"\ud83d\udd79\ufe0f Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off."
Instrucciones de origen · Vista previa de solo lectura
name
autonomous-agent-patterns
description
🕹️ Autonomous Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/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
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
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.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
classAgentLoop:
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 = []
defrun(self, task: str) -> str:
self.history.append({"role": "user", "content": task})
for i inrange(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 toolif response.tool_calls:
for tool_call in response.tool_calls:
# Act: Execute the tool
result = self._execute_tool(tool_call)
# Observe: Add result to historyself.history.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": str(result)
})
else:
# No more tool calls = task completereturn 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
classMultiModelAgent:
"""
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
}
defselect_model(self, task_type: str) -> str:
if task_type == "planning":
returnself.models["fast"]
elif task_type == "analysis":
returnself.models["smart"]
elif task_type == "code":
returnself.models["code"]
returnself.models["smart"]
Examples
Example 1: Ask for the upstream workflow directly
Use @autonomous-agent-patterns 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 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 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 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-claude/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.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
classTool:
"""Base class for agent tools""" @propertydefschema(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()
}
}
defexecute(self, **kwargs) -> ToolResult:
"""Execute the tool and return result"""raise NotImplementedError
classReadFileTool(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"]
defexecute(self, path: str, start_line: int = None, end_line: int = None) -> ToolResult:
try:
withopen(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
classEditFileTool(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"defexecute(
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)
"""withopen(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)
withopen(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
classPermissionLevel(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
classApprovalManager:
def__init__(self, ui, config):
self.ui = ui
self.config = config
self.session_approvals = {}
defrequest_approval(self, tool_name: str, args: dict) -> bool:
level = self.config.get(tool_name, PermissionLevel.ASK_EACH)
if level == PermissionLevel.AUTO:
returnTrueif level == PermissionLevel.NEVER:
self.ui.show_error(f"Tool '{tool_name}' is not allowed")
returnFalseif level == PermissionLevel.ASK_ONCE:
if tool_name inself.session_approvals:
returnself.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", "")
ifany(danger in cmd for danger in ["rm -rf", "sudo", "chmod"]):
return"HIGH"return"MEDIUM"
from mcp import Server, Tool
classMCPAgent:
"""
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 = {}
defconnect_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
}
asyncdefcreate_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"withopen(path, 'w') as f:
f.write(code)
# Hot-reloadself.connect_server(server_name, {"path": path})
returnf"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.
