Process Local Files with an AI Agent

import json

input_data = json.loads(data)
file_path = input_data.get("path", "")

try:
    with open(file_path, "r") as f:
        content = f.read()
    result = {"content": content, "path": file_path, "size": len(content)}
except FileNotFoundError:
    result = {"error": f"File not found: {file_path}"}
except PermissionError:
    result = {"error": f"Permission denied: {file_path}"}

import os
import json

input_data = json.loads(data)
directory = input_data.get("path", ".")

try:
    entries = []
    for entry in os.listdir(directory):
        full_path = os.path.join(directory, entry)
        entries.append({
            "name": entry,
            "type": "directory" if os.path.isdir(full_path) else "file",
            "size": os.path.getsize(full_path) if os.path.isfile(full_path) else None,
        })
    result = {"entries": entries, "path": directory, "count": len(entries)}
except FileNotFoundError:
    result = {"error": f"Directory not found: {directory}"}

You are a codebase assistant with access to the local filesystem.
You can list directories and read files to help answer questions about code.

Available tools:
- list_files: Lists files in a directory. Pass {"path": "/absolute/path"}
- read_file: Reads a file's contents. Pass {"path": "/absolute/path/to/file"}

When asked about a project:
1. First list the directory to understand the structure
2. Read relevant files (README, main entry points, config files)
3. Provide clear, helpful analysis

Always use absolute paths. Be careful not to read binary files.

tensorify runner start

from openai import OpenAI

client = OpenAI(
    base_url="https://triggers.tensorify.io/h/YOUR_HOOK_PATH",
    api_key="your-tensorify-api-key",
)

response = client.chat.completions.create(
    model="tensorify",
    messages=[{
        "role": "user",
        "content": "Explore /home/user/projects/my-app and tell me what it does."
    }],
)

print(response.choices[0].message.content)

import os
import json

input_data = json.loads(data)
directory = input_data.get("path", ".")
pattern = input_data.get("pattern", "")

matches = []
for root, dirs, files in os.walk(directory):
    dirs[:] = [d for d in dirs if d not in ['.git', 'node_modules', '__pycache__', '.venv']]
    for fname in files:
        fpath = os.path.join(root, fname)
        try:
            with open(fpath, 'r') as f:
                for i, line in enumerate(f, 1):
                    if pattern in line:
                        matches.append({"file": fpath, "line": i, "content": line.strip()})
        except (UnicodeDecodeError, PermissionError):
            pass

result = {"matches": matches[:50], "pattern": pattern, "total": len(matches)}

import os
import json
from datetime import datetime

input_data = json.loads(data)
path = input_data.get("path", "")

stat = os.stat(path)
result = {
    "path": path,
    "size_bytes": stat.st_size,
    "modified": datetime.fromtimestamp(stat.st_mtime).isoformat(),
    "created": datetime.fromtimestamp(stat.st_ctime).isoformat(),
    "is_file": os.path.isfile(path),
    "is_dir": os.path.isdir(path),
}