Azure Appconfiguration Py V2

Azure App Configuration SDK for Python

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/azure-appconfiguration-py 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.

Azure App Configuration SDK for Python Centralized configuration management with feature flags and dynamic settings.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Configuration Settings, List Settings, Feature Flags, Read-Only Settings.

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.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

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

Imported Workflow Notes

Imported: Installation

pip install azure-appconfiguration

Imported: Environment Variables

AZURE_APPCONFIGURATION_CONNECTION_STRING=Endpoint=https://<name>.azconfig.io;Id=...;Secret=...
# Or for Entra ID:
AZURE_APPCONFIGURATION_ENDPOINT=https://<name>.azconfig.io

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-appconfiguration-py-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 @azure-appconfiguration-py-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 @azure-appconfiguration-py-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 @azure-appconfiguration-py-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.

• Use labels for environment separation (dev, staging, prod)
• Use key prefixes for logical grouping (app:database:, app:cache:)
• Make production settings read-only to prevent accidental changes
• Create snapshots before deployments for rollback capability
• Use Entra ID instead of connection strings in production
• Refresh settings periodically in long-running applications
• Use feature flags for gradual rollouts and A/B testing

Imported Operating Notes

Imported: Best Practices

1. Use labels for environment separation (dev, staging, prod)
2. Use key prefixes for logical grouping (app:database:, app:cache:)
3. Make production settings read-only to prevent accidental changes
4. Create snapshots before deployments for rollback capability
5. Use Entra ID instead of connection strings in production
6. Refresh settings periodically in long-running applications
7. Use feature flags for gradual rollouts and A/B testing

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/azure-appconfiguration-py, 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: Authentication

Connection String

from azure.appconfiguration import AzureAppConfigurationClient

client = AzureAppConfigurationClient.from_connection_string(
    os.environ["AZURE_APPCONFIGURATION_CONNECTION_STRING"]
)

Entra ID

from azure.appconfiguration import AzureAppConfigurationClient
from azure.identity import DefaultAzureCredential

client = AzureAppConfigurationClient(
    base_url=os.environ["AZURE_APPCONFIGURATION_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Imported: Configuration Settings

Get Setting

setting = client.get_configuration_setting(key="app:settings:message")
print(f"{setting.key} = {setting.value}")

Get with Label

# Labels allow environment-specific values
setting = client.get_configuration_setting(
    key="app:settings:message",
    label="production"
)

Set Setting

from azure.appconfiguration import ConfigurationSetting

setting = ConfigurationSetting(
    key="app:settings:message",
    value="Hello, World!",
    label="development",
    content_type="text/plain",
    tags={"environment": "dev"}
)

client.set_configuration_setting(setting)

Delete Setting

client.delete_configuration_setting(
    key="app:settings:message",
    label="development"
)

Imported: List Settings

All Settings

settings = client.list_configuration_settings()
for setting in settings:
    print(f"{setting.key} [{setting.label}] = {setting.value}")

Filter by Key Prefix

settings = client.list_configuration_settings(
    key_filter="app:settings:*"
)

Filter by Label

settings = client.list_configuration_settings(
    label_filter="production"
)

Imported: Feature Flags

Set Feature Flag

from azure.appconfiguration import ConfigurationSetting
import json

feature_flag = ConfigurationSetting(
    key=".appconfig.featureflag/beta-feature",
    value=json.dumps({
        "id": "beta-feature",
        "enabled": True,
        "conditions": {
            "client_filters": []
        }
    }),
    content_type="application/vnd.microsoft.appconfig.ff+json;charset=utf-8"
)

client.set_configuration_setting(feature_flag)

Get Feature Flag

setting = client.get_configuration_setting(
    key=".appconfig.featureflag/beta-feature"
)
flag_data = json.loads(setting.value)
print(f"Feature enabled: {flag_data['enabled']}")

List Feature Flags

flags = client.list_configuration_settings(
    key_filter=".appconfig.featureflag/*"
)
for flag in flags:
    data = json.loads(flag.value)
    print(f"{data['id']}: {'enabled' if data['enabled'] else 'disabled'}")

Imported: Read-Only Settings

# Make setting read-only
client.set_read_only(
    configuration_setting=setting,
    read_only=True
)

# Remove read-only
client.set_read_only(
    configuration_setting=setting,
    read_only=False
)

Imported: Snapshots

Create Snapshot

from azure.appconfiguration import ConfigurationSnapshot, ConfigurationSettingFilter

snapshot = ConfigurationSnapshot(
    name="v1-snapshot",
    filters=[
        ConfigurationSettingFilter(key="app:*", label="production")
    ]
)

created = client.begin_create_snapshot(
    name="v1-snapshot",
    snapshot=snapshot
).result()

List Snapshot Settings

settings = client.list_configuration_settings(
    snapshot_name="v1-snapshot"
)

Imported: Async Client

from azure.appconfiguration.aio import AzureAppConfigurationClient
from azure.identity.aio import DefaultAzureCredential

async def main():
    credential = DefaultAzureCredential()
    client = AzureAppConfigurationClient(
        base_url=endpoint,
        credential=credential
    )

    setting = await client.get_configuration_setting(key="app:message")
    print(setting.value)

    await client.close()
    await credential.close()

Imported: Client Operations

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.