Prioritize before you reply
LinkedIn Post Scoring
Know which LinkedIn conversations deserve attention first.
FiboAgent compares each post with your workspace context and returns a relevance score, impact tier, explanation, matched keywords, and recommended action.
A founder asks for alternatives to a failing outbound process
The sales workspace targets small B2B teams evaluating outbound workflows. The post names a current problem, describes its impact, and invites recommendations.
Why it matters
The author owns the problem and is actively comparing approaches. Review the thread for existing answers, then respond with a concrete diagnostic rather than a product pitch.
Scores are illustrative. Actual results depend on the configured workspace and post context.
How it works
From LinkedIn context to a reviewable decision
- 01
Create a scoring anchor
Choose the workspace use case and define target customers, business goals, relevant events, and exclusions.
- 02
Evaluate the post
The scoring workflow considers the post content alongside workspace criteria rather than applying one universal score to every team.
- 03
Read the reasoning
Review the 0–100 relevance score, S–C impact tier, matched keywords, why-it-matters explanation, and suggested action.
- 04
Filter the queue
Use saved signal views to focus on higher-impact posts, then open the original context before choosing an action.
Product capabilities
What this feature adds to the workflow
0–100 relevance score
A normalized relevance score helps compare posts inside the same workspace and scoring context.
S–C impact tier
Impact tiers give teams a faster way to filter the queue while the explanation preserves the reasoning behind the label.
Action-oriented output
Scoring includes why the post matters and a recommended next action, so the score supports a decision instead of becoming an isolated number.
Human review
A score is a prioritization aid, not an automatic qualification rule.
Model-assisted scoring makes a busy signal queue easier to review, but it can miss context, sarcasm, prior relationships, or details outside the captured post.
- ✓Compare posts only within a relevant workspace and scoring setup.
- ✓Read the explanation instead of relying on the score alone.
- ✓Revisit criteria when high scores repeatedly produce poor opportunities.
Limits and responsible use
What to verify before relying on the result
- Scores are contextual and should not be compared across unrelated workspaces without review.
- A high score does not confirm budget, authority, or readiness to purchase.
- Incomplete post context can reduce scoring quality.
- Teams should refine scoring criteria as they learn which signals lead to useful conversations.
Related resources
Learn, try, and apply the feature
Guides
Free tools
FAQ
Questions about LinkedIn Post Scoring
- How does LinkedIn post scoring work?
- FiboAgent sends the post context and workspace-specific criteria through its signal scoring workflow, then stores a relevance score, impact tier, explanation, matched keywords, and recommended action.
- What do S, A, B, and C mean?
- They are impact tiers used to organize the signal queue. Teams should interpret each tier together with the relevance score and written explanation.
- Can I define my own scoring criteria?
- Yes. Workspace configuration supports use-case-specific scoring context and scoring criteria so relevance can reflect your target customer and goal.
Continue the workflow
Related FiboAgent features
Start with your own context
Turn LinkedIn activity into a workflow your team can review.
Create a workspace, define what matters, and keep the signal, score, action, and reply history connected.