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 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).

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.

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)

Evaluations (configured policies on apps)

Custom evaluations (policies) & application links

By-id is prebuilt-only. evaluations get <id> fetches a prebuilt/configured evaluation. Custom policies have no get-by-id — find one via custom-evaluations list / list-for-application and match by id/name.

Coverage & model pricing

The --from-file bodies for evaluations and custom-evaluations match the AI v3 API shape verbatim; use - to read JSON from stdin. For evaluations create, target is required and must be uppercase (PROMPT or RESPONSE); for custom-evaluations create, name, instructions, and policyType are required. Exception: model-pricing set takes just the raw model→price map — cx wraps it as {"prices": …} for you, so do not include the outer prices envelope. Each model maps to a price object; all four fields are optional doubles (USD per one million tokens), omit the ones that don't apply:

{
  "gpt-4o": {
    "inputPricePerMillionTokens": 2.5,
    "outputPricePerMillionTokens": 10,
    "cacheReadPricePerMillionTokens": 1.25,
    "cacheWritePricePerMillionTokens": 3.75
  }
}

An empty map {} clears all overrides (set replaces the whole set — it's team-wide, new data only). model-pricing get returns the wrapper { "pricing": { "id", "companyId", "prices": { … } } } — the per-model overrides live under prices (empty when none are set).

Common workflows

Inventory & guardrail gaps

# Which apps are NOT guarded?
cx ai-center applications list -o json | jq '[.[] | select(.guardrailsIntegrated==false)]'

Enable a policy on an app (write — confirm first!)

# 1. Describe to the user; 2. get approval; 3. then:
cx ai-center evaluations create --from-file eval.json --yes
# eval.json: { "application": "...", "subsystem": "...", "target": "PROMPT", "config": { "<type>": {...} }, "isEnabled": true }
# `target` is REQUIRED and must be UPPERCASE — "PROMPT" or "RESPONSE" (the API rejects lowercase / a missing target).

Read the actual conversations (telemetry, not config)

Use cx spans with the query library in references/ai-center-queries.md — reading messages, cost, latency, errors, tool calls, and per-user analysis.

Key principles

• Config vs. telemetry: inventory / evaluations / policies / coverage / pricing → cx ai-center; content / cost / latency / errors / verdicts → GenAI spans via cx spans. Don't answer one from the other.
• Confirm before writes. Describe the operation, get approval, then run with --yes.

Related Skills

• cx-telemetry-querying — general logs/spans/metrics/DataPrime querying (the engine behind the cx spans queries used here).
• cx-olly — the conversational AI assistant (cx olly ask).