/cx-ai-center

name: cx-ai-center
description: >
  Use this skill for any question or action about the user's AI/GenAI applications or agents —
  their behavior, prompts/responses, quality, hallucinations, guardrails, security, cost/tokens,
  errors, evaluations/policies, model pricing, or configuration — including comparing or tracking
  agents over time. It covers both analyzing AI telemetry (GenAI spans) and managing AI Center
  config via the `cx ai-center` commands.
metadata:
  version: "0.1.0"

AI Center Skill

**This is the tool for anything about AI/GenAI applications** — both **questions** about their behavior (prompts/responses, quality, hallucinations, guardrails, security, cost/tokens, errors, latency — everything AI apps expose through their GenAI spans/tags) and **actions** to manage them (applications, evaluations/policies, policy↔app links, model pricing). If a request touches an AI application or its GenAI telemetry, use this skill.

Coralogix **AI Center** observes, evaluates, and guards GenAI/LLM applications. This skill answers questions about AI apps from two sources:

• **Configuration** (this skill's `cx ai-center` commands): the AI application inventory,

configured evaluations/policies, coverage, custom evaluations, and model pricing — none of which live in span telemetry.

• **Telemetry** (GenAI spans): what users asked, how the model answered, cost, tokens,

latency, errors, tool calls, and eval/guardrail verdicts — queried with **`cx spans '<DataPrime>'`**. See [references/ai-center-queries.md](references/ai-center-queries.md) for the full, runnable query library, span schema, and playbooks.

Match the source to the question: *"which apps lack guardrails"* → config (`cx ai-center applications list`); *"what are users asking my chatbot"* → telemetry (`cx spans '…'`, reading the conversation from the GenAI spans). Some questions need **both** — e.g. *"is my chatbot's PII policy actually catching PII?"* joins config (is the policy enabled) with telemetry (the PII verdicts + the messages).

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Destructive Operation Safety

All write operations (`create`, `update`, `delete`, `add`, `remove`, `set`) require interactive confirmation. `ai-center` is a **risky** command, so writes are also gated by `allow_risky_commands` in `~/.cx/config.toml`. To skip the prompt in scripts, pass `--yes`.

**IMPORTANT: NEVER pass `--yes` without explicit user approval.** Before executing any write: 1. Describe the exact operation to the user (what will be created/modified/deleted/linked). 2. Wait for the user to confirm. 3. Only then execute with `--yes`.

Read operations (`list`, `get`, `coverage`, `list-for-application`, `model-pricing get`) do not require confirmation and can be run freely.

Read-Only Mode

Use `--read-only` (or `CX_READ_ONLY=1`) to block every write at the CLI level — safe for exploration.

Agent Mode

When running inside an AI agent (Claude Code, Cursor, Codex, …), cx detects it and — instead of showing a confirmation prompt that would hang forever (no human is there to type y/n) — stops immediately with an error telling you to get the user's approval, then re-run with `--yes`.

No delete commands (by design)

The CLI intentionally exposes **no delete** for custom-evaluation policies, AI applications, or model pricing — even though the AI v3 API has those delete endpoints, `cx ai-center` does not surface them.

• **Custom-evaluation policy:** can't be deleted; to take it off an app, detach with

`custom-evaluations remove` (the policy object survives and can be re-attached).

• **Model pricing:** no delete command. It's **team-wide** (not per-app), so to change or clear

it, run `model-pricing set` with a new map (an empty map `{}` clears all overrides) — `set` replaces the whole set.

---

Golden rule

For **content** questions (quality, hallucination, sentiment, topics) read the actual conversation and cite the `traceID` — don't rely on verdict tags alone. The transcript lives in one of two conventions (`gen_ai.input.messages`/`output.messages`, or the older indexed `gen_ai.prompt.<n>`/`completion.<n>` tags); read it with the **Reading conversations (content questions)** queries in the library, which handle both and exclude the system prompt and tool traffic. Full guidance: [references/ai-center-queries.md](references/ai-center-queries.md).

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CLI Commands

**Show names to the user; use UUIDs only internally.** When presenting results, refer to apps and evaluations by their human names (application/subsystem, evaluation name), not raw UUIDs. The UUID is only needed to *call* a by-id or write command — resolve it yourself from the matching `list` command (never guess or make the user paste a UUID).

Applications (inventory + guarded status)

| Command | Purpose | |---------|---------| | `cx ai-center applications list` | List AI apps incl. `guardrailsIntegrated` (guarded) status | | `cx ai-center applications list --evaluation-type <TYPE>` | Filter to apps using an eval type (repeatable) | | `cx ai-center applications list --page-size <N> --page-offset <N>` | Paginate | | `cx ai-center applications get <application-id>` | One application by UUID |

Evaluations (configured policies on apps)

| Command | Purpose | |---------|---------| | `cx ai-center evaluations list` | All configured evaluations | | `cx ai-center evaluations list --application <app> --subsystem <sub>` | Scope to one app (the pair) | | `cx ai-center evaluations list --evaluation-type <TYPE>` | Filter by type — `<TYPE>` is the API enum (e.g. `PII`, `TOXICITY`, `PROMPT_INJECTION`; the keys from `coverage`), **not** the lowercase form | | `cx ai-center evaluations get <evaluation-id>` | One evaluation by UUID | | `cx ai-center evaluations create --from-file eval.json` | Create/enable an evaluation *(write)* | | `cx ai-center evaluations update <evaluation-id> --from-file patch.json` | Partial update *(write)* | | `cx ai-center evaluations delete <evaluation-id>` | R