Autonomous Agent Patterns V2

🕹️ Autonomous Agent Patterns

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/autonomous-agent-patterns from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

🕹️ Autonomous Agent Patterns > Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: 1. Core Agent Architecture, 2. Tool Design Patterns, 3. Permission & Safety Patterns, 4. Browser Automation, 5. Context Management, 6. MCP (Model Context Protocol) Integration.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Building autonomous AI agents
• Designing tool/function calling APIs
• Implementing permission and approval systems
• Creating browser automation for agents
• Designing human-in-the-loop workflows
• Use when the request clearly matches the imported source intent: Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: 1. Core Agent Architecture

1.1 Agent Loop

┌─────────────────────────────────────────────────────────────┐
│                     AGENT LOOP                               │
│                                                              │
│  ┌──────────┐    ┌──────────┐    ┌──────────┐              │
│  │  Think   │───▶│  Decide  │───▶│   Act    │              │
│  │ (Reason) │    │ (Plan)   │    │ (Execute)│              │
│  └──────────┘    └──────────┘    └──────────┘              │
│       ▲                               │                     │
│       │         ┌──────────┐          │                     │
│       └─────────│ Observe  │◀─────────┘                     │
│                 │ (Result) │                                │
│                 └──────────┘                                │
└─────────────────────────────────────────────────────────────┘

class AgentLoop:
    def __init__(self, llm, tools, max_iterations=50):
        self.llm = llm
        self.tools = {t.name: t for t in tools}
        self.max_iterations = max_iterations
        self.history = []

    def run(self, task: str) -> str:
        self.history.append({"role": "user", "content": task})

        for i in range(self.max_iterations):
            # Think: Get LLM response with tool options
            response = self.llm.chat(
                messages=self.history,
                tools=self._format_tools(),
                tool_choice="auto"
            )

            # Decide: Check if agent wants to use a tool
            if response.tool_calls:
                for tool_call in response.tool_calls:
                    # Act: Execute the tool
                    result = self._execute_tool(tool_call)

                    # Observe: Add result to history
                    self.history.append({
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": str(result)
                    })
            else:
                # No more tool calls = task complete
                return response.content

        return "Max iterations reached"

    def _execute_tool(self, tool_call) -> Any:
        tool = self.tools[tool_call.name]
        args = json.loads(tool_call.arguments)
        return tool.execute(**args)

1.2 Multi-Model Architecture

class MultiModelAgent:
    """
    Use different models for different purposes:
    - Fast model for planning
    - Powerful model for complex reasoning
    - Specialized model for code generation
    """

    def __init__(self):
        self.models = {
            "fast": "gpt-3.5-turbo",      # Quick decisions
            "smart": "gpt-4-turbo",        # Complex reasoning
            "code": "claude-3-sonnet",     # Code generation
        }

    def select_model(self, task_type: str) -> str:
        if task_type == "planning":
            return self.models["fast"]
        elif task_type == "analysis":
            return self.models["smart"]
        elif task_type == "code":
            return self.models["code"]
        return self.models["smart"]

Examples

Example 1: Ask for the upstream workflow directly

Use @autonomous-agent-patterns-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @autonomous-agent-patterns-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @autonomous-agent-patterns-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @autonomous-agent-patterns-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/autonomous-agent-patterns, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Resources

• Cline
• OpenAI Codex
• Model Context Protocol
• Anthropic Tool Use

Imported: 2. Tool Design Patterns

2.1 Tool Schema

class Tool:
    """Base class for agent tools"""

    @property
    def schema(self) -> dict:
        """JSON Schema for the tool"""
        return {
            "name": self.name,
            "description": self.description,
            "parameters": {
                "type": "object",
                "properties": self._get_parameters(),
                "required": self._get_required()
            }
        }

    def execute(self, **kwargs) -> ToolResult:
        """Execute the tool and return result"""
        raise NotImplementedError

class ReadFileTool(Tool):
    name = "read_file"
    description = "Read the contents of a file from the filesystem"

    def _get_parameters(self):
        return {
            "path": {
                "type": "string",
                "description": "Absolute path to the file"
            },
            "start_line": {
                "type": "integer",
                "description": "Line to start reading from (1-indexed)"
            },
            "end_line": {
                "type": "integer",
                "description": "Line to stop reading at (inclusive)"
            }
        }

    def _get_required(self):
        return ["path"]

    def execute(self, path: str, start_line: int = None, end_line: int = None) -> ToolResult:
        try:
            with open(path, 'r') as f:
                lines = f.readlines()

            if start_line and end_line:
                lines = lines[start_line-1:end_line]

            return ToolResult(
                success=True,
                output="".join(lines)
            )
        except FileNotFoundError:
            return ToolResult(
                success=False,
                error=f"File not found: {path}"
            )

2.2 Essential Agent Tools

CODING_AGENT_TOOLS = {
    # File operations
    "read_file": "Read file contents",
    "write_file": "Create or overwrite a file",
    "edit_file": "Make targeted edits to a file",
    "list_directory": "List files and folders",
    "search_files": "Search for files by pattern",

    # Code understanding
    "search_code": "Search for code patterns (grep)",
    "get_definition": "Find function/class definition",
    "get_references": "Find all references to a symbol",

    # Terminal
    "run_command": "Execute a shell command",
    "read_output": "Read command output",
    "send_input": "Send input to running command",

    # Browser (optional)
    "open_browser": "Open URL in browser",
    "click_element": "Click on page element",
    "type_text": "Type text into input",
    "screenshot": "Capture screenshot",

    # Context
    "ask_user": "Ask the user a question",
    "search_web": "Search the web for information"
}

2.3 Edit Tool Design

class EditFileTool(Tool):
    """
    Precise file editing with conflict detection.
    Uses search/replace pattern for reliable edits.
    """

    name = "edit_file"
    description = "Edit a file by replacing specific content"

    def execute(
        self,
        path: str,
        search: str,
        replace: str,
        expected_occurrences: int = 1
    ) -> ToolResult:
        """
        Args:
            path: File to edit
            search: Exact text to find (must match exactly, including whitespace)
            replace: Text to replace with
            expected_occurrences: How many times search should appear (validation)
        """
        with open(path, 'r') as f:
            content = f.read()

        # Validate
        actual_occurrences = content.count(search)
        if actual_occurrences != expected_occurrences:
            return ToolResult(
                success=False,
                error=f"Expected {expected_occurrences} occurrences, found {actual_occurrences}"
            )

        if actual_occurrences == 0:
            return ToolResult(
                success=False,
                error="Search text not found in file"
            )

        # Apply edit
        new_content = content.replace(search, replace)

        with open(path, 'w') as f:
            f.write(new_content)

        return ToolResult(
            success=True,
            output=f"Replaced {actual_occurrences} occurrence(s)"
        )

Imported: 3. Permission & Safety Patterns

3.1 Permission Levels

class PermissionLevel(Enum):
    # Fully automatic - no user approval needed
    AUTO = "auto"

    # Ask once per session
    ASK_ONCE = "ask_once"

    # Ask every time
    ASK_EACH = "ask_each"

    # Never allow
    NEVER = "never"

PERMISSION_CONFIG = {
    # Low risk - can auto-approve
    "read_file": PermissionLevel.AUTO,
    "list_directory": PermissionLevel.AUTO,
    "search_code": PermissionLevel.AUTO,

    # Medium risk - ask once
    "write_file": PermissionLevel.ASK_ONCE,
    "edit_file": PermissionLevel.ASK_ONCE,

    # High risk - ask each time
    "run_command": PermissionLevel.ASK_EACH,
    "delete_file": PermissionLevel.ASK_EACH,

    # Dangerous - never auto-approve
    "sudo_command": PermissionLevel.NEVER,
    "format_disk": PermissionLevel.NEVER
}

3.2 Approval UI Pattern

class ApprovalManager:
    def __init__(self, ui, config):
        self.ui = ui
        self.config = config
        self.session_approvals = {}

    def request_approval(self, tool_name: str, args: dict) -> bool:
        level = self.config.get(tool_name, PermissionLevel.ASK_EACH)

        if level == PermissionLevel.AUTO:
            return True

        if level == PermissionLevel.NEVER:
            self.ui.show_error(f"Tool '{tool_name}' is not allowed")
            return False

        if level == PermissionLevel.ASK_ONCE:
            if tool_name in self.session_approvals:
                return self.session_approvals[tool_name]

        # Show approval dialog
        approved = self.ui.show_approval_dialog(
            tool=tool_name,
            args=args,
            risk_level=self._assess_risk(tool_name, args)
        )

        if level == PermissionLevel.ASK_ONCE:
            self.session_approvals[tool_name] = approved

        return approved

    def _assess_risk(self, tool_name: str, args: dict) -> str:
        """Analyze specific call for risk level"""
        if tool_name == "run_command":
            cmd = args.get("command", "")
            if any(danger in cmd for danger in ["rm -rf", "sudo", "chmod"]):
                return "HIGH"
        return "MEDIUM"

3.3 Sandboxing

class SandboxedExecution:
    """
    Execute code/commands in isolated environment
    """

    def __init__(self, workspace_dir: str):
        self.workspace = workspace_dir
        self.allowed_commands = ["npm", "python", "node", "git", "ls", "cat"]
        self.blocked_paths = ["/etc", "/usr", "/bin", os.path.expanduser("~")]

    def validate_path(self, path: str) -> bool:
        """Ensure path is within workspace"""
        real_path = os.path.realpath(path)
        workspace_real = os.path.realpath(self.workspace)
        return real_path.startswith(workspace_real)

    def validate_command(self, command: str) -> bool:
        """Check if command is allowed"""
        cmd_parts = shlex.split(command)
        if not cmd_parts:
            return False

        base_cmd = cmd_parts[0]
        return base_cmd in self.allowed_commands

    def execute_sandboxed(self, command: str) -> ToolResult:
        if not self.validate_command(command):
            return ToolResult(
                success=False,
                error=f"Command not allowed: {command}"
            )

        # Execute in isolated environment
        result = subprocess.run(
            command,
            shell=True,
            cwd=self.workspace,
            capture_output=True,
            timeout=30,
            env={
                **os.environ,
                "HOME": self.workspace,  # Isolate home directory
            }
        )

        return ToolResult(
            success=result.returncode == 0,
            output=result.stdout.decode(),
            error=result.stderr.decode() if result.returncode != 0 else None
        )

Imported: 4. Browser Automation

4.1 Browser Tool Pattern

class BrowserTool:
    """
    Browser automation for agents using Playwright/Puppeteer.
    Enables visual debugging and web testing.
    """

    def __init__(self, headless: bool = True):
        self.browser = None
        self.page = None
        self.headless = headless

    async def open_url(self, url: str) -> ToolResult:
        """Navigate to URL and return page info"""
        if not self.browser:
            self.browser = await playwright.chromium.launch(headless=self.headless)
            self.page = await self.browser.new_page()

        await self.page.goto(url)

        # Capture state
        screenshot = await self.page.screenshot(type='png')
        title = await self.page.title()

        return ToolResult(
            success=True,
            output=f"Loaded: {title}",
            metadata={
                "screenshot": base64.b64encode(screenshot).decode(),
                "url": self.page.url
            }
        )

    async def click(self, selector: str) -> ToolResult:
        """Click on an element"""
        try:
            await self.page.click(selector, timeout=5000)
            await self.page.wait_for_load_state("networkidle")

            screenshot = await self.page.screenshot()
            return ToolResult(
                success=True,
                output=f"Clicked: {selector}",
                metadata={"screenshot": base64.b64encode(screenshot).decode()}
            )
        except TimeoutError:
            return ToolResult(
                success=False,
                error=f"Element not found: {selector}"
            )

    async def type_text(self, selector: str, text: str) -> ToolResult:
        """Type text into an input"""
        await self.page.fill(selector, text)
        return ToolResult(success=True, output=f"Typed into {selector}")

    async def get_page_content(self) -> ToolResult:
        """Get accessible text content of the page"""
        content = await self.page.evaluate("""
            () => {
                // Get visible text
                const walker = document.createTreeWalker(
                    document.body,
                    NodeFilter.SHOW_TEXT,
                    null,
                    false
                );

                let text = '';
                while (walker.nextNode()) {
                    const node = walker.currentNode;
                    if (node.textContent.trim()) {
                        text += node.textContent.trim() + '\\n';
                    }
                }
                return text;
            }
        """)
        return ToolResult(success=True, output=content)

4.2 Visual Agent Pattern

class VisualAgent:
    """
    Agent that uses screenshots to understand web pages.
    Can identify elements visually without selectors.
    """

    def __init__(self, llm, browser):
        self.llm = llm
        self.browser = browser

    async def describe_page(self) -> str:
        """Use vision model to describe current page"""
        screenshot = await self.browser.screenshot()

        response = self.llm.chat([
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Describe this webpage. List all interactive elements you see."},
                    {"type": "image", "data": screenshot}
                ]
            }
        ])

        return response.content

    async def find_and_click(self, description: str) -> ToolResult:
        """Find element by visual description and click it"""
        screenshot = await self.browser.screenshot()

        # Ask vision model to find element
        response = self.llm.chat([
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": f"""
                        Find the element matching: "{description}"
                        Return the approximate coordinates as JSON: {{"x": number, "y": number}}
                        """
                    },
                    {"type": "image", "data": screenshot}
                ]
            }
        ])

        coords = json.loads(response.content)
        await self.browser.page.mouse.click(coords["x"], coords["y"])

        return ToolResult(success=True, output=f"Clicked at ({coords['x']}, {coords['y']})")

Imported: 5. Context Management

5.1 Context Injection Patterns

class ContextManager:
    """
    Manage context provided to the agent.
    Inspired by Cline's @-mention patterns.
    """

    def __init__(self, workspace: str):
        self.workspace = workspace
        self.context = []

    def add_file(self, path: str) -> None:
        """@file - Add file contents to context"""
        with open(path, 'r') as f:
            content = f.read()

        self.context.append({
            "type": "file",
            "path": path,
            "content": content
        })

    def add_folder(self, path: str, max_files: int = 20) -> None:
        """@folder - Add all files in folder"""
        for root, dirs, files in os.walk(path):
            for file in files[:max_files]:
                file_path = os.path.join(root, file)
                self.add_file(file_path)

    def add_url(self, url: str) -> None:
        """@url - Fetch and add URL content"""
        response = requests.get(url)
        content = html_to_markdown(response.text)

        self.context.append({
            "type": "url",
            "url": url,
            "content": content
        })

    def add_problems(self, diagnostics: list) -> None:
        """@problems - Add IDE diagnostics"""
        self.context.append({
            "type": "diagnostics",
            "problems": diagnostics
        })

    def format_for_prompt(self) -> str:
        """Format all context for LLM prompt"""
        parts = []
        for item in self.context:
            if item["type"] == "file":
                parts.append(f"## File: {item['path']}\n```\n{item['content']}\n```")
            elif item["type"] == "url":
                parts.append(f"## URL: {item['url']}\n{item['content']}")
            elif item["type"] == "diagnostics":
                parts.append(f"## Problems:\n{json.dumps(item['problems'], indent=2)}")

        return "\n\n".join(parts)

5.2 Checkpoint/Resume

class CheckpointManager:
    """
    Save and restore agent state for long-running tasks.
    """

    def __init__(self, storage_dir: str):
        self.storage_dir = storage_dir
        os.makedirs(storage_dir, exist_ok=True)

    def save_checkpoint(self, session_id: str, state: dict) -> str:
        """Save current agent state"""
        checkpoint = {
            "timestamp": datetime.now().isoformat(),
            "session_id": session_id,
            "history": state["history"],
            "context": state["context"],
            "workspace_state": self._capture_workspace(state["workspace"]),
            "metadata": state.get("metadata", {})
        }

        path = os.path.join(self.storage_dir, f"{session_id}.json")
        with open(path, 'w') as f:
            json.dump(checkpoint, f, indent=2)

        return path

    def restore_checkpoint(self, checkpoint_path: str) -> dict:
        """Restore agent state from checkpoint"""
        with open(checkpoint_path, 'r') as f:
            checkpoint = json.load(f)

        return {
            "history": checkpoint["history"],
            "context": checkpoint["context"],
            "workspace": self._restore_workspace(checkpoint["workspace_state"]),
            "metadata": checkpoint["metadata"]
        }

    def _capture_workspace(self, workspace: str) -> dict:
        """Capture relevant workspace state"""
        # Git status, file hashes, etc.
        return {
            "git_ref": subprocess.getoutput(f"cd {workspace} && git rev-parse HEAD"),
            "git_dirty": subprocess.getoutput(f"cd {workspace} && git status --porcelain")
        }

Imported: 6. MCP (Model Context Protocol) Integration

6.1 MCP Server Pattern

from mcp import Server, Tool

class MCPAgent:
    """
    Agent that can dynamically discover and use MCP tools.
    'Add a tool that...' pattern from Cline.
    """

    def __init__(self, llm):
        self.llm = llm
        self.mcp_servers = {}
        self.available_tools = {}

    def connect_server(self, name: str, config: dict) -> None:
        """Connect to an MCP server"""
        server = Server(config)
        self.mcp_servers[name] = server

        # Discover tools
        tools = server.list_tools()
        for tool in tools:
            self.available_tools[tool.name] = {
                "server": name,
                "schema": tool.schema
            }

    async def create_tool(self, description: str) -> str:
        """
        Create a new MCP server based on user description.
        'Add a tool that fetches Jira tickets'
        """
        # Generate MCP server code
        code = self.llm.generate(f"""
        Create a Python MCP server with a tool that does:
        {description}

        Use the FastMCP framework. Include proper error handling.
        Return only the Python code.
        """)

        # Save and install
        server_name = self._extract_name(description)
        path = f"./mcp_servers/{server_name}/server.py"

        with open(path, 'w') as f:
            f.write(code)

        # Hot-reload
        self.connect_server(server_name, {"path": path})

        return f"Created tool: {server_name}"

Imported: Best Practices Checklist

Agent Design

• Clear task decomposition
• Appropriate tool granularity
• Error handling at each step
• Progress visibility to user

Safety

• Permission system implemented
• Dangerous operations blocked
• Sandbox for untrusted code
• Audit logging enabled

UX

• Approval UI is clear
• Progress updates provided
• Undo/rollback available
• Explanation of actions

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.