Scan an AgentMail inbox for newsletter signals using configurable keyword campaigns. Extracts matched keywords, context snippets, and company mentions from incoming emails. Use for monitoring accounting industry newsletters for buying signals like acquisitions, migrations, and staffing news.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Scan an AgentMail inbox for newsletter signals using configurable keyword campaigns. Designed for monitoring accounting industry newsletters for buying signals like acquisitions, Sage Intacct migrations, staffing challenges, and technology adoption.
# Set your API key
export AGENTMAIL_API_KEY="your_key_here"
# Scan inbox with all campaigns (summary view)
python3 skills/newsletter-monitor/scripts/scan_newsletters.py --output summary
# Scan specific campaign, last 7 days
python3 skills/newsletter-monitor/scripts/scan_newsletters.py --campaign acquisitions --days 7 --output summary
# JSON output for downstream processing
python3 skills/newsletter-monitor/scripts/scan_newsletters.py --output json --limit 50
pip3 install agentmail python-dotenv
Keyword campaigns are defined in config/campaigns.json. Each campaign has a description and a list of keywords for case-insensitive substring matching.
Built-in campaigns:
| Flag | Description | Default |
|---|---|---|
--campaign NAME | Run only a specific campaign | All campaigns |
--days N | Only scan emails from last N days | No limit |
--keywords "a,b,c" | Custom keywords (overrides campaigns) | Use campaigns.json |
--output json|summary | Output format | json |
--inbox ADDRESS | Override inbox address | AGENTMAIL_INBOX env or supergoose@agentmail.to |
--limit N | Max messages to fetch | 100 |
Returns an array of matched messages with:
message_id, from, subject, datematched_campaigns - which campaigns triggeredmatched_keywords - specific keywords foundcontext_snippets - 200-char window around each matchcompanies_mentioned - capitalized multi-word phrases near matchesHuman-readable report showing matched emails grouped by campaign with snippets and detected companies.
When newsletter signals are found, chain to:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
日本語の概要は準備中です。原文の説明を表示しています。
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
日本語の概要は準備中です。原文の説明を表示しています。
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
日本語の概要は準備中です。原文の説明を表示しています。
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
日本語の概要は準備中です。原文の説明を表示しています。