Overview
Skills Insights helps managers better understand the strengths and capabilities demonstrated across their teams. The feature analyzes recognition, appreciation, and trust feedback shared through Cooleaf and maps demonstrated behaviors to the Korn Ferry competency framework.
Skills Insights transforms recognition activity into practical workforce insights that can support:
- Coaching and development conversations
- Identification of employee and team strengths
- Mentoring opportunities
- Talent planning
- Team-building decisions
- More intentional, behavior-based recognition
Explore the Managers Guide Here
Important: Skills Insights reflects patterns found in recognition data. The information should be used as an additional input for coaching and development, not as a standalone assessment of employee performance.
Skills Insights analyzes the content of employee recognition and identifies skill signals associated with demonstrated behaviors. Those signals are grouped into skills and broader competency clusters, then displayed in interactive reporting views.
Managers can move from an organization or team-level overview to:
- Skill clusters demonstrated across the team
- Individual skills associated with team members
- Changes in skill representation over time
- Recognition messages supporting an identified skill
- AI-generated summaries of team strengths and potential development opportunities
Example: Team Skill Representation
Caption: Skills Insights displays skill representation by employee, allowing managers to compare the mix of competency clusters demonstrated across the team.
Accessing Skills Insights
To access Skills Insights:
- Select your profile avatar from the Cooleaf home page.
- Select Manager Panel.
- Open Enhanced Reporting.
- Select See Report under the Manager Engagement Dashboard.
- Navigate to Skills AI.
- Select Go to page to open the Skills Insights reporting experience.
Availability may depend on your organization’s Cooleaf configuration and your assigned reporting access.
Explore the Skills Insights Dashboards
Skills Insights includes several connected reporting views. Each view provides a different way to understand the skills reflected in employee recognition.
Skills AI
The Skills AI view provides a high-level picture of the competency clusters demonstrated across a manager’s team.
Skill Representation by Skill Cluster
This visualization shows the proportion of recognition-associated skills represented within each competency cluster. Managers can use the view to understand which clusters appear most frequently and which appear less often in available recognition data.
The documented competency clusters include:
- Being Authentic
- Being Flexible and Adaptable
- Being Open
- Building Collaborative Relationships
- Creating the New and Different
- Focusing on Performance
- Influencing People
- Making Complex Decisions
- Managing Execution
- Optimizing Diverse Talent
- Taking Initiative
- Understanding the Business
Example: Skill Clusters Over Time
Caption: The Skill Representation by Skill Cluster visualization shows how the composition of demonstrated competency clusters changes by quarter.
How to Interpret This View
Each column represents the selected time period. The colored portions show the relative mix of skill clusters identified in recognition during that period.
Managers can use the visualization to explore questions such as:
- Which skill clusters appear consistently over time?
- Are new strengths becoming more visible?
- Are certain capabilities appearing less frequently in recognition?
- Does the recognition being shared reflect the behaviors the team is being encouraged to demonstrate?
A lower percentage does not necessarily mean that a team lacks a skill. It may mean that the skill has not appeared frequently in the recognition included within the selected filters.
Skills Decomposition Tree
The Skills Decomposition Tree allows managers to explore skills at different levels of detail. Available breakdowns include:
- Skill cluster
- Individual skill
- Immediate leader
- Person
- Quarter
- Year
This view can help managers move from a high-level competency cluster into the underlying skills, employees, leaders, or reporting periods associated with the data.
Skills Profile
The Skills Profile views help managers examine how skill clusters and individual skills are represented across their teams.
Overall Skill Representation by Cluster or Skill
This visualization ranks clusters or skills based on how frequently the selected item is represented in the available recognition data.
Example: Overall Skill Representation
Caption: The Overall Skill Representation view compares how frequently different skill clusters or skills appear in the selected recognition data.
In the example shown, Understanding the Business has the highest representation, followed by Building Collaborative Relationships, Influencing People, and Taking Initiative. These values belong to the sample screenshot and will differ based on the selected organization, team, employee population, date range, and available recognition data.
How to Use This View
Managers can use this visualization to:
- Identify the capabilities most often reinforced through recognition
- Look for areas of concentrated team strength
- Notice clusters that appear less frequently
- Inform follow-up questions during coaching or team discussions
- Consider whether recognition practices reflect the full range of employee contributions
Skill representation measures what is visible in the available recognition data. It should not be interpreted as a complete inventory of an employee’s capabilities.
Skill Representation Across the Team
This view shows the individual skills associated with each team member’s recognition. Each horizontal bar represents one employee, while the colored sections correspond to different skills.
Example: Individual Skills Across Employees
Caption: Skill Representation Across the Team helps managers compare which skills are being demonstrated and recognized for individual team members.
How to Interpret This View
The distribution of colors within an employee’s bar shows the mix of skills identified in recognition associated with that employee.
Managers can use the view to:
- Explore the range of skills recognized for each team member
- Identify employees with complementary demonstrated strengths
- Prepare examples for coaching conversations
- Consider mentoring or knowledge-sharing opportunities
- Recognize employees more intentionally for specific behaviors
Comparisons should be made carefully. Differences may be influenced by the volume, timing, and detail of recognition received, not only by differences in employee capabilities.
Skills Representation and AI-Generated Insights
The Skills Representation view combines employee-level skill data with an AI-generated narrative. The narrative can summarize commonly demonstrated competencies, highlight patterns in the available data, and suggest potential mentor and mentee pairings.
Example: Skill Matrix and AI-Generated Summary
Caption: The Skills Representation view combines filtering options, employee-level skill representation, an AI-generated team summary, and suggested mentoring opportunities.
The view shown includes:
- A date filter
- Leadership hierarchy selections
- Active and inactive employee status filtering
- A matrix of employees and competency clusters
- Percentage-based skill representation
- A written summary of commonly demonstrated skills
- Suggested mentor and mentee pairings
- A notice that the narrative was created with AI and that inaccuracies are possible
Using the AI-Generated Narrative Responsibly
AI-generated summaries are intended to help managers explore patterns in the available data. Managers should review the underlying information before using a recommendation in a coaching, development, or mentoring conversation.
Recommended practices include:
- Verify the narrative against the dashboard data.
- Review the recognition supporting the identified skills.
- Consider whether the selected date range and employee filters provide enough information.
- Treat suggested mentoring pairs as conversation starters rather than automatic assignments.
- Include employee interests, development goals, availability, and context before establishing a mentoring relationship.
- Avoid treating an AI-generated percentage or summary as a performance rating.
Skills References
The Skills References view shows the recognition messages from which skills were identified. This creates a connection between a displayed skill and the recognition supporting it.
Managers can use Skills References to:
- Understand why a skill was associated with an employee
- Review examples of demonstrated behavior
- Prepare evidence-based coaching conversations
- Provide specific follow-up recognition
- Validate patterns shown in the summary dashboards
Reviewing Skills References is especially helpful before discussing an identified strength, development opportunity, or suggested mentoring connection.
Filtering and Reviewing Skills Data
The available screenshots show filters for date, leadership hierarchy, person, and active or inactive status.
Before drawing conclusions from a visualization, confirm that the filters reflect the population and period you want to review.
Consider the following:
Date Range
Recognition patterns may change depending on the selected reporting period. A short date range may provide a limited sample, while a longer range may combine older and more recent behaviors.
Leadership Hierarchy
Use the hierarchy selection to focus the dashboard on the appropriate organization, leader, or employee population.
Employee Status
Confirm whether the view includes active employees, inactive employees, or another available status selection.
Recognition Volume
Employees with limited recognition may have fewer visible skill signals. A low or missing value should not automatically be interpreted as a lack of capability.
Best Practices for Managers
Use Skills Insights as a Conversation Starter
Use the dashboard to develop thoughtful questions rather than predetermined conclusions.
Examples include:
- “This skill appears frequently in your recognition. How do you see it showing up in your work?”
- “Which of these strengths would you like to use more often?”
- “Are there capabilities you use regularly that may not be visible in the recognition data?”
- “Would you be interested in sharing your experience in this area with another team member?”
Connect Insights to Specific Recognition
When discussing a skill, review the associated recognition so the conversation remains grounded in specific examples of demonstrated behavior.
Encourage Detailed Recognition
Meaningful, behavior-based recognition gives Skills Insights more context to analyze. Encourage employees and managers to describe:
- What the employee did
- How the employee approached the work
- Who or what benefited
- Why the contribution mattered
Look for Patterns, Not Isolated Values
Review multiple data points, time periods, and recognition examples before interpreting a skill as a sustained strength or an opportunity for development.
Revisit Insights Regularly
Periodic review can help managers notice changes in the skills being recognized and determine whether new capabilities are becoming visible over time.
Frequently Asked Questions
Where does Skills Insights data come from?
Recognition text is the primary documented source used to identify and surface skill signals. The resulting skills are stored and aggregated into Manager Insights dashboards.
What competency framework is used?
Skills identified through recognition are mapped to the Korn Ferry competency framework.
Can managers view skills for individual employees?
Yes. Skills Insights includes views that show the skills associated with individual team members.
Can managers see the recognition behind a skill?
Yes. Skills References shows the recognition associated with extracted skills.
What does a percentage represent?
Percentages displayed in Skills Insights reflect the representation of identified skills or clusters within the currently selected data and filters. The screenshots show percentages by person and skill cluster.
A percentage should not be treated as an employee performance score or a complete measure of proficiency.
Does a zero or low value mean an employee does not have that skill?
Not necessarily. Skills Insights is based on skills visible in the recognition data available for the selected filters. A skill may not have been mentioned or detected in that data.
Are the mentoring suggestions automatically assigned?
The dashboard displays suggested mentor and mentee pairings as part of its AI-generated narrative.
The documentation does not state that suggestions create or assign a formal mentoring relationship. Managers should review the recommendation and discuss interest and fit with the employees involved.
Should Skills Insights be used for performance evaluations?
Skills Insights is designed to provide additional evidence for coaching, employee development, talent planning, and team formation.
The dashboard should be considered alongside broader context and direct manager and employee conversations. Recognition-derived insights should not be used as the sole basis for a performance or employment decision.
Why might a skill be missing?
The skill may not appear in recognition included in the selected filters. Skills are identified by recognition, so employees may have skills that have yet to be formally recognized on the platform.
Are AI-generated summaries always accurate?
The displayed dashboard includes a notice stating, “Created with AI. Inaccuracies are possible.”
Managers should verify generated summaries against the dashboard data and supporting recognition before acting on them.
Need Help?
If you cannot access Skills Insights or believe the dashboard is not displaying the expected employee population, contact your organization’s Cooleaf administrator or submit a request to Cooleaf Support. When contacting Support, include:
- The dashboard or page being viewed
- The selected filters
- The expected employee population
- A description of the unexpected result
- A screenshot, if appropriate