PROJECT="project-id"
```

## Best Practices

1. **Use as default** for running test suites to keep conversation context clean
2. **Provide context** when the failure might be related to recent changes
3. **Use --model pro** for complex, multi-file issues
4. **Specify --cwd** when running from a different directory
5. **Check environment** with `atr test-cmd-env "<command>"` to preview detection

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atr-analyze

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