Content Retro

• Source: Part of The Content Engine suite, seven skills built from the one agent per task workflow Mark Fershteyn described on the GTM AI Podcast with Coach K. Published as received.
• Frameworks applied: Three-question retro, dated rules in learnings.md, rules override skill defaults

It is the only step that makes the next run better. Feedback that lives in a chat window disappears; feedback written as a rule gets applied automatically.

Known weakness, published as found: It only improves output if you answer honestly and keep learnings.md with the workspace. Lose the file and the suite forgets everything.

1. 1.Copy the SKILL.md text below, or download the raw file.
2. 2.Create a folder named exactly content-retro in your agent's skills directory.
3. 3.Save the file inside that folder as SKILL.md.
4. 4.Ask the agent one of the trigger requests below.
5. 5.Check the output against what you already know before it leaves your desk.

• run a retro on this piece
• remember that I hate this phrasing
• save this feedback as a rule for next time

• rewrite this post
• write the next article

---
name: content-retro
description: Runs a three-question retro after a piece of content is finished and turns the answers into short, dated rules saved to learnings.md so every future run improves. Use when the user says "run a retro," "what should we change next time," "save this feedback," "remember that I hate this," "update my content rules," "learn from this," or when the content-engine runs Step 6. Does not rewrite the content itself.
---

# Content Retro

One job: turn feedback into rules the other skills follow next time.

This is how a skill gets better on its own. Without it, you give the same feedback forever.

## STEP 1 (REQUIRED): Ask three questions, one at a time

1. "What worked in this output?"
2. "What did you change, cut, or hate?"
3. "What should I do differently next time?"

Wait for each answer. If the user says "it was fine," ask: "What's one line you'd rewrite before posting?"

If the user already edited the output, compare their version against `repurposed.md` or `edited.md` and point out what changed: "You cut every sentence that started with 'So.' Want that as a rule?"

## STEP 2 (REQUIRED): Convert answers into rules

Each rule must be:
- **Short:** one line
- **Testable:** you can check a piece against it
- **Scoped:** tagged to the skill it applies to

| Feedback | Bad rule | Good rule |
|---|---|---|
| "It sounded stiff." | "Be more casual." | `[editor] Use contractions. Max 18 words per sentence.` |
| "The hooks were all questions." | "Better hooks." | `[hooks] Max one question hook out of five.` |
| "Too many bullets on LinkedIn." | "Fewer bullets." | `[repurposer] LinkedIn: one list max, 4 items max.` |
| "You made up a stat." | "Be accurate." | `[drafter] Never state a number the user didn't give.` |

Tags: `[interviewer]` `[drafter]` `[editor]` `[hooks]` `[repurposer]` `[all]`

## STEP 3 (REQUIRED): Check for conflicts, then save

1. Read `learnings.md`. If a new rule contradicts an old one, show both and ask which wins. Delete the loser.
2. If a rule repeats an old one, skip it.
3. Append new rules under today's date:

```markdown
## 2026-09-23 (topic: one agent rule)
- [hooks] Max one question hook out of five.
- [editor] Use contractions. Max 18 words per sentence.
```

4. Confirm to the user: "Saved 2 rules. learnings.md now holds 14."

## Keep the file lean

When `learnings.md` passes 40 rules, offer to consolidate: merge duplicates and drop rules the user hasn't needed in the last 10 runs. A short rule list gets followed. A long one gets skimmed.

## Edge cases

- **User gives praise only:** save what worked as a rule to keep doing ("[hooks] Number hooks performed best. Lead with one.").
- **Feedback is about the topic, not the writing:** save it to the "Next time" list in `interview.md`, not to `learnings.md`.
- **No `learnings.md` yet:** create it with a one-line header: `# Content rules (read before every run)`.

What does the Content Retro skill do?

Asks three questions after the piece is done and converts the answers into short dated rules in learnings.md. Every other skill in the suite reads that file and treats its rules as overrides.

Where does the Content Retro skill come from?

Part of The Content Engine suite, seven skills built from the one agent per task workflow Mark Fershteyn described on the GTM AI Podcast with Coach K. Published as received.. It was written by The Revenue AI Report against a 12 criterion quality rubric and graded in an independent scoring pass.

Why was the Content Retro skill chosen for this library?

It is the only step that makes the next run better. Feedback that lives in a chat window disappears; feedback written as a rule gets applied automatically.

When should the Content Retro skill not be used?

Do not use it for: rewrite this post Or: write the next article

How do I install the Content Retro SKILL.md file?

Download the file, create a folder named exactly content-retro inside your agent's skills directory, and save the file inside it as SKILL.md. The agent loads it when a request matches the description.