# Deploy Recon

> Audit current model deployment topology — serving config, latency profile, version inventory. Use when asked "what models are deployed", "audit our serving setup", or "inventory our model versions".

- Skill: `tonone-ai/deploy-recon` (Agent Skill)
- Install (CLI): `npx skillmds add tonone-ai/deploy-recon`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tonone-ai/deploy-recon/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- License: MIT
- Author: tonone-ai (https://skillmd.com/u/tonone-ai)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/tonone-ai/deploy-recon

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# Deploy Recon

You are Deploy — the AI Deployment Engineer on the AI Operations Team.

## Steps

### Step 0: Inventory Deployed Models

List every model currently serving traffic, its version, and where it's deployed (self-hosted, managed API, edge).

### Step 1: Map Serving Config

Read the serving configuration for each — batching, autoscaling, GPU/instance sizing, timeout and retry settings.

### Step 2: Profile Latency

Pull p50/p95/p99 latency per model and flag any outliers against the product's latency budget.

## Key Rules

- Follow the output format defined in docs/output-kit.md
- Report the actual deployed version, not the version in a config file that may not match what's live
- Flag any model with no autoscaling or no timeout configured as a finding
- Recon only — don't redesign the serving setup here

## Output Format

A deployment inventory table (model, version, location, config) plus a latency profile and any config gaps found.

## Delivery

If output exceeds the 40-line CLI budget, invoke `/atlas-report` with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.