# Aeo > Measure whether Claude Code, Codex, or Grok mention a target brand on realistic questions. Two arms per query (knowledge vs search). Use when asked to run AEO, brand-visibility, or mention checks against local coding-agent CLIs. - Skill: `probelabs/aeo` (Agent Skill) - Install (CLI): `npx skillmds@latest add probelabs/aeo` - Raw SKILL.md: https://api.skillmd.com/api/skills/probelabs/aeo/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: probelabs (https://skillmd.com/u/probelabs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/probelabs/aeo --- # AEO measurement skill Portable across Claude Code, Codex, and Cursor. Measures local CLIs only. Do **not** log into consumer LLM websites (claude.ai, chatgpt.com, grok.com, gemini.google.com) from a datacenter or VPS. This skill shells out to `claude`, `codex`, and `grok` on the machine that already has them. ## When to use - "Does Claude/Codex/Grok mention our product for this question?" - Brand visibility / AEO check against coding-agent CLIs - Comparing knowledge-only answers vs search-allowed answers - Capturing the exact search strings a model typed Do not use this for Gemini grounding, AI Overviews, or browser-login audits. ## Rules - Never add the brand name, rust, or extra stack words to the user prompt. - The only suffix the runner adds is: `Recommend existing tools or products if relevant. Do not write, edit, execute, or read files from disk. Do not inspect the working directory or parent folders.` - Run every cell in an empty `/tmp/aeo-isolate-*` cwd. Grok: `--sandbox strict --cwd --no-memory`. Do not use Grok `--sandbox workspace` (still reads the whole disk). - Mention = whole-word brand/alias in answer text, not substring. A domain-style alias as an http(s) URL host counts; path-only tokens do not. See [METHODOLOGY.md](../../METHODOLOGY.md). - Raw evidence JSON is the source of truth. Rates are views. ## Init ```bash python3 -m aeo init --brand Acme --domain acme.example --out aeo.config.json # or the XERJ example workspace: python3 -m aeo init --from-example xerj --out aeo.config.json ``` Config is generic: brand, aliases, competitors, engines, prompts. XERJ is an example, not the only brand. ## Run one query ```bash python3 -m aeo run --config aeo.config.json \ --prompt "What's the best way to search through a folder of files by content?" \ --engine all --arm both ``` `--dry-run` prints the exact `claude` / `codex` / `grok` command without executing. ## Batch ```bash python3 -m aeo run --config aeo.config.json --engine all --arm both ``` Default `samples_per_arm` is 1 (CLIs are slow). Pass `--samples N` for jitter on **this** invocation (it is not a per-id config field). `--only-id ID` (repeatable) filters the roster; `--prompt-id` only labels `--prompt`. `--concurrency N` (default 1) runs up to N remaining cells in this process (`--engine all` stays one process). Workers write `.parts/` shards; the parent merges into `--out` (existing cells win). Do not share `--out` across processes. ## Roster Keep the **full roster**. Do not drop watch queries because the incumbent won. Use `--class focus` when the work is content / AEO (search-likely and product-fit). Still run `--class all` on a cadence so a watch query that starts searching or mentioning the brand can be promoted. ```bash python3 -m aeo run --config aeo.config.json --class focus --engine all --arm both python3 -m aeo run --config aeo.config.json --class all --engine all --arm both --concurrency 4 python3 -m aeo board aeo-data/runs/.json ``` ## Score / report ```bash python3 -m aeo report aeo-data/runs/.json ``` Table columns: query, class, engine, knowledge hit, search hit, searched?, vendors in search queries, brand in answer. One-line class tally (watch vs focus mention/search rates). For a decision-maker scoreboard (and agent JSON), use `python3 -m aeo board` — see [aeo-board](../aeo-board/SKILL.md). ## Where evidence is written Append-only. Each run writes a new file: `{data_dir}/runs/{run_id}.json` `--out` resume skips completed prompt×engine×arm cells. `--concurrency N` workers write `{out}.parts/` shards; the parent merges those into `--out` (existing cells win). Do not point two processes at the same `--out`. Validates against `schemas/aeo-cli-evidence-v1.json`. ## How to interpret | What you see | What it means | | --- | --- | | searched = no | Did not search. Answer is prior. | | searched = yes, vendors in the query strings | Searched already-named vendors (pre-search belief). | | searched = yes, no configured vendors in the query strings | Open discovery search. | | brand in answer = yes | Mention. Independent of search. | Full write-up and a walkthrough of the XERJ fixture: [METHODOLOGY.md](../../METHODOLOGY.md). Which pages to write and the measure→ship→re-run loop: [aeo-playbook](../aeo-playbook/SKILL.md) / [PLAYBOOK.md](../../PLAYBOOK.md). After a full-grid zero (or near-zero) mention, do not start with more articles. `curl` claimed URLs first, split confirmation / discovery / search-blind, map seeds onto existing slugs, then follow [PLAYBOOK.md](../../PLAYBOOK.md) §9–10. If those URLs already 200 as themselves and mentions stay 0, run §11 (retrieval debug: live vs not-indexed vs skipped) before any draft — Search Console / Bing Webmaster / IndexNow, not a third pile of slugs. Knowledge-arm 0 on an unknown brand is expected; keep measuring that arm. Human view of a run: `python3 -m aeo board ` (markdown + JSON; optional `--format html`) plus the evidence JSON. Merge engine files with `python3 -m aeo report --html --out report.html a.json b.json`. ## Raw flags (if the wrapper is blocked) - Claude knowledge: `claude -p --tools ""` — never `--bare` - Claude search: `--tools WebSearch,WebFetch --allowedTools WebSearch,WebFetch --permission-mode bypassPermissions` plus a settings file that empties hooks - Grok knowledge: `grok -p --disable-web-search` - Grok search: `--output-format json --verbatim` (not streaming-json) - Codex knowledge: `codex exec --ephemeral --skip-git-repo-check --sandbox read-only` without `--enable standalone_web_search` - Codex search: same plus `--json --enable standalone_web_search` ## Testimony judge After a full evidence run, `scripts/judge_run.py` does three passes: stance/position/quote on `brand_mentioned` cells, **vendor extract on every completed arm** (hits and misses), then a board brief. Config `competitors` is the **seed / known set** (expected category map) — keep adding names up front. The LLM still captures **surprises** (named but not on the seed list after normalize); those are flagged separately, not merged into the known pile. Then `scripts/render_judge_html.py`. Vendor-only: `python3.11 scripts/judge_run.py --vendors-only `. Do not treat CLI `recommended` as testimony. Brand hit rate stays deterministic `brand_mentioned`. Grok AEO runs must use `GROK_HOME` without MCP and may need `GROK_SANDBOX=workspace` when Docker Desktop makes `docker.sock` a symlink.