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dedup-strategy
Deduplication strategy for identifying when multiple newsletters cover the same story. Use when reviewing or improving dedup logic.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Deduplication strategy for identifying when multiple newsletters cover the same story. Use when reviewing or improving dedup logic.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Create and manage OpenHands automations - scheduled tasks that run in sandboxes. Use for cron-scheduled automations.
Parse email newsletters into individual stories. Use when structural/heuristic parsing fails and LLM extraction is needed.
Run the DailyMe newsletter processing pipeline. Handles email ingestion, parsing, dedup, ranking, and optional digest delivery.
| name | dedup-strategy |
| description | Deduplication strategy for identifying when multiple newsletters cover the same story. Use when reviewing or improving dedup logic. |
| triggers | ["dedup","duplicate","merge","same story"] |
Multiple AI newsletters often cover the same story. "TLDR AI", "The Batch", and "Ben's Bites" might all report on the same GPT-5 announcement. We want to group these into one story with attribution to all sources.
Strip tracking parameters and normalize:
utm_*, ref, source, campaign, mc_*, fbclid, gclidwww. prefixExample: https://www.techcrunch.com/article/?utm_source=tldr → techcrunch.com/article
Code: app/processing/dedup.py::canonicalize_url()
If two stories have the same canonical URL → they're duplicates.
Example: "OpenAI Releases GPT-5" vs "GPT-5 Released by OpenAI" → Jaccard ≈ 0.75 → duplicate
sentence-transformers/all-MiniLM-L6-v2 (384-dim, local)story_groups, not removed. The user can see "covered by 3 newsletters."newsletter_id so we can show "via TLDR AI, The Batch, Ben's Bites."SELECT sg.title, sg.story_count FROM story_groups sg WHERE sg.story_count > 1 ORDER BY sg.story_count DESC