ad-report

ad-report is a command for Claude Code from seancrowe01/ads-machine. It costs 46 tokens per session (1,345 once invoked), scanned A, original, MIT.

A command that turns advertising data from an Airtable Swipe File into a competitor intelligence report. The report summarizes what competing advertisers are running and how their ads are performing over time.

In plain words
What is it for?
Use it to report new ads, long-running ads, stopped ads, recurring themes, and effective opening messages from tracked competitors.
Why use it?
It removes the need to manually review ad records and assemble weekly findings. It makes changes in competitors’ ads, messaging, and activity easier to share with a team or client.

Command for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/seancrowe01/ads-machine/ad-report
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-report

README.md
[![agentmods](https://agentmods.dev/badge/commands/seancrowe01/ads-machine/ad-report.svg)](https://agentmods.dev/commands/seancrowe01/ads-machine/ad-report)
Your own site
<a href="https://agentmods.dev/commands/seancrowe01/ads-machine/ad-report"><img src="https://agentmods.dev/badge/commands/seancrowe01/ads-machine/ad-report.svg" alt="Measured on agentmods" height="20"></a>
Per session 46 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,345 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00046 $0.01345
Opus 5 $0.00023 $0.00673
Sonnet 5 $0.00009 $0.00269
Haiku 4.5 $0.00005 $0.00135

Measured 4d ago against content hash 60475e4e6363, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ad-report 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 4d 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-report.md · 178 lines

How it starts

The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ad Intelligence Report

You generate a formatted competitor intelligence report from the Ad Swipe File. This is a snapshot of what's happening in the market right now -- who's running what, what's working, what's dying, and what hooks are winning.

What you produce: A markdown report ready to share with clients, your team, or stakeholders.


Config

Read from CLAUDE.md:

Airtable Base ID: YOUR_AIRTABLE_BASE_ID
Ad Swipe File Table: YOUR_SWIPE_FILE_TABLE_ID
Competitors Table: YOUR_COMPETITORS_TABLE_ID
Business: YOUR_BUSINESS_NAME

Step 1: Pull the Data

Fetch all ads from the Swipe File:

Use Airtable MCP: list_records
  base_id: {from CLAUDE.md}
  table_id: {Swipe File table ID}
  fields: Ad Archive ID, Competitor, Page Name, Angle Category, Ad Format Type, Display Format, Days Active, Longevity Tier, Start Date, End Date, Hook Copy, Body Text, CTA Type, Is Active, Scrape Date

Also fetch competitor list:

Use Airtable MCP: list_records
  table_id: {Competitors table ID}
  filter: {Status}='Active'
  fields: Name, Niche Tier

Step 2: Calculate the Numbers

From the data, calculate:

  • Total ads tracked across all competitors
  • New ads this week (Start Date within last 7 days)
  • Killed ads this week (End Date within last 7 days, no longer active)
  • Long-Runners (60d+) -- total count and any new ones
  • By competitor: ad count, format breakdown, most common angle
  • By angle: which angles have the most Long-Runners
  • By format: which formats have the most Long-Runners
  • Top 5 hooks from Long-Runners (sorted by Days Active)

Step 3: Generate the Report

# Competitor Ad Intelligence Report
**{Business Name}** | Week of {date range} | Generated {today}

---

## Market Snapshot

| Metric | Count |
|---|---|
| Competitors tracked | {N} |
| Total ads in swipe file | {N} |
| New ads this week | {N} |
| Ads killed this week | {N} |
| Long-Runners (60d+) | {N} |

---

## What's Working (Long-Runners)

These ads have been running 60+ days. Someone is paying to keep them alive.

{For each Long-Runner, show:}
1. **{Competitor}** -- {Days Active}d -- {Angle} -- {Format}
   Hook: "{first line of copy}"
   [View in Ad Library]({ad library url})

---

## What's New This Week

{N} new ads launched across {N} competitors.

| Competitor | New Ads | Formats | Dominant Angle |
|---|---|---|---|
| {name} | {count} | {formats} | {angle} |

---

## What Got Killed

{N} ads stopped running this week.

| Competitor | Killed | Avg Days Active | Common Angle |
|---|---|---|---|
| {name} | {count} | {avg days} | {angle} |

Short-lived ads (<7 days) suggest failed tests. Track what they tried and why it might have failed.

---

## Angle Breakdown

| Angle | Total Ads | Long-Runners | Win Rate |
|---|---|---|---|
| {angle} | {count} | {lr count} | {lr/total %} |

Win rate = Long-Runners / Total. Higher win rate means this angle consistently works in your market.

---

## Top 5 Proven Hooks

These hooks have the longest run times in your swipe file.

1. **{days}d** | {competitor} | {angle}
   "{hook text}"

2. **{days}d** | {competitor} | {angle}
   "{hook text}"

3. ...

---

## Recommendations

{Write 3-5 specific, actionable recommendations based on the data above. Examples:}

- "Social proof is the dominant winning angle (X% win rate). Prioritize testimonial-style ads."
- "{Competitor} launched 8 new ads this week, all video UGC. They're testing at volume -- watch for which ones survive to 14d+."
- "No competitors are using [angle]. This is a gap you could test."
- "The top 3 hooks all use question openers. Test question hooks in your next batch."

Read the full file on GitHub · 178 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. 4d ago First seen · 178 lines · 46 tokens per session scan A 60475e4e6363

Subscribe to this mod's changes

ad-report is a command published in the GitHub repository seancrowe01/ads-machine (20 stars, last pushed 5mo ago), licensed MIT. It adds 46 tokens to every session and 1,345 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.

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