collect-feedback

name	collect-feedback
description	Closes the content pipeline feedback loop after a Medium article or LinkedIn post has been published. Provide the published Medium URL and/or the final LinkedIn post text you actually used. Compares the published version against the original draft, extracts style and structural learnings from your edits, and updates memory/brand/writing_style.md so future drafts need fewer edits. Also logs each round to memory/brand/feedback-log.md. Trigger after publishing: 'collect feedback [medium URL]', 'I posted, here is what I changed', or 'update writing style from my Medium post'.
metadata	{"openclaw":{"emoji":"🔁"}}

Collect Feedback

Learns from the gap between draft and final published post. Updates writing style memory so each draft cycle improves.

When to Use

• "collect feedback [Medium URL]"
• "I posted the article, here's what I changed: ..."
• "update writing style from my Medium post"
• "learn from what I published"

Inputs

Accept one or both of:

1. Medium URL — fetch and read the final published article
2. Final LinkedIn post text — paste the variant that was actually used

If neither is provided, ask for at least one before proceeding.

Process

Step 1 — Load originals

Read:

• outputs/content/article.md — original draft
• outputs/content/linkedin-posts.md — original LinkedIn variants

If files are missing, note it and proceed with what is available.

Step 2 — Load finals

• If Medium URL provided: fetch the full article text from the URL
• If LinkedIn text provided: use as-is

Step 3 — Compare and extract learnings

Analyse the diff between original draft and final published version across these dimensions:

Article (Medium):

• Title: was it changed? How? (shorter, punchier, different angle)
• Hook: was the first sentence/paragraph edited? What pattern?
• Structure: sections added, removed, reordered?
• Length: longer or shorter than the draft?
• Voice: any recurring word substitutions or phrase replacements?
• Evidence: did the author add or cut specific examples/data?
• Conclusion: was the ending changed?
• Visuals: did the author add images/media manually? (note as recurring behaviour)

LinkedIn:

• Which variant was used (Insight / Story / Question)?
• How was the hook edited?
• Was the length changed?
• Were hashtags changed?
• Was the CTA changed?

Step 4 — Generate learnings

Summarise edits as actionable style rules. Format each learning as:

- [What to do differently]: [specific pattern observed]

Examples:

• Shorten titles: author consistently trims titles to under 8 words
• Strengthen hooks: first sentence is always rewritten to remove scene-setting
• LinkedIn preference: Variant 2 (Story) is consistently chosen over Insight
• Add images: author always adds a visual manually — include an image suggestion in drafts

Step 5 — Update writing style memory

Append learnings to memory/brand/writing_style.md under a ## Learned from Published Posts section. If the section already exists, append new learnings without duplicating existing ones.

Step 6 — Log the feedback round

Append to memory/brand/feedback-log.md:

## [Date] — [Article title or slug]

**Medium:** [URL or "not provided"]
**LinkedIn variant used:** [Insight / Story / Question / unknown]

### Edits observed
[bullet list of specific changes]

### Learnings added to writing_style.md
[bullet list of rules added]

Create the file if it does not exist.

Step 7 — Confirm

Reply with:

• Number of learnings extracted
• 3 most impactful style rules added
• Confirmation that writing_style.md and feedback-log.md were updated