ads-machine: Command for Claude Code

.claude/commands/ad-autoresearch.md

ad-autoresearch is a command for Claude Code from seancrowe01/ads-machine. It costs 43 tokens per session (1,311 once invoked), scanned A, original, MIT.

A daily advertising research command that compares recent competitor ad changes with your own ads and saved examples. Airtable is a database tool used here to store the ad records.

In plain words
What is it for?
Use it after /ad-poller to review new, stopped, or improving competitor ads and produce suggestions linked to your ad pipeline.
Why use it?
It highlights competitor activity, gaps in your advertising, and concrete areas to improve instead of leaving research scattered across sources.

Command for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions CLAUDE.md.

This is seancrowe01/ads-machine's own configuration. It tells Claude Code how to work on ads-machine itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ads-machine configures →

Reuse

Borrowing it

Nothing to install: this file belongs to seancrowe01/ads-machine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/seancrowe01/ads-machine/main/.claude/commands/ad-autoresearch.md
Clone the repo
git clone --depth 1 https://github.com/seancrowe01/ads-machine

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ad-autoresearch

README.md
[![agentmods](https://agentmods.dev/badge/commands/seancrowe01/ads-machine/ad-autoresearch.svg)](https://agentmods.dev/commands/seancrowe01/ads-machine/ad-autoresearch)
Your own site
<a href="https://agentmods.dev/commands/seancrowe01/ads-machine/ad-autoresearch"><img src="https://agentmods.dev/badge/commands/seancrowe01/ads-machine/ad-autoresearch.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,311 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.01311
Opus 5 $0.00022 $0.00656
Sonnet 5 $0.00009 $0.00262
Haiku 4.5 $0.00004 $0.00131

Measured 8d ago against content hash 61f62289992a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

ad-autoresearch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/commands/ad-autoresearch.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ad Auto-Research

You are a daily research analyst. You compare what changed in the market yesterday against your current ads and swipe file, then produce specific, actionable suggestions for improvement.

What you produce: A daily brief with competitor moves, gap analysis, and improvement suggestions tied to specific Pipeline records.


Config

Read from CLAUDE.md:

Airtable Base ID: YOUR_AIRTABLE_BASE_ID
Ad Swipe File Table: YOUR_SWIPE_FILE_TABLE_ID
Ad Pipeline Table: YOUR_PIPELINE_TABLE_ID
Competitors Table: YOUR_COMPETITORS_TABLE_ID

Step 1: What Changed Yesterday

Pull ads added or updated in the last 24 hours:

Use Airtable MCP: list_records
  table_id: {Swipe File table ID}
  filter: IS_AFTER({Scrape Date}, DATEADD(TODAY(), -1, 'days'))
  fields: Ad Archive ID, Competitor, Angle Category, Ad Format Type, Display Format, Hook Copy, Body Text, Days Active, Longevity Tier, Is Active

Categorise:

  • New launches: Scrape Date = yesterday, Start Date = recent
  • Newly killed: Was active last scrape, now inactive
  • Crossed a tier threshold: Moved from Testing -> Solid, Solid -> Performer, or Performer -> Long-Runner since last check

Step 2: What Are You Running

Pull your active Pipeline ads:

Use Airtable MCP: list_records
  table_id: {Pipeline table ID}
  filter: OR({Status}='Active', {Status}='Launched')
  fields: Name, Angle, Format, Hook, Status, Launch Date, Spend, Leads, CPL, Verdict

Step 3: Compare and Analyse

3a. Competitor Moves

For each new competitor ad launched yesterday:

  • What angle are they testing?
  • What format?
  • Is this a new direction for them or more of the same?
  • Are they copying something that's already working for someone else?

3b. Gap Analysis

Compare competitor angles and formats against YOUR active ads:

  • Angles they're using that you're not -- potential gap
  • Formats they're testing that you haven't tried -- potential opportunity
  • Hooks that are working for them (Long-Runners) that you haven't adapted -- low-hanging fruit

Read the full file on GitHub · 142 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 142 lines · 43 tokens per session scan A 61f62289992a

Subscribe to this mod's changes

ad-autoresearch is a command published in the GitHub repository seancrowe01/ads-machine (20 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 1,311 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other commands, from other repositories

competitive-auction-insights

Analyze Google Ads auction insights to understand competitive landscape. See who you're competing against and where you're winning or losing. (requires Pro subscription).

Ad-Superpowers/ad-superpowers-plugin · 27 tokens

organic-performance-audit

Comprehensive audit of your organic search performance using Google Search Console data. Analyzes keyword rankings, CTR optimization opportunities, page performance, search type breakdown, and trend analysis with actionable recommendations. (requires Pro subscription).

Ad-Superpowers/ad-superpowers-plugin · 43 tokens

creative-fatigue-scanner

Proactively detect ad creative fatigue before performance tanks. Uses platform-specific evidence-based thresholds: TikTok fatigues 4x faster (3-7 days) than Meta (14 days). Google Ads uses asset ratings (4-8 weeks). LinkedIn has longest cycles (4-6 weeks). (requires Pro subscription).

Ad-Superpowers/ad-superpowers-plugin · 64 tokens

incrementality-test-designer

Design rigorous incrementality tests to measure true advertising impact. Includes geo lift test planning, holdout/control group design, statistical power calculations, test duration recommendations, and cost-benefit analysis. Essential for understanding true incremental ROAS in a privacy-first measurement landscape.…

Ad-Superpowers/ad-superpowers-plugin · 56 tokens

meta-audit

Full Meta Ads account audit, tailored to ecommerce or lead-gen automatically.

HYPD-AI/ads-mcp-plugin · 13 tokens

exa-crawl

Crawl one or more URLs via Exa with the tuned parameters, honest status classification, and the cached-index fallback.

thangnguyenworkspace/company-research-pipeline · 23 tokens