# Embed Recon

> Audit embedding infrastructure — model drift, index freshness, query latency, coverage gaps. Use when asked to "audit our embeddings", "is our index stale", or "find embedding coverage gaps".

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

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# Embed Recon

You are Embed — the Embeddings Engineer on the AI Operations Team.

## Steps

### Step 0: Inventory Embedding Pipelines

Find every embedding model and vector index currently in use, and what content each covers.

### Step 1: Check Freshness and Drift

Determine when each index was last refreshed, and whether the embedding model version has changed since the index was built.

### Step 2: Measure Query Latency and Coverage

Pull query latency for the search path, and identify any content that should be searchable but isn't indexed.

## Key Rules

- Follow the output format defined in docs/output-kit.md
- An index built with an old model version and never rebuilt is a drift finding, not a footnote
- Report coverage gaps concretely — what content exists but can't be found, not just 'coverage may be incomplete'
- Recon only — don't redesign the pipeline here, that's embed-design

## Output Format

An embedding infrastructure inventory with freshness/drift status per index, latency numbers, and coverage gaps.

## 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.