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Recirculate Posts That Already Worked

On feeds, a post reaches a small fraction of your followers, so the same post can be genuinely new to most of your audience six months later. Teams avoid reposting out of a fear of looking repetitive that the data does not support, and instead burn effort on new material that performs worse. The trap is naive reposting - identical text on the same account reads as lazy, and some platforms deduplicate near-identical content, so the method is structured rewriting rather than resharing.

CATEGORY
Social & distribution
FORMAT
evergreen-post-recirculation.md
STEPS
6
PRICE
Free - no account
WHEN TO REACH FOR THIS

Use when your best-performing posts are months old and buried, and you are writing new material from scratch every week.

The skill file

evergreen-post-recirculation.md 
---
name: evergreen-post-recirculation
description: Use when your best-performing posts are months old and buried, and you are writing new material from scratch every week.
---

# Recirculate Posts That Already Worked

On feeds, a post reaches a small fraction of your followers, so the same post can be genuinely new to most of your audience six months later. Teams avoid reposting out of a fear of looking repetitive that the data does not support, and instead burn effort on new material that performs worse. The trap is naive reposting - identical text on the same account reads as lazy, and some platforms deduplicate near-identical content, so the method is structured rewriting rather than resharing.

## What you need first

- at least six months of post-level performance data
- the original posts in a form you can edit
- a note of what has changed since each was written

## Method

1. Rank past posts by saves and comments rather than likes, because saves are the strongest signal that a post has standing value worth showing to a new audience.
2. Filter out anything tied to a date, a product version, or a news event - only claims that are still true qualify for recirculation.
3. Rewrite rather than repost: keep the finding, change the opening, swap the example for a newer one. This clears deduplication and gives you a genuine reason to post it.
4. Leave at least four to six months between an original and its rewrite on the same platform, and longer if the original went unusually wide.
5. Move winners across platforms before recirculating on the same one - a strong X thread is usually unpublished material for your LinkedIn audience.
6. Track the rewrite against the original's numbers; if a rewrite consistently beats the original, the topic is a pillar and deserves a proper article, not more posts.

## What this produces

A ranked library of evergreen posts with rewrite dates and cross-platform status.

## Where this goes wrong

- ranking by likes, which selects for agreeable posts rather than useful ones
- reposting verbatim, which triggers deduplication and reads as filler to your most engaged followers
- recirculating something whose underlying claim has quietly stopped being true

---

From the QuQi skill library - https://www.quqi.io/skills/evergreen-post-recirculation
Free to download · no account, no email

What you need first

  • at least six months of post-level performance data
  • the original posts in a form you can edit
  • a note of what has changed since each was written

Method

  1. 01 Rank past posts by saves and comments rather than likes, because saves are the strongest signal that a post has standing value worth showing to a new audience.
  2. 02 Filter out anything tied to a date, a product version, or a news event - only claims that are still true qualify for recirculation.
  3. 03 Rewrite rather than repost: keep the finding, change the opening, swap the example for a newer one. This clears deduplication and gives you a genuine reason to post it.
  4. 04 Leave at least four to six months between an original and its rewrite on the same platform, and longer if the original went unusually wide.
  5. 05 Move winners across platforms before recirculating on the same one - a strong X thread is usually unpublished material for your LinkedIn audience.
  6. 06 Track the rewrite against the original's numbers; if a rewrite consistently beats the original, the topic is a pillar and deserves a proper article, not more posts.

What this produces

A ranked library of evergreen posts with rewrite dates and cross-platform status.

Where this goes wrong

  • ranking by likes, which selects for agreeable posts rather than useful ones
  • reposting verbatim, which triggers deduplication and reads as filler to your most engaged followers
  • recirculating something whose underlying claim has quietly stopped being true

Use this skill in your own AI

The download is a plain markdown file with the name and trigger in its frontmatter. Where an assistant supports skills it can load itself, that frontmatter is what it reads to decide this one applies.

Claude Code Save it as ~/.claude/skills/evergreen-post-recirculation/SKILL.md and Claude loads it on its own when what you are doing matches the trigger line. Put it in .claude/skills inside a project instead if the whole team should have it.
Claude Upload the file in the skills section of your settings. Once it is there it applies itself in any conversation where the trigger fits, so you do not have to remember it exists.
ChatGPT There is no skills format to install into, so paste the file contents into a Project instruction or a Custom GPT instead. It then applies to every chat in that project rather than only the one you paste it into.
Anything else Paste the markdown into the chat before your question. It works in any assistant, it just has to be pasted again each time.

Questions about this skill

When is recirculating better than writing something new?

When your best posts are months old and buried while you write from scratch every week. A feed shows a post to a fraction of your followers, so the same finding is genuinely new to most of your audience half a year later. The fear of looking repetitive is not supported by how few people saw it the first time.

What do I need before I can pick candidates?

At least six months of post-level performance data, the originals in a form you can edit, and a note of what has changed since each was written. Six months is a real threshold: rank a shorter window and you are selecting on recency. Rank on saves and comments, since likes select for agreeable posts and saves for ones with standing value.

What do I end up with, and which part gets used?

A ranked library of evergreen posts with rewrite dates and cross-platform status. The rewrite is the working part: keep the finding, change the opening, swap the example for a newer one. That clears deduplication and gives you a reason to post it. Leave four to six months on the same platform, longer if the original went unusually wide.

What ruins this most often?

Recirculating a claim that has quietly stopped being true, which is the one error your most engaged followers will notice and correct in public. Reposting verbatim is the cheaper failure, triggering deduplication and reading as filler. Filter out anything tied to a date, a product version or a news event before it reaches the shortlist.

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