Trend & Report Summarizer

What you need to provide

The prompt asks for 3 inputs. Have these ready before you paste it in:

1. 1Role
2. 2Priorities
3. 3Report / Article / Transcript

How to use it

1. 1Paste the prompt, then paste the report or transcript below it.
2. 2Tell it your role and what you care about.
3. 3Ask it to go deeper on any section that matters.
4. 4Ask for a 5-bullet version to share with the team.

See it in action

Report summary — category trends (filtered for: better-for-you + pricing, snack brand)

Key takeaways (most relevant to you):
1. "Permissible indulgence" is rising — shoppers want treats with a better-for-you angle (portion, protein, less sugar), not pure restraint.
2. Shrinkflation backlash is real — shoppers notice smaller packs and resent it more than open price rises.
3. GLP-1 users are reshaping snacking toward smaller portions and protein — early but worth watching.
4. Private label is growing on value — pressure on mid-tier brands.

What it means for your brand:
- Better-for-you: a permissible-indulgence angle (e.g. portion-controlled or protein-forward) fits the trend better than going fully "healthy."
- Pricing: protect trust — if you must take price, avoid quiet shrinkflation; shoppers punish it. Consider value framing vs private label.

Findings vs opinion:
- Solid (data-backed in the report): private-label growth, shrinkflation sensitivity.
- Author opinion / early signal: GLP-1's long-term snacking impact — directional, not proven. Don't over-rotate yet.

Questions it raises:
- Is your portion/pack format on the wrong side of the shrinkflation backlash?
- Do you have a permissible-indulgence SKU, or only indulgent and "diet"?

Recommended actions:
1. Audit pack sizes for shrinkflation risk before any price move.
2. Scope a permissible-indulgence variant (portion or protein).
3. Monitor GLP-1 signals; don't bet the plan on it yet.

Want a 5-bullet version for the team, or a deeper dive on the GLP-1 section?

The prompt

This is the actual prompt, not a teaser. Read the opening below, then unlock the free bundle with your email for the full version plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a food & beverage insights analyst who turns long documents into decision-ready reads. You filter through the reader's lens, pull out what's relevant, and separate solid findings from opinion. You never pad with a generic abstract.

# Context I'll provide
- My role / lens: [ROLE]
- What I care about: [PRIORITIES]
- The document (paste below): [REPORT / ARTICLE / TRANSCRIPT]

# Your task
1. If the document or my lens is missing, ask for them before summarizing.
2. Pull the key takeaways most relevant to my priorities (not a flat summary).
3. Spell out what it means specifically for my brand/category.
4. Separate solid, data-backed findings from the author's opinion or early signals.
5. Surface the questions it raises and recommend actions.

What's in the free download

Frequently asked questions

How is this different from just asking for a summary?

A generic summary abstracts the whole document evenly. This skill filters it through your role and priorities, pulls only what's decision-relevant, separates evidence from opinion, and ends with recommended actions. You get the 'so what' for your brand, not a shorter version of everything the document happened to cover.

Will it make up statistics from the report?

No. It works only from the text you paste and is instructed never to invent figures, quotes, or claims that aren't there. If a number isn't in the document, it won't appear in the summary — which matters when you're about to repeat a statistic in a meeting or a deck.

Can it handle earnings calls and articles, not just reports?

Yes. Paste any long-form text — a market report, a trend deck's notes, a news article, or an earnings-call transcript — and it applies the same lens-filtered read. It's built for anyone who's sent more to read than they can act on and needs the relevant signal fast.

How does it separate fact from hype?

It explicitly splits solid, data-backed findings from the author's opinion or early signals, so you can tell a proven shift from a speculative one. That stops you over-rotating on a buzzy but unproven claim. Cross-checking the key claims against live demand data confirms whether a trend is actually showing up in real behavior.