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.
npx agentmods add commands/seancrowe01/ads-machine/ad-reportgit clone --depth 1 https://github.com/seancrowe01/ads-machineWrote 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.
[](https://agentmods.dev/commands/seancrowe01/ads-machine/ad-report)<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>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.
| Model | Per session | Once 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 |
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.
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."
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.
- 4d ago First seen · 178 lines · 46 tokens per session scan A 60475e4e6363
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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