| name | alibabacloud-analyticdb-postgresql-ai-coaching-best-practice |
| description | Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development.
Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG.
Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"
|
ADBPG AI Coaching Best Practice
Build RAG-driven coaching systems using ADBPG Supabase (training data management) + ADBPG Vector Knowledge Base (RAG-driven intelligent coaching).
Core Capabilities:
- Supabase Project: PostgreSQL-based platform for managing coaching domains, learners, and session records
- Vector Knowledge Base: ADBPG instance with vector optimization for RAG-driven coaching
- Seamless Integration: Supabase stores structured data, ADBPG powers intelligent AI coaching dialogue
Pre-check: Aliyun CLI >= 3.3.3 Required
IMPORTANT: Run aliyun version to verify >= 3.3.3. If not installed or version too low,
run curl -fsSL https://aliyuncli.alicdn.com/setup.sh | bash to update,
or see references/cli-installation-guide.md for installation instructions.
Then [MUST] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation.
[MUST] run aliyun plugin update to ensure that any existing plugins on your local machine are always up-to-date.
[MUST] CLI User-Agent
Every aliyun CLI command invocation must include:
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
[MUST] CLI AI-Mode Configuration
At the start of the Core Workflow (before any CLI invocation):
[MUST] Enable AI-Mode — AI-mode is required for Agent Skill execution.
Run the following commands before any CLI invocation:
aliyun configure ai-mode enable
aliyun configure ai-mode set-user-agent --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice"
[MUST] Disable AI-Mode at EVERY exit point — Before delivering the final response for ANY reason, always disable AI-mode first. This applies to ALL exit paths: workflow success, workflow failure, error/exception, user cancellation, session end, or any other scenario where no further CLI commands will be executed.
AI-mode is only used for Agent Skill invocation scenarios and MUST NOT remain enabled after the skill stops running.
aliyun configure ai-mode disable
Pre-check: Alibaba Cloud Credentials Required
Security Rules:
- NEVER read, echo, or print AK/SK values
- NEVER ask the user to input AK/SK directly
- NEVER print passwords or API Keys in plain text in logs or stdout
- ONLY use
aliyun configure list to check credential status
- When displaying API Keys, show only the first 6 characters +
*** (e.g., sk-abc1***)
aliyun configure list
If no valid profile exists, STOP here. Configure credentials outside of this session via aliyun configure or environment variables.
Scenario Description
| Scenario | Use Case | Target Users |
|---|
| Workflow Coaching | Guide professionals through structured business processes (sales cycles, project management) | Sales teams, project managers |
| Decision Support | Help engineers evaluate trade-offs and make informed technical decisions | Engineers, architects |
| Skill Development | Develop communication, negotiation, or technical skills through guided practice | Professionals, new hires |
| Onboarding | Systematically guide new team members through technical and process onboarding | New employees, mentors |
Architecture
User (Web / Terminal / Agent)
│
┌──────┴──────┐
v v
┌─────────────┐ ┌────────────────────────┐
│ Supabase │ │ Agent Mode │
│ (spb-xxx) │ │ ChatWithKnowledgeBase │
│ - Domains │ └───────────┬────────────┘
│ - Sessions │ │
└──────┬──────┘ │
v v
┌────────────────────────────────────────┐
│ ADBPG Instance (gp-xxx) + KB │
│ Domain Knowledge + RAG + LLM │
└────────────────────────────────────────┘
RAM Policy
Required Permissions
| Operation | RAM Permission |
|---|
| Supabase Project Management | gpdb:CreateSupabaseProject, gpdb:GetSupabaseProject, gpdb:ModifySupabaseProjectSecurityIps |
| ADBPG Instance Management | gpdb:CreateDBInstance, gpdb:DescribeDBInstances, gpdb:ModifySecurityIps |
| Account Management | gpdb:DescribeAccounts, gpdb:CreateAccount |
| Knowledge Base Operations | gpdb:InitVectorDatabase, gpdb:CreateNamespace, gpdb:CreateDocumentCollection, gpdb:UploadDocumentAsync, gpdb:ChatWithKnowledgeBase |
| VPC Network | vpc:DescribeVpcs, vpc:DescribeVSwitches, vpc:DescribeVSwitchAttributes |
| NAT Gateway & EIP | vpc:DescribeNatGateways, vpc:CreateNatGateway, vpc:DescribeEipAddresses, vpc:AllocateEipAddress, vpc:AssociateEipAddress, vpc:CreateSnatEntry |
Recommended System Policies: AliyunGPDBFullAccess, AliyunVPCFullAccess (or AliyunVPCReadOnlyAccess if NAT already exists)
See references/ram-policies.md for complete list.
[MUST] Permission Failure Handling: When any command fails due to permission errors:
- Read references/ram-policies.md for required permissions
- Use
ram-permission-diagnose skill to guide the user
- Pause and wait until user confirms permissions granted
Core Workflow
When user says "Help me set up an AI coaching system" or similar, execute the following steps:
Smart Defaults Mode: User only needs minimal input (e.g., "北京i"). The agent auto-parses region, discovers VPC/VSwitch, generates passwords, and presents all parameters for one-click confirmation.
Step 1: Create Supabase Project
Parameters to confirm for this step:
| Parameter | Default | Notes |
|---|
RegionId | Auto-parse | "北京i" → cn-beijing, "上海b" → cn-shanghai, "杭州" → cn-hangzhou, "深圳" → cn-shenzhen |
ZoneId | Auto-parse | "北京i" → cn-beijing-i; query zones when only city provided |
VpcId | Auto-discover | Query available VPCs, select one with most available IPs |
VSwitchId | Auto-discover | Query VSwitches in target zone, select one with most available IPs |
ProjectName | ai_coaching | Supabase project name |
AccountPassword | Auto-generate | Password rules: 8-32 chars, at least 3 of uppercase/lowercase/digits/special (@#$%^&*), avoid ! |
1.1 Check/Create NAT Gateway
Important: Supabase public connection requires a NAT Gateway with SNAT rules in the VPC.
aliyun vpc describe-nat-gateways --profile adbpg \
--biz-region-id <RegionId> --vpc-id <VpcId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
- If
TotalCount > 0 and SNAT entries cover the VSwitch CIDR → Skip to Step 1.2
- If no NAT Gateway → Get user confirmation, then:
aliyun vpc describe-vswitch-attributes --profile adbpg \
--biz-region-id <RegionId> --vswitch-id <VSwitchId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun vpc create-nat-gateway --profile adbpg \
--biz-region-id <RegionId> --vpc-id <VpcId> --vswitch-id <VSwitchId> \
--nat-type Enhanced \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun vpc describe-eip-addresses --profile adbpg \
--biz-region-id <RegionId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun vpc allocate-eip-address --profile adbpg \
--biz-region-id <RegionId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun vpc associate-eip-address --profile adbpg \
--biz-region-id <RegionId> \
--allocation-id <EIP-AllocationId> --instance-id <NatGatewayId> \
--instance-type Nat \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun vpc create-snat-entry --profile adbpg \
--biz-region-id <RegionId> \
--snat-table-id <SnatTableId> \
--source-cidr "<VSwitch-CidrBlock>" --snat-ip "<EipAddress>" \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
1.2 Create Supabase Project
aliyun gpdb create-supabase-project --profile adbpg \
--biz-region-id <RegionId> --zone-id <ZoneId> \
--project-name <ProjectName> --account-password '<AccountPassword>' \
--security-ip-list "127.0.0.1" --vpc-id <VpcId> --vswitch-id <VSwitchId> \
--project-spec 2C4G --storage-size 20 --pay-type Postpaid \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Record: ProjectId (sbp-xxx), PublicConnectUrl, API Keys (store securely; do NOT print full API Keys in logs)
Timeout: Supabase project creation takes 5-10 minutes. Poll status until running:
aliyun gpdb get-supabase-project --profile adbpg \
--biz-region-id <RegionId> --project-id <ProjectId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Check Status field. Retry every 30 seconds until Status=running.
Step 2: Initialize Coaching Platform Database
Note: Steps 2-3 execute on Supabase Project, Steps 4-8 on ADBPG Instance. They are independent.
Modify whitelist, then connect via psql and execute schema from references/database-schema.md.
aliyun gpdb modify-supabase-project-security-ips --profile adbpg \
--biz-region-id <RegionId> --project-id <ProjectId> \
--security-ip-list "<WhitelistIP>" \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Step 3: Insert Preset Coaching Domains
Execute SQL from references/database-schema.md via psql to insert coaching domains and coaching personas.
Step 4: Discover / Select / Create ADBPG Instance
4.1 Discover Existing Instances
aliyun gpdb describe-db-instances --profile adbpg \
--biz-region-id <RegionId> --page-size 100 \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Filter results: DBInstanceStatus=Running AND VectorConfigurationStatus=enabled.
4.2 User Selects Instance
Present qualifying instances to user:
Available Instances (Running + Vector Enabled):
| # | Instance ID | Spec | Region | Status | Description |
|---|
| 1 | gp-xxxxx | 4C32G | cn-hangzhou | Running | Production |
| 2 | gp-yyyyy | 8C64G | cn-hangzhou | Running | Testing |
Select an instance, or enter "Create New".
- User selects existing → Go to Step 4.3
- User selects "Create New" → Go to Step 4.4
- No qualifying instances → Inform user, go to Step 4.4
4.3 Verify Selected Instance (when using existing)
aliyun gpdb describe-db-instance-attribute --profile adbpg \
--db-instance-id <DBInstanceId> --region <RegionId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Confirm: DBInstanceStatus=Running + VectorConfigurationStatus=enabled. Then proceed to Step 5.
4.4 Create New Instance (when no existing or user chooses new)
Must present configuration and get user confirmation before execution:
💰 Cost note: Creating an instance incurs charges. Release or pause via ADBPG Console when not in use.
| Config | Default | Notes |
|---|
| RegionId | cn-hangzhou | User-specified |
| ZoneId | cn-hangzhou-j | Auto-query VPC/VSwitch after selection |
| EngineVersion | 7.0 | |
| DBInstanceMode | StorageElastic | Storage elastic mode |
| DBInstanceCategory | Basic | Default Basic; optional HighAvailability |
| InstanceSpec | 4C16G | Basic: 4C16G/8C32G/16C64G; HA: 4C32G/8C64G/16C128G |
| SegNodeNum | 2 | Basic default 2 (multiples of 2); HA default 4 (multiples of 4) |
| StorageSize | 50 GB | Range: 50–8000 GB |
| SegStorageType | cloud_essd | ESSD cloud disk |
| VPC/VSwitch | Auto-discover | Select VSwitch with most available IPs |
| VectorConfigurationStatus | enabled | Must be enabled for AI coaching |
| PayType | Postpaid | Pay-as-you-go; optional Prepaid |
Query VSwitch list for the zone:
aliyun vpc describe-vswitches --profile adbpg \
--biz-region-id <RegionId> --zone-id <ZoneId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Present VSwitch options to user, recommend the one with most available IPs.
After user confirms:
aliyun gpdb create-db-instance --profile adbpg \
--biz-region-id <RegionId> --zone-id <ZoneId> \
--engine gpdb --engine-version "7.0" \
--db-instance-mode StorageElastic --db-instance-category Basic \
--instance-spec 4C16G --seg-node-num 2 \
--storage-size 50 --seg-storage-type cloud_essd \
--vpc-id <VpcId> --vswitch-id <VSwitchId> \
--vector-configuration-status enabled --pay-type Postpaid \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Timeout: Instance creation takes 10–15 minutes (max 30 min). Poll every 30–60 seconds:
aliyun gpdb describe-db-instance-attribute --profile adbpg \
--db-instance-id <DBInstanceId> --region <RegionId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Wait until DBInstanceStatus=Running.
Step 5: Configure Database Account
Check if the ADBPG instance already has a database account:
aliyun gpdb describe-accounts --profile adbpg \
--db-instance-id <DBInstanceId> \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Case A: No existing account → Create a new account:
Suggest account creation, confirm with user before executing:
- Account name: auto-generate
ai_coaching_XX (XX = random 2-digit number), or user-specified
- Password: auto-generate a compliant password (8-32 chars, at least 3 character types, avoid
!), or user-specified
- Example:
Account: ai_coaching_01, Password: Coach3Acc#2x9K — Please confirm or provide your own.
⚠️ Important:
- Account name cannot be changed after creation — confirm carefully!
- Password can be reset via console, but save it securely now.
- This account will be used as
ManagerAccount in Step 6.
aliyun gpdb create-account --profile adbpg \
--db-instance-id <DBInstanceId> --region <RegionId> \
--account-name <ManagerAccount> --account-password '<ManagerAccountPassword>' \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Case B: Account already exists → Inform the user. If the account was not created by the agent, ask the user for the existing account password before proceeding to Step 6.
Record: ManagerAccount and ManagerAccountPassword — these will be used in Step 6 for knowledge base initialization.
Step 6: Create Knowledge Base
Parameters to confirm for this step: Auto-generate the following, present to user for confirmation (user may modify), then execute.
| Parameter | Default | Notes |
|---|
Namespace | ns_coaching | Namespace name, cannot be changed after creation |
NamespacePassword | Auto-generate | Namespace password (same password rules); needed for uploads and coaching sessions |
Collection | coaching_knowledge | Knowledge base name |
EmbeddingModel | text-embedding-v4 | Embedding model |
Using the ManagerAccount and ManagerAccountPassword from Step 5, after user confirms the above parameters, execute:
aliyun gpdb init-vector-database --profile adbpg \
--biz-region-id <RegionId> --db-instance-id <DBInstanceId> \
--manager-account <ManagerAccount> --manager-account-password '<ManagerAccountPassword>' \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun gpdb create-namespace --profile adbpg \
--biz-region-id <RegionId> --db-instance-id <DBInstanceId> \
--manager-account <ManagerAccount> --manager-account-password '<ManagerAccountPassword>' \
--namespace <Namespace> --namespace-password '<NamespacePassword>' \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
aliyun gpdb create-document-collection --profile adbpg \
--biz-region-id <RegionId> --db-instance-id <DBInstanceId> \
--manager-account <ManagerAccount> --manager-account-password '<ManagerAccountPassword>' \
--namespace <Namespace> --collection <Collection> \
--embedding-model <EmbeddingModel> --dimension 1024 \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Step 7 (Optional): Upload Domain Knowledge Documents
If the user has domain knowledge documents (PDF/TXT/Markdown, etc.), upload them to the knowledge base to enhance coaching quality. This step can be skipped — proceed directly to Step 8 to start coaching.
aliyun gpdb upload-document-async --profile adbpg \
--biz-region-id <RegionId> --db-instance-id <DBInstanceId> \
--namespace <Namespace> --namespace-password '<NamespacePassword>' \
--collection <Collection> --file-name "domain_knowledge.pdf" \
--file-url "https://example.com/knowledge.pdf" \
--document-loader-name ADBPGLoader --chunk-size 500 --chunk-overlap 50 \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Recommended documents by scenario: Sales methodologies, process guides (Workflow); Architecture patterns, design docs (Decision Support); Communication frameworks, best practices (Skill Development); Tech stack docs, onboarding guides (Onboarding).
Step 8: Start Coaching Session
Optional parameters for this step:
| Parameter | Default | Notes |
|---|
Model | qwen-max | LLM model; use qwen-turbo for daily practice (lower cost) |
TopK | 5 | RAG retrieval count |
Note: SourceCollection element MUST include Namespace field.
aliyun gpdb chat-with-knowledge-base --profile adbpg \
--biz-region-id <RegionId> --db-instance-id <DBInstanceId> \
--model-params '{"Model": "<Model>", "Messages": [
{"Role": "system", "Content": "<system_prompt from coaching_personas>"},
{"Role": "user", "Content": "<learner message>"}
]}' \
--knowledge-params '{"SourceCollection": [{
"Collection": "<Collection>", "Namespace": "<Namespace>",
"NamespacePassword": "<NamespacePassword>", "QueryParams": {"TopK": <TopK>}
}]}' \
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-analyticdb-postgresql-ai-coaching-best-practice
Scenario Quick Reference
| Scenario | Flow |
|---|
| Workflow Coaching | Query sales_workflow_coach → Inject coaching persona + process KB → Guide learner through sales stages → Record session |
| Decision Support | Query architecture_advisor → Inject coaching persona + tech KB → Guide trade-off analysis → Document decision |
| Skill Development | Query communication_coach → Inject coaching persona + best practices KB → Practice scenarios → Provide feedback |
| Onboarding | Query onboarding_mentor → Inject coaching persona + tech docs KB → Progressive learning → Verify understanding |
Success Verification
See references/verification-method.md for detailed verification steps.
Quick verification:
- Supabase project exists and is
Running
- ADBPG instance has
VectorConfigurationStatus=enabled
- Database tables exist (coaching_domains, coaching_personas, learners, coaching_sessions)
- Preset coaching domains are queryable
ChatWithKnowledgeBase returns meaningful coaching responses
Best Practices
- Supabase for data, KB for AI — Session records through Supabase, coaching dialogue through RAG
- Coaching persona is key — Quality of
system_prompt determines coaching effectiveness
- Always store session records — Write every coaching round for review and improvement
- All operations use
--profile adbpg — Consistent credential management
- Team isolation with namespaces — Different teams use different
Namespace
- TopK recommendation: 5 — Reduces token consumption
- Daily practice: qwen-turbo (low cost), assessments: qwen-max (high quality)
- Idempotent write operations — Before any resource creation (CreateSupabaseProject, CreateDBInstance, CreateAccount, CreateNamespace, etc.), always query first (Describe/List) to check if the resource already exists. Only create when the resource does not exist. This prevents duplicate resources on retry
References
