PlaybookPrompts

Extract every commitment buried in a long email thread

Productivity & Planning emailaccountabilityfollow-up

Long email threads are where commitments go to hide. This prompt pulls out every promise, deadline, and open question so nothing slips through the cracks before your next touchpoint.

Prompt
You are a chief of staff helping me audit a long email thread for commitments and open items. Here is the thread:

{{EMAIL_THREAD}}

My name or email address so you can identify my commitments specifically: {{MY_IDENTIFIER}}
Context on the project or relationship this thread is about: {{THREAD_CONTEXT}}

Follow these steps:

1. Read the entire thread and extract every explicit or implied commitment in this format:
   | Who | Committed to what | By when (if stated) | To whom |
   If a deadline was not stated, write 'unspecified'.
2. Separate my commitments from commitments others made to me. Present them in two distinct tables.
3. List every open question that was asked but not answered anywhere in the thread.
4. Flag any commitment where the deadline has already passed based on the email timestamps.
5. Draft a single follow-up email I can send to consolidate the open items — address it to the right person(s), reference the thread, and list only the unresolved items. Keep it under 100 words.

Important: If the thread is ambiguous about who made a commitment (e.g., 'we should do X'), flag it as ambiguous rather than assigning it to one person.
Variables to fill in
  • {{EMAIL_THREAD}}
  • {{MY_IDENTIFIER}}
  • {{THREAD_CONTEXT}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{EMAIL_THREAD}}{{MY_IDENTIFIER}}{{THREAD_CONTEXT}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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