Teamcentric Skills
AvailableKnow the skills you have, before you need them.
A live, shared picture of your engineering skills, connected to the work: project readiness, role fit, team coverage, and the skills only one person holds.
Teamcentric Skills is available. There is no self-serve signup: a workspace and its accounts are set up for your organisation.
Where it sits
Capability before commitment
Skills gives engineering leaders evidence for planning who can take on the work.
The problem
Staffing decisions still run on memory
Projects are staffed, hiring is planned and risk is judged on what managers happen to remember about their people. Skills live in spreadsheets that go stale, in CVs written for a different job, and in the heads of a few leads.
HR systems track job titles, not skills. Survey tools collect a snapshot once and never connect it to projects, roles or teams.
Questions it answers from records
- Do we have the people to start this project?
- Which skills does only one person hold?
- Which roles and projects fall short, and by how much?
- Who should we train, who should mentor, and where do we need to hire?
- Which ratings have gone stale, and how far can we still trust them?
How it works
A hosted workspace, read live
Teamcentric runs the service; your people need only a browser. Email carries the invitations. Git statistics and AI-assistant access are optional connections, and both are off until you add them.
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Set up the workspace
An admin defines the skills catalogue, the roles and their seniority levels, the teams and the projects. A guided set-up proposes a starting point to review and edit; nothing is created until it is accepted.
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Invite your people
Members are invited by email, one at a time or in bulk, and rate their own skills. Leads and managers review those ratings for the people who report to them.
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Read the answers live
Readiness, role fit, coverage and risk are calculated every time a report is opened, from the current data and scoped to what the reader may see.
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Connect what helps
Optionally, add GitHub or GitLab activity statistics for context, and let people ask questions of the data through an AI assistant.
The model
One connected model, from records to decisions
Readiness, role fit and coverage are calculated live from records you control, and every figure decomposes back into them.
The dependency order matters: a readiness score means nothing without project requirements, requirements mean nothing without a catalogue, and an assessment means nothing without a model to assess against. The application walks an organisation through that order rather than presenting an empty dashboard.
From capability data to delivery decisions
One connected, explainable model
Define
Your capability model
Members & teams
Reviewed levels · allocation · recency
Roles
Seniority variants · weighted needs
Projects
Skill demand · staffing · complexity
Connect
Skills intelligence
capability × requirement × capacity
- Reviewed level
- The level analytics use: the self-rating until a reviewer changes it
- Requirement weight
- How much a role or project needs that skill
- Recency decay
- Older assessments count for less, on a published curve
- Allocation
- Committed capacity, spent once across every role
Reviewed evidence feeds every score. Activity statistics stay contextual and never infer a skill level.
Decide
Analytics & action
Project readiness
Role fit
Critical gaps
Kubernetes
2 members below requirement
Skill resilience
Threat modelling
Single-owner risk
The capability model, the factors that combine it, and the analytics that come out. Names and figures are illustrative.
People and their skills
Self-assessed, reviewed, and kept current
Each person rates their own skills, and their leads and managers review them. The self level and the reviewed level sit side by side; the reviewed level drives the reports. The effective level falls as a rating ages without verification, and every change is kept in a rating history.
The catalogue is shared: categories of technical and soft skills, managed by admins. Anyone can propose a new skill; leads and managers approve it, merge it into an existing one, ask for changes or reject it.
Projects and teams
See what a project lacks before it starts
A project states the skills it needs, with a required level and a weight, and the roles it needs and how many of each. People count towards it directly or through their teams. The readiness index shows how well they cover the requirements, the gap view names the strongest available person for each skill, and complexity checks flag thin cover on the skills that weigh most.
Analytics
Twelve reports, calculated when you open them
Each report explains how it is calculated, and most export to CSV. Every gap comes with a suggested action: train, mentor or hire.
- Project Readiness
- Readiness index, cover and top gaps for every project.
- Project Gaps
- The skill gaps behind each project's readiness.
- Project Role Coverage
- Whether each project has the roles and headcount it asks for.
- Role Fit
- How well each person fits a selected role and seniority.
- Role Gaps
- The skills where people fall short of their role.
- Team Coverage
- How well each team covers the skills it needs.
- Member Skill Matrix
- Everyone's effective level in every skill, in one table.
- Critical Gaps
- The skills that matter most and are weakest, with a suggested action and a place to record the decision.
- Stale Skills
- Ratings not verified for over one or two years, by person.
- Skill Resilience
- Skills held by one person or too few, and the active projects exposed.
- Where-Used
- Which roles and projects depend on a given skill.
- Data Health
- Gaps in the data itself, such as people without roles or roles without requirements.
Risk
Find your single points of failure
Skill Resilience lists the skills held by one person or too few, and the active projects that depend on them, before a resignation or a holiday turns thin cover into an outage or a missed audit.
Optional connections
Off until you turn them on
AI assistants · early access
People can connect an assistant that supports the Model Context Protocol (MCP) and ask questions such as “who on my teams knows Kubernetes well?” The assistant sees only what that person may see, and a change it suggests stays a proposal until the person confirms it. Tested with Claude Code.
- Off for every organisation until its admin turns it on.
- A personal token per person, limited to the actions they choose and optionally to some teams.
- Tokens expire within 90 days and can be revoked at any time; every call is audited.
Git activity statistics
Connect GitHub or GitLab to see commit and pull-request activity on a person's profile, a project's repositories and a team's work. It is context beside the skills record, not evidence for it: activity never sets a skill level or a readiness score, and Skills never reads your code.
Privacy and access
Capability data describes people. It is treated that way.
Who can see what is a product decision here, not a configuration afterthought.
- Visibility follows the reporting line
- Managers and leads see the people in their own reporting line, and the teams and projects connected to it; admins see the whole organisation. Enforced on the server, not hidden in the interface.
- Reviews stay with the reviewers
- A reviewer's adjustment drives the reports but is not shown back to the member, so a lead can record an honest judgement.
- Changes are audited
- Material changes and sensitive exports write audit events, and every change to a skill level is kept in its rating history.
- Your own workspace
- Each organisation has its own workspace, kept separate from every other. Seniority labels, locale and currency are set per workspace.
Questions
What people ask before they start
Skills is a hosted service: nothing to install, any modern browser, and a workspace of your own.
- Who can see an engineer's skills?
- The engineer, the people above them in their reporting line, and the workspace admins. Managers and leads see only their own reporting line. Reviewer adjustments are not shown to the engineer.
- How much effort does it take?
- Engineers rate their skills once and update them when something changes; leads review in a queue. The reports need no upkeep: they are calculated live from the current data.
- Does it read our code?
- No. The optional Git add-on reads activity statistics, such as commits and pull requests, from the repositories you link. It never changes anyone's skill levels.
- Can an AI assistant see everything?
- No. Assistant access is off until an admin turns it on. Each person's token sees only what that person may see, can be limited further to some actions and teams, expires within 90 days and can be revoked at any time. Every call is audited.
- What happens when a rating is out of date?
- Effective levels fade as ratings age without verification, and the Stale Skills report and the verification queue show what needs a fresh look.
- Can we get our data out?
- Yes. Most reports and lists export to CSV.
What it does not do
Being precise about the edges, so you can rule the product in or out quickly.
Deliberately not the job
- Inferring skill levels from commits or code
- Reading your source code
- Hiring pipeline and applicant tracking
- Forecasting and scenario simulation
Coming soon, with no dates committed
- Single sign-on and two-factor sign-in
- Scheduled reports and alerts
- AI-assistant sign-in with OAuth, for assistants such as ChatGPT
- Learning recommendations
See your own teams' skills, gaps and risks
Book a demo for a guided walkthrough of Skills with your own projects, roles and teams in view. Each organisation gets its own workspace, set up with you rather than through a signup form.