aicore-lifecycle-management

Q: How do I install aicore-lifecycle-management?

Run npx skills add https://github.com/sap/ai-skills-library --skill aicore-lifecycle-management in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/aicore-lifecycle-management/ (personal) or .claude/skills/aicore-lifecycle-management/ (project).

by sapAI & Agents

Manage SAP AI Core lifecycle resources: scenarios, executables, configurations, and deployments. WHEN: user wants to list or inspect scenarios or executables, create or list configurations, create or list or stop or delete or patch deployments, get deployment logs, or remove duplicate LLM deployments. DO NOT USE FOR: listing foundation models or getting inference examples — use genai-hub-foundation-models; managing resource groups, secrets, or GitOps resources — use aicore-admin-resources.

Installs: 88
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/sap/ai-skills-library --skill aicore-lifecycle-management

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads aicore-lifecycle-management automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Rules

Run all scripts from the skill root directory with uv run scripts/<name>.py.
Never print or log credential values — the SDK reads them from the environment.
Scripts that delete or mutate state require --confirm. Always pass it explicitly; never bypass it.
When the user does not specify a resource group, omit --resource-group so the SDK uses its configured default.
For LLM deployments: the scenario ID, executable ID, and model parameter names come from genai-hub-foundation-models — invoke it first if those values are unknown.
Deployment configuration naming convention: use {executable_id}--{model_name} for LLM deployments (e.g. azure-openai--gpt-4o-mini). Before creating a configuration, check whether one with that name already exists via get_configurations.py --scenario-id <id> and reuse it to avoid duplicates.

List / Inspect Scenarios

# List all scenarios
uv run scripts/get_scenarios.py

# Inspect a specific scenario and its versions
uv run scripts/get_scenarios.py --scenario-id <id>
uv run scripts/get_scenarios.py --scenario-id <id> --response-format detailed

# JSON output for piping
uv run scripts/get_scenarios.py --json

List Executables

# List executables for a scenario
uv run scripts/get_executables.py --scenario-id <id>

# Inspect a specific executable (includes parameters and input artifacts)
uv run scripts/get_executables.py --scenario-id <id> --executable-id <id>
uv run scripts/get_executables.py --scenario-id <id> --executable-id <id> --response-format detailed

# JSON output
uv run scripts/get_executables.py --scenario-id <id> --json

Parameters are only returned when inspecting a single executable (--executable-id). The list view returns id, name, and description only.

Configurations

# List configurations
uv run scripts/get_configurations.py
uv run scripts/get_configurations.py --scenario-id <id>
uv run scripts/get_configurations.py --response-format detailed
uv run scripts/get_configurations.py --json

# Inspect a specific configuration
uv run scripts/get_configurations.py --configuration-id <id>

# Create a configuration (binds parameters to an executable)
uv run scripts/create_configuration.py \
  --name my-config \
  --scenario-id <id> \
  --executable-id <id> \
  --param modelName=gpt-4o \
  --param temperature=0.7

If a deployment fails or stays PENDING after several minutes, verify parameter names and values against the executable's declared parameters:

uv run scripts/get_executables.py --scenario-id <id> --executable-id <id> --response-format detailed

Deployments

Deployments serve inference endpoints. The standard flow is: create (or reuse) a configuration, then create a deployment from it.

Configuration naming convention

Always check whether a matching configuration already exists before creating one, to avoid duplicates:

# Check existing configurations before creating
uv run scripts/get_configurations.py --scenario-id <scenario-id> --json | jq '.[] | select(.name == "<expected-name>")'

Naming convention:

LLM: {executable_id}--{model_name} — e.g. azure-openai--gpt-4o-mini
Orchestration: orchestration
Custom: choose a descriptive name

LLM deployment (from genai-hub-foundation-models handoff)

# 1. Create configuration (skip if one with this name already exists):
uv run scripts/create_configuration.py \
  --name azure-openai--gpt-4o-mini \
  --scenario-id foundation-models \
  --executable-id azure-openai \
  --param modelName=gpt-4o-mini \
  --param modelVersion=latest

# 2. Deploy:
uv run scripts/create_deployment.py --configuration-id <config-id> --wait

Orchestration deployment

# 1. Create configuration (skip if "orchestration" config already exists):
uv run scripts/create_configuration.py \
  --name orchestration \
  --scenario-id orchestration \
  --executable-id orchestration

# 2. Deploy:
uv run scripts/create_deployment.py --configuration-id <config-id> --wait --timeout 600

List and inspect deployments

# List all deployments
uv run scripts/get_deployments.py

# Filter by status or scenario or executable
uv run scripts/get_deployments.py --status RUNNING
uv run scripts/get_deployments.py --scenario orchestration
uv run scripts/get_deployments.py --executable-id azure-openai --status RUNNING
uv run scripts/get_deployments.py --executable-id azure-openai --status RUNNING --json

# Inspect one deployment
uv run scripts/get_deployments.py --deployment-id <id>
uv run scripts/get_deployments.py --deployment-id <id> --response-format detailed
uv run scripts/get_deployments.py --deployment-id <id> --json

Stop, patch, delete, logs

# Stop a deployment
uv run scripts/stop_deployment.py --deployment-id <id> --confirm
uv run scripts/stop_deployment.py --deployment-id <id> --confirm --wait

# Patch (swap configuration on a running deployment)
uv run scripts/patch_deployment.py --deployment-id <id> --configuration-id <new-config-id>

# Delete (must be STOPPED or DEAD first)
uv run scripts/delete_deployment.py --deployment-id <id> --confirm
# Stop and delete in one step:
uv run scripts/delete_deployment.py --deployment-id <id> --confirm --auto-stop

# Deployment logs
uv run scripts/get_deployment_logs.py --deployment-id <id>
uv run scripts/get_deployment_logs.py --deployment-id <id> --tail 50
uv run scripts/get_deployment_logs.py --deployment-id <id> --order asc
uv run scripts/get_deployment_logs.py --deployment-id <id> --json

# Remove duplicate LLM deployments (only affects LLM deployments with a model name)
uv run scripts/dedup_deployments.py --dry-run
uv run scripts/dedup_deployments.py

Handoffs

Need a Bearer token or credential setup? → invoke aicore-admin-resources
Need executable ID, model name, or model capabilities? → invoke genai-hub-foundation-models
Want to list available models or providers? → invoke genai-hub-foundation-models

FAQ

What does the aicore-lifecycle-management skill do?

How do I install aicore-lifecycle-management?

Run npx skills add https://github.com/sap/ai-skills-library --skill aicore-lifecycle-management in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/aicore-lifecycle-management/ (personal) or .claude/skills/aicore-lifecycle-management/ (project).

Which AI agents can use aicore-lifecycle-management?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is aicore-lifecycle-management safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains aicore-lifecycle-management?

It is published by sap in sap/ai-skills-library. It has 88 recorded installs.

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aicore-lifecycle-management

Install

Install

Describe the task

Review the result

SKILL.md

Rules

List / Inspect Scenarios

List Executables

Configurations

Deployments

Configuration naming convention

LLM deployment (from genai-hub-foundation-models handoff)

Orchestration deployment

List and inspect deployments

Stop, patch, delete, logs

Handoffs

FAQ

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