# Cisco Cloud Control Setup

> Use when the user asks to prepare Cisco Cloud Control, AgenticOps, AI Canvas, Cloud Control Studio,

- **Type:** Skill
- **Install:** `agentstack add skill-chambear2809-splunk-cisco-skills-cisco-cloud-control-setup`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [chambear2809](https://agentstack.voostack.com/s/chambear2809)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [chambear2809](https://github.com/chambear2809)
- **Source:** https://github.com/chambear2809/splunk-cisco-skills/tree/main/skills/cisco-cloud-control-setup

## Install

```sh
agentstack add skill-chambear2809-splunk-cisco-skills-cisco-cloud-control-setup
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Cisco Cloud Control Setup

## Prerequisites

| Tool or access | Purpose | Verify |
|---|---|---|
| Bash and Python 3 | Run bundled setup and validation helpers | `bash --version && python3 --version` |
| Required product/platform access | Inspect or configure the selected target | Complete the documented preflight |
| Credential files for live modes | Keep secrets out of chat | Verify paths only |

## Workflow Overview

```text
┌───────────┐   ┌───────────────┐   ┌───────────────┐   ┌─────────────────┐
│ Preflight │ → │ Render/review │ → │ Apply/handoff │ → │ Validate evidence │
└───────────┘   └───────────────┘   └───────────────┘   └─────────────────┘
```

## When to Activate

- Prepare Cisco Cloud Control, AgenticOps, AI Canvas, Cloud Control Studio, Cloud Control Workflows, or governed
  Cisco/Splunk agent execution workflows.
- Preview and review the cisco cloud control setup workflow before any live apply phase.
- Diagnose failed prerequisites, generated assets, configuration, or validation evidence.

## Scope

Follow the documented read-only or render-first path whenever it is available.
This skill does not imply permission to mutate live systems. Require explicit
apply flags, protected credentials, and operator review for state changes.

## Examples

Inspect the supported setup modes before selecting one:

```bash
bash skills/cisco-cloud-control-setup/scripts/setup.sh --help
```

Expected output: usage, supported modes, and required arguments are displayed
without changing the target environment.

Inspect validation modes before running completion checks:

```bash
bash skills/cisco-cloud-control-setup/scripts/validate.sh --help
```

Expected output: offline, live, and completion options are displayed when the
skill supports them; help exits without mutation.

## Troubleshooting

| Issue | Cause | Resolution |
|---|---|---|
| Preflight fails | A required tool or access path is missing | Resolve it before rendering or applying |
| Rendered assets are incomplete | Required non-secret inputs are absent | Complete intake and render again |
| Apply is blocked | Review, credentials, or explicit acceptance is missing | Use the documented handoff |
| Validation is incomplete | Live evidence is unavailable | Record the gap and keep completion open |

This skill is a render-first parent workflow for Cisco Cloud Control adoption.
It does not call undocumented Cisco Cloud Control APIs. It renders the
operator plan, official feature/product coverage, Cisco Workflows API
readiness, Cloud Control Studio briefs, AI Canvas board templates, and
executable child-skill handoffs where supported.

## Supported Paths

1. **Cisco Data Fabric architecture**: delegate the complete lifecycle-aware
   product, feature, federation, storage/catalog, context, AI, governance, and
   source-evidence packet to `cisco-data-fabric-setup`. That dedicated parent
   routes constituent Splunk skills only when it has reviewed non-secret
   inputs and never claims a standalone Cisco Data Fabric package or API.
2. **MCP connectors**: delegate Splunk MCP Server and ThousandEyes MCP client
   setup plans. Splunk MCP client rendering is emitted only when
   `mcp.splunk_mcp_url` is set, because the child skill otherwise needs
   Splunk credentials to derive the endpoint. ThousandEyes MCP can render
   independently.
3. **Agent observability**: delegate Splunk AI Agent Monitoring setup.
4. **Observability content**: delegate dashboards and detectors to existing
   Observability skills.
5. **Official Cloud Control surfaces**: render coverage for onboarding,
   tenant groups, product integrations, AI context, users/roles, SSO, audit
   logs, AI Assistant, AI Canvas, Actions, Notifications, Favorites, Inventory,
   Licensing, RBAC, Topology, Workflows, release notes, and Multicloud Fabric.
6. **Cisco Workflows API readiness**: render the documented API/OAS, target,
   account-key, auth, and rate-limit checklist without making API calls.
7. **Domain readiness**: render child-skill handoffs for Intersight, Nexus,
   Nexus Hyperfabric, ThousandEyes, Meraki, Catalyst Center, Catalyst SD-WAN,
   Security Cloud Control, Secure Access, Duo, ISE, Secure Firewall, Splunk
   Cloud, Collaboration Control Hub, and Cisco IQ.
8. **Cloud Control Studio and AI Canvas**: render UI/CA handoff artifacts only.

## Safe First Command

```bash
bash skills/cisco-cloud-control-setup/scripts/setup.sh --help
```

## Primary Workflow

Render from the example intake:

```bash
bash skills/cisco-cloud-control-setup/scripts/setup.sh \
  --render \
  --validate \
  --spec skills/cisco-cloud-control-setup/template.example \
  --output-dir cisco-cloud-control-rendered
```

Run the doctor report:

```bash
bash skills/cisco-cloud-control-setup/scripts/setup.sh \
  --doctor \
  --spec skills/cisco-cloud-control-setup/template.example \
  --output-dir cisco-cloud-control-rendered
```

Review delegated execution without changing anything:

```bash
bash skills/cisco-cloud-control-setup/scripts/setup.sh \
  --execute data-fabric,mcp,agent-observability \
  --dry-run \
  --json \
  --spec skills/cisco-cloud-control-setup/template.example
```

Execute only reviewed delegated sections:

```bash
bash skills/cisco-cloud-control-setup/scripts/setup.sh \
  --execute data-fabric,mcp \
  --accept-execute \
  --spec skills/cisco-cloud-control-setup/template.example \
  --output-dir cisco-cloud-control-rendered
```

## CLI Contract

`setup.sh` supports `--render`, `--validate`, `--doctor`,
`--execute SECTION[,SECTION]`, `--accept-execute`, `--dry-run`, `--json`,
`--spec PATH`, and `--output-dir DIR`.

Delegated executable sections:

- `data-fabric`
- `mcp`
- `agent-observability`
- `observability-content`
- `domain-readiness` (handoff-only; an execute request exits nonzero)
- `cloud-control-studio` (handoff-only; an execute request exits nonzero)
- `ai-canvas` (handoff-only; an execute request exits nonzero)

The `data-fabric` section runs only the dedicated parent's render and static
validation workflow. Any constituent apply remains behind the owning child
skill's explicit approval gates.

`domain-readiness`, `cloud-control-studio`, and `ai-canvas` never mutate Cisco
Cloud Control. They render operator handoff artifacts, and an explicit execute
request exits nonzero so a handoff cannot be mistaken for an applied change.

The Cisco Workflows API is treated as a readiness surface: this skill renders
the public API/OAS, target, account-key, and rate-limit checklist, but it does
not issue Workflows API calls or claim a direct Cisco Cloud Control platform
mutation API.

## Secret Handling

This parent skill rejects direct secret flags such as `--token`, `--password`,
`--api-key`, `--client-secret`, and `--private-key`. Specs must not contain raw
secret-looking keys such as `token`, `password`, `api_key`, `client_secret`, or
`private_key`. Put credentials in the delegated child skill's supported secret
files and keep those values out of chat and argv.

## Validation

```bash
bash skills/cisco-cloud-control-setup/scripts/validate.sh \
  --output-dir cisco-cloud-control-rendered
```

For code validation:

```bash
python3 -m py_compile \
  skills/cisco-cloud-control-setup/scripts/render_assets.py
```

See `reference.md` for the source ledger, coverage boundaries, and delegated
skill map.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [chambear2809](https://github.com/chambear2809)
- **Source:** [chambear2809/splunk-cisco-skills](https://github.com/chambear2809/splunk-cisco-skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-chambear2809-splunk-cisco-skills-cisco-cloud-control-setup
- Seller: https://agentstack.voostack.com/s/chambear2809
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.