Huawei Cloud Functiongraph Function Create logo

Huawei Cloud Functiongraph Function Create

Organization
huaweicloud
huawei-cloud-functiongraph-function-create

Create and invoke FunctionGraph functions on Huawei Cloud. Supports VPC configuration, environment variables, synchronous and asynchronous function invocation. Use this skill when users ask to create, deploy, upload, or invoke cloud functions, serverless functions, or FunctionGraph functions. Triggered by keywords like "create function", "deploy function", "upload function", "invoke function", "create cloud function", "创建函数", "部署函数", "上传函数", "调用函数", "创建云函数", "FunctionGraph function", "serverless function", "function invocation".

Overview

Publisherhuaweicloud
Repositoryhuaweicloud-skills
Skill namehuawei-cloud-functiongraph-function-create
Stars
59
Forks
24
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huaweicloud on GitHub. Read the source before you install it.

Installation

Install the Huawei Cloud Functiongraph Function Create AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/computing/functiongraph/huawei-cloud-functiongraph-function-create
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huaweicloud/huaweicloud-skills.git /tmp/huaweicloud-skills
mkdir -p .claude/skills
cp -r /tmp/huaweicloud-skills/skills/computing/functiongraph/huawei-cloud-functiongraph-function-create .claude/skills/huawei-cloud-functiongraph-function-create
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Huawei Cloud Functiongraph Function Create in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Huawei Cloud Functiongraph Function Create on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Huawei Cloud Functiongraph Function Create is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

FunctionGraph Function Creation & Invocation Skill

Overview

This skill manages FunctionGraph functions on Huawei Cloud, supporting creation with advanced configuration (VPC, environment variables) and function invocation (sync/async).

Architecture: Uses Huawei Cloud FunctionGraph Python SDK to interact with FunctionGraph service.

Applicable Scenarios:

  • Quickly create cloud functions without manual console login
  • Batch creation of multiple functions with VPC or environment variable configuration
  • Foundation component for workflow deployment
  • CI/CD integration for function deployment
  • Invoke and test deployed functions

Typical Use Cases:

  1. "Create a Python function named my_handler"
  2. "Deploy this code to FunctionGraph with VPC configuration"
  3. "Create a function with environment variables"
  4. "Invoke the function I just created"

Prerequisites

1. Python Environment

  • Python 3.9+
  • Verify: python --version

2. SDK Installation

bash
pip install huaweicloudsdkfunctiongraph

3. Authentication Configuration

  • Valid Huawei Cloud credentials (AK/SK mode)
  • Configure via environment variables:
bash
export HUAWEI_AK="your-access-key"
export HUAWEI_SK="your-secret-key"
export HUAWEI_REGION="cn-north-4"
export HUAWEI_PROJECT_ID="your-project-id"

Security Rules:

  • NEVER expose AK/SK values in code or logs
  • NEVER let users input AK/SK directly in conversation
  • ALWAYS use environment variables for credentials
  • Recommend using IAM user instead of main account

4. IAM Permission Requirements

  • functiongraph:function:create - Create function
  • functiongraph:function:get - Query function details
  • functiongraph:function:list - List functions
  • functiongraph:function:invoke - Invoke function

See IAM Policies for detailed permission configuration.

Workflow

User Request → Parse Parameters → Validate Input
  → Create Function (with optional VPC/Env Vars) → Return Function URN
  → (Optional) Invoke Function → Return Invocation Result

Usage

Create a Function

bash
cd scripts

# Basic creation
python create_function.py create \
  --name my_function \
  --runtime Python3.9 \
  --handler index.handler \
  --code main.py

# With VPC configuration
python create_function.py create \
  --name my_function \
  --runtime Python3.9 \
  --handler index.handler \
  --code main.py \
  --vpc-id vpc-xxx \
  --subnet-id subnet-xxx \
  --security-group-id sg-xxx

# With environment variables
python create_function.py create \
  --name my_function \
  --runtime Python3.9 \
  --handler index.handler \
  --code main.py \
  --env-vars '{"DB_HOST":"localhost","DB_PORT":"3306"}'

# Full configuration
python create_function.py create \
  --name my_function \
  --runtime Python3.9 \
  --handler index.handler \
  --code main.py \
  --memory 256 \
  --timeout 30 \
  --description "My production function" \
  --vpc-id vpc-xxx \
  --subnet-id subnet-xxx \
  --security-group-id sg-xxx \
  --env-vars '{"DEBUG":"true","LOG_LEVEL":"info"}'

Invoke a Function

Note: The function URN uses the real FunctionGraph URN prefix urn:fss:. Replace PROJECT_ID in the examples below with your actual project ID (the same value as the HUAWEI_PROJECT_ID environment variable). The function package is default and the version is latest (or a specific version/alias). You can copy the URN returned by the create command and append :latest for invocation.

bash
# Synchronous invocation (default)
python create_function.py invoke \
  --function-urn urn:fss:cn-north-4:PROJECT_ID:function:default:my_function:latest \
  --body '{"key1":"value1","source":"test"}'

# Asynchronous invocation
python create_function.py invoke \
  --function-urn urn:fss:cn-north-4:PROJECT_ID:function:default:my_function:latest \
  --body '{"async":true}' \
  --invocation-type async

# Invoke with log tail (returns last 4KB of log)
python create_function.py invoke \
  --function-urn urn:fss:cn-north-4:PROJECT_ID:function:default:my_function:latest \
  --body '{"test":"data"}' \
  --log-type tail

Parameter Confirmation

Create Parameters

ParameterRequired/OptionalDescriptionDefault
--nameRequiredFunction name, must follow naming rules-
--runtimeRequiredRuntime environment (Python3.9, etc.)-
--handlerRequiredFunction entry point (e.g., index.handler)-
--codeRequiredCode file path or inline code content-
--memoryOptionalMemory size in MB128
--timeoutOptionalTimeout in seconds3
--descriptionOptionalFunction description-
--vpc-idOptionalVPC ID for VPC configuration (must be set together with --subnet-id and --security-group-id)-
--subnet-idOptionalSubnet ID for VPC configuration (must be set together with --vpc-id and --security-group-id)-
--security-group-idOptionalSecurity group ID for VPC configuration (must be set together with --vpc-id and --subnet-id)-
--env-varsOptionalEnvironment variables as JSON string-

Invoke Parameters

ParameterRequired/OptionalDescriptionDefault
--function-urnRequiredFunction URN to invoke-
--bodyOptionalEvent payload as JSON string{}
--invocation-typeOptionalsync or asyncsync
--log-typeOptionaltail to return last 4KB log-

Runtime Options

RuntimeHandler FormatExample
Python3.9index.handlerdef handler(event, context):
Node.js14.18index.handlerexports.handler = (event, context) => {}
Java8com.example.Handler::handleRequestJava class method
Go1.xhandlerGo function name

Verification Method

See Verification Method for detailed procedures.

Quick Verification

python
# Verify function exists using SDK

from huaweicloudsdkfunctiongraph.v2.functiongraph_client import FunctionGraphClient
from huaweicloudsdkfunctiongraph.v2.model import ListFunctionsRequest

# List functions to verify creation
client = FunctionGraphClient.new_builder().build()
request = ListFunctionsRequest()
response = client.list_functions(request)
print(f"Total functions: {len(response.functions)}")

Output Format

Create Output

The script returns a structured JSON object:

json
{
  "status": "success",
  "function_urn": "urn:fss:cn-north-4:project_id:function:default:my_function",
  "function_name": "my_function",
  "runtime": "Python3.9",
  "memory_size": 128,
  "timeout": 3,
  "handler": "index.handler",
  "code_size": 1234,
  "message": "Function created successfully"
}

Invoke Output

json
{
  "status": "success",
  "function_urn": "urn:fss:cn-north-4:project_id:function:default:my_function",
  "invocation_type": "sync",
  "result": {
    "output": "..."
  },
  "request_id": "a1b2c3d4-...",
  "message": "Function invoked successfully"
}

Best Practices

  1. Test before production: Always test in a development environment first
  2. Monitor resources: Set up monitoring for function invocations and errors
  3. Use environment variables: Store sensitive data in environment variables
  4. Implement error handling: Add proper error handling in function code
  5. Set appropriate timeouts: Configure timeout based on function logic
  6. VPC for private resources: Use VPC configuration when functions need access to private network resources

Error Handling

Error CodeDescriptionResolution
InvalidParameterInvalid input parametersCheck parameter format and values
InsufficientPermissionInsufficient permissionsCheck IAM permissions
QuotaExceededResource quota exceededRequest quota increase or delete unused functions
FunctionAlreadyExistsFunction with same name existsUse different function name or delete existing function
FunctionNotFoundFunction URN not found (invoke)Verify the function URN is correct

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Huawei Cloud Functiongraph Function Create AI skill do?

Create and invoke FunctionGraph functions on Huawei Cloud. Supports VPC configuration, environment variables, synchronous and asynchronous function invocation. Use this skill when users ask to create, deploy, upload, or invoke cloud functions, serverless functions, or FunctionGraph functions. Triggered by keywords like "create function", "deploy function", "upload function", "invoke function", "create cloud function", "创建函数", "部署函数", "上传函数", "调用函数", "创建云函数", "FunctionGraph function", "serverless function", "function invocation".

Why use Huawei Cloud Functiongraph Function Create on TypingMind?

Because you install it once and use it with any model. Huawei Cloud Functiongraph Function Create is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Huawei Cloud Functiongraph Function Create in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/computing/functiongraph/huawei-cloud-functiongraph-function-create. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Huawei Cloud Functiongraph Function Create?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Huawei Cloud Functiongraph Function Create?

As many as you like. As long as a model supports skills, you can use Huawei Cloud Functiongraph Function Create with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Huawei Cloud Functiongraph Function Create AI skill free?

Yes. It is published on GitHub by huaweicloud under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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