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gooseworks-ai / composites-ad-angle-miner

Ad Angle Miner

Mine the highest-converting ad angles from customer reviews, Reddit complaints, support tickets, and competitor ads. Extracts actual pain language, competitor weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank with proof quotes…

agent codexmodel gpt-5.5snapshot python312-uveval programmatic8 stepsv1.0.0

Deploy Ad Angle Miner to your jetty.io

One-click installs this runbook into a collection on your Jetty account. You can run it from the Spot dashboard, schedule it, or pipe inputs in via the API.

The shape of the run

8 steps · start to finish.

  1. 1
    Step 1

    Environment Setup

    Create /app/results and verify the collection plan before making network requests. If Apify sources are selected, verify APIFY_API_TOKEN is set; otherwise continue with pasted files and web-search-accessible sources.

    mkdir -p /app/results
    if [ -z "${APIFY_API_TOKEN:-}" ]; then
      echo "APIFY_API_TOKEN not set; skip Apify-only collectors unless pasted evidence is provided."
    fi
    
  2. 2
    Step 2

    Intake

    Capture the product, two to five competitors, ICP, selected data sources, and any angles already tested. Convert the intake into a collection plan with explicit source names, queries, item limits…

  3. 3
    Step 3

    Source Collection

    Collect evidence from the selected sources. For Amazon reviews, start web_wanderer/amazon-reviews-extractor, poll until the actor succeeds, and fetch the dataset items. For Reddit, use…

  4. 4
    Step 4

    Evidence Normalization

    Normalize each evidence item into source_evidence.json with source type, product or competitor, rating or sentiment where available, text excerpt, URL or file provenance, date if present, and tags…

  5. 5
    Step 5

    Angle Extraction

    Extract candidate angles from repeated buyer language. Preserve exact proof quotes, especially complaints, outcome phrases, and comparison language. Group near-duplicates into a single angle and…

  6. 6
    Step 6

    Score and Rank Angles

    Score each angle using evidence volume, intensity of language, source diversity, ICP fit, competitor weakness, and novelty against tested angles. Produce a ranked bank with recommended ad formats…

  7. 7
    Step 7

    Iterate on Errors (max 3 rounds)

    If evidence is thin, scoring is tied, or quotes are not attributable, run targeted follow-up collection for max 3 rounds. Each round must name the missing evidence, the exact query or file to…

  8. 8
    Step 8

    Write Outputs

    Write angle_bank.md, angle_bank.csv, source_evidence.json, summary.md, and validation_report.json under /app/results. The Markdown summary should call out top angles, proof quotes, recommended ad…