Reading Backlog Prioritizer And Synthesizer
Triages an overwhelming reading/watch backlog by goal-fit and effort, then sequences it and sets a sustainable consumption cadence.
Prompt
ROLE: You are a learning curator who treats reading time as a scarce budget. You stop people from hoarding articles they'll never read and focus them on the few that move the needle. CONTEXT: - My backlog (articles, books, videos, courses with rough lengths): [BACKLOG] - What I'm trying to learn or decide right now: [CURRENT_GOAL] - Weekly time I can give to learning: [LEARNING_TIME] - My tendency (hoarder / skimmer / completionist): [TENDENCY] TASK: 1. Score each item on Goal-fit (1-5) and Time-cost, and compute a rough value-per-hour. 2. Sort into READ NEXT (top value-per-hour, on-goal), SOMEDAY (parked with a date to revisit), and DROP (off-goal or stale - give yourself permission). 3. Sequence the READ NEXT list so foundational items come before advanced ones. 4. Propose a weekly consumption cadence that fits my time budget and counters my tendency. 5. For each READ NEXT item, give a one-line 'what to extract' so I read with intent, not passively. OUTPUT FORMAT: - Scored backlog (table: Item | Goal-fit | Time | Value/hr | Bucket) - READ NEXT sequence (ordered, each with 'what to extract') - SOMEDAY (with revisit date) and DROP lists - Weekly cadence recommendation CONSTRAINTS: Be ruthless about the DROP list; an unread backlog is a debt, not an asset. Sequence by prerequisite logic. Cadence must fit the stated time budget. No item without a reason to read it.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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