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Operations & ContentWeekly & Monthly Recap#weekly#BLUF#operations

Operations Weekly Recap · Bottom-Line-Up-Front

Compress this week's scattered work into 1 conclusion + 3 numbers + 2 next actions in 5 minutes.

Pain addressed · Pain #2 · scenario gap: ops people know AI can help write the weekly, but they don't know what to feed it or where to edit. Pain #1 in parallel: a checklist blocks fluff like 'continuing to push.'

Operations Weekly Recap · Bottom-Line-Up-Front

Prompt·Beginner
Also available in 中文 →
You are my weekly recap assistant. I'm an Operations Manager. Write my weekly report in BLUF (Bottom Line Up Front) structure.

# My raw notes for this week
{paste_raw_log}
Note: includes OKRs, project progress, data, ad-hoc items; order doesn't matter.

# Writing rules
1. First paragraph (1-2 sentences): overall conclusion. NO filler like "continuously improving" — use a judgment sentence (e.g. "User activity hit a Q2 high this week, mostly driven by new channel A").
2. Second paragraph: 3 key numbers ("metric — current value — WoW/MoM — short explanation"), max 30 words each.
3. Third paragraph: completed items (bullets, each ≤ 20 words, include project name).
4. Fourth paragraph: 1-2 risks. MUST include how I plan to solve, not just list.
5. Fifth paragraph: top 2 actions for next week. Only two, must be measurable and dated.

# Must avoid
- Empty phrases: "continuously optimize", "steadily advance", "empower", "synergize"
- Putting numbers before the conclusion
- Going over 350 words

Output only the report body. No preamble, no explanation.
Saves ~45 min per use

Open directly on your favourite AI

One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V).

Workflow · 5 steps

  1. 1

    Dump your week into one block of raw notes — calendar events, Slack threads, dashboards, anything you touched. Messy is fine; the model will structure it.

  2. 2

    Paste the prompt into ChatGPT / Claude / DeepSeek and replace {paste_raw_log} with your dump.

  3. 3

    Make exactly three edits to the first draft: tighten the BLUF sentence into something you'd actually say to your manager, fact-check the three numbers against the dashboard, and add real deadlines to next week's two actions.

  4. 4

    Run the verify checklist; delete any remaining filler phrases like 'continue to optimize' or 'drive alignment'.

  5. 5

    Send to your manager; @-ping them on the conclusion line only, not the whole report.

Verify before you ship

AI's "almost right" is the #1 pain. Tick these off so reviewers can't find the obvious holes. (Checkbox state stays local — we don't track it.)

Done 0 / 5 checks

Why this prompt works

This prompt isn't a generic “write me a Weekly & Monthly Recap”. It folds in the concrete context a Operations & Content actually faces (target reader, info density, deliverable format). LLMs deliver an order-of-magnitude better answer when they get role × scenario × constraints together — they can make the right tradeoffs about what to spotlight and what to cut.

When to use

Best moment: you know roughly that you need to do a Weekly & Monthly Recap, but you're stuck on “how do I even ask the AI?” Muscle memory has no template. Copy this card, tweak 2-3 fields per the workflow, and get a first revisable draft in 30 seconds.

Common pitfalls

  • Send without tweaking placeholders · the draft reads as default-GPT and your reviewer spots it in 3 seconds.
  • Skip the verify checklist · “almost right” failures usually hide in a thing you assumed the model handled but it didn't.
  • Stop after the first answer · a single follow-up like “make section X tighter / more formal” usually pushes a 70 → 90.

Variants & extensions

Want to use this card more aggressively? Three extensions:

  • Swap languages: both EN/ZH prompts ship in the card — use the EN version when shipping to a Western audience.
  • Swap platforms: the same prompt has slightly different taste on ChatGPT / Claude / Kimi. The recommended-platform pill marks the best fit.
  • Stack the checklist: paste the verify checklist back into the chat and ask the AI to self-audit — error rate drops sharply.
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