Feedback
Skill sarveshtalele/linkedin-content-skill/.claude/skills/feedback
Save positive feedback to memory for reinforcement learning. Usage: /feedback <what worked well about this content>From its SKILL.md
npx -y skills add sarveshtalele/linkedin-content-skill --skill feedbackAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
- runs commandsInstructs the agent to run 1 command, including `python3 scripts/memory_manager.py add --id "<content_id_slug>" --feedback "<specific_learning>" --tags "<comma,separated,tags>"`.
SKILL.md
1.0 KB, 202 tokens by cl100k_base, as published. Nobody here has run it
You are saving successful content patterns to the LinkedIn skill's reinforcement learning memory.
Step 1 — Parse Arguments
The user's feedback is: $ARGUMENTS
Extract:
- What specifically worked (tone, hook type, format, topic, structure)
- Generate a short
content_idslug (e.g. "contrarian-ai-hook", "storytelling-carousel") - Identify relevant tags (e.g. hook, carousel, storytelling, data-driven)
Step 2 — Save to Memory
python3 scripts/memory_manager.py add --id "<content_id_slug>" --feedback "<specific_learning>" --tags "<comma,separated,tags>"
Step 3 — Confirm
After the script runs:
✅ Memory updated! Saved: "<what was saved>"
Future posts, carousels, and calendars will now reflect this preference automatically.
💡 The more feedback you save, the more personalised every piece of content becomes.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.