> ## Documentation Index
> Fetch the complete documentation index at: https://docs.orbit.devotel.io/llms.txt
> Use this file to discover all available pages before exploring further.

# The Quality hub: leaderboard, scorecards, and evaluations from one page

> A map of the Quality section of the dashboard — the leaderboard, per-agent scorecards, the evaluations ledger, Practice Studio roleplay, and the recording library — what each page answers, how the leaderboard ranks agents, how to run an evaluation and read the scorecard, and the roleplay loop that coaches before go-live.

# The Quality hub: leaderboard, scorecards, and evaluations from one page

The **Quality** section of the dashboard groups every contact-center quality surface behind one hub. This page is the map: the sections below walk each surface end to end — what it answers, which metrics feed it, and how to run it — and point at the in-depth guide behind each page. The reading path is supervisor-first; agents open their own card on the same surfaces.

Use [The Quality hub: the supervisor loop](/guides/quality-hub-supervisor-loop) for the narrative walkthrough; this page is the orientation map — pick the right surface first, then open its in-depth guide.

## 1. What the Quality hub surfaces

The hub's own landing page (`/quality`) renders the org-wide scorecard rollup — conversations scored, pass rate, judge confidence, a by-channel and by-rubric breakdown, and a recent-failures triage list — plus an AI auto-QA coverage card and the QA score trend bars. Every row below is one hop off that hub:

| Surface               | Dashboard route           | What it answers                                                                                                                                                                                            | In-depth guide                                               |
| --------------------- | ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ |
| **Leaderboard**       | `/quality/leaderboard`    | *Where does each agent rank this window, and which KPI carried them?* Points composed from QA scores, handled calls, and CSAT, with badges and per-request custom weights.                                 | [Quality leaderboard](/guides/quality-leaderboard)           |
| **Agent scorecards**  | `/quality/agents/<agent>` | *What is one agent's full performance picture?* QA composite, schedule adherence, AHT, FCR, CSAT, compliance flags, the rubric breakdown, and drill-down to the source interactions.                       | [Agent scorecard](/guides/agent-scorecard)                   |
| **Evaluations**       | `/quality/evaluations`    | *Which conversations were scored, by whom, and where in the ack/appeal lifecycle?* Reviewer-graded, AI auto-scored, and agent self-evaluations on one ledger; calibration and workload tabs for reviewers. | [QA Evaluations](/guides/quality-evaluations)                |
| **Practice Studio**   | `/quality/practice`       | *How do I coach the failing pattern before it hits a real customer?* AI-simulated-customer roleplay with an automatic score and written coaching feedback; no real customer is ever contacted.             | [Practice Studio roleplay](/guides/practice-studio-roleplay) |
| **Recording Library** | `/quality/recordings`     | *Which recording do I grade (or re-grade) first?* Every recording joined to its transcript, its latest QA score, and its recording-health verdict, searchable by transcript text.                          | [Recording Library](/guides/quality-recordings)              |

Agents get a narrower path on the same surfaces: **Me → My scorecard** is the agent-facing half of the leaderboard (rank, points, badges, plus per-rubric pass rates, the daily trend, and top failure modes), and the **My evaluations** view on the evaluations page lists only their own rows, scoped on the server. See [Read your own QM scorecard](/guides/my-scorecard).

## 2. How the leaderboard ranks agents — and what feeds it

The board on `/quality/leaderboard` turns counters the voice and QA pipelines already write into a point total. The default ruleset weights quality over raw volume so call count alone does not win:

| Metric key                                                             | Points in the default ruleset                      |
| ---------------------------------------------------------------------- | -------------------------------------------------- |
| `callsHandled` — completed calls attributed to the answering agent     | 2 points per call (linear)                         |
| `avgQaScore` — official evaluation average                             | average scaled to 60 points                        |
| `avgCsat` — customer-satisfaction responses (1–5) on the agent's calls | average scaled to 30 points                        |
| `avgHandleSeconds` — talk seconds divided by call count                | 20 bonus points when the average is 300 s or under |

Badges add threshold awards — **Top Performer** (rank 1), **Podium Finish** (top 3), **QA Ace** (QA average ≥ 90 on at least 3 evaluations), **Centurion** (100+ handled calls), **Customer Champion** (CSAT ≥ 4.5 on at least 5 responses). Average-based badges carry a sample guard so a thin window cannot mint them; points pay out regardless of sample size. Compare like-for-like — same queue, same window, similar call counts — and weight the QA and CSAT columns over total points when coverage differs.

Rows queue only on **official** score rows — reviewer-graded or AI auto-scored once a reviewer has graded them; pending and self-scored rows stay out — so the board counts whatever the evaluations ledger corrected. Ties sort deterministically on QA average, then calls handled, then agent id.

### Leaderboard row shape

Over the API, one ranked entry carries `rank`, `agentId`, `agentName`, `totalPoints`, the per-rule `breakdown`, earned `badges`, and the raw `metrics` every rule or badge can reference:

```json theme={null}
{
  "rank": 1,
  "agentId": "user_2mN4zR7Ld",
  "agentName": "Dana Okafor",
  "totalPoints": 337,
  "breakdown": [
    { "ruleId": "calls-volume", "label": "Calls handled", "points": 236 },
    { "ruleId": "qa-quality", "label": "QA score", "points": 55 }
  ],
  "badges": [
    {
      "id": "top-performer",
      "name": "Top Performer",
      "description": "Ranked #1 on the leaderboard for this period.",
      "icon": "trophy"
    }
  ],
  "metrics": {
    "callsHandled": 118,
    "avgHandleSeconds": 264,
    "qaCount": 7,
    "avgQaScore": 92.14,
    "csatCount": 31,
    "avgCsat": 4.4
  }
}
```

Ranked from `POST /api/v1/quality/gamification/leaderboard` (or the **Leaderboard** page itself); the six `metrics` keys and the badge definitions are on `GET /api/v1/quality/gamification/config`. Visibility is role-scoped — owner and admin see the full roster, a supervisor sees only the queues they supervise, and an agent's self-view (`GET /gamification/me`) ranks them against the same default board so their rank always matches the supervisor view. Tuning `point_rules` in the request previews a custom scheme and flips `rules_source` to `custom` — a per-request preview, never the standing record.

## 3. Running an evaluation and reading the scorecard

An evaluation scores one conversation for one agent against one weighted scorecard form. The form is the rubric: weighted sections, each holding weighted criteria, each criterion with a `max_score` (default 100). Mark a section `auto_fail` to force the whole evaluation to zero when any criterion in it scores zero — the compliance-gate pattern. The reviewer submits a raw score per criterion and the server re-derives the weighted 0–100 `total_score`; a client can never set the total directly.

### Evaluation rubric fields

```json theme={null}
{
  "name": "Inbound Support QA",
  "sample_rate_pct": 10,
  "definition": {
    "sections": [
      {
        "id": "compliance",
        "label": "Compliance",
        "weight": 1,
        "auto_fail": true,
        "criteria": [
          { "id": "pii", "label": "Verified identity before disclosure", "weight": 1 }
        ]
      },
      {
        "id": "soft-skills",
        "label": "Soft skills",
        "weight": 2,
        "criteria": [
          { "id": "empathy", "label": "Showed empathy", "weight": 1 },
          { "id": "clarity", "label": "Clear next steps", "weight": 1 }
        ]
      }
    ]
  }
}
```

Every scored row moves through the ack/appeal lifecycle — `pending → acknowledged`, or `pending → appealed → resolved` when the evaluated agent appeals and a reviewer closes the appeal. Two system-created row types join the same ledger: **auto-sampled** rows queued un-scored by a form's `sample_rate_pct` and claimed by a reviewer, and **AI auto-scored** rows (`auto_scored: true`) whose below-threshold scores carry `flagged_for_review` until a reviewer corrects them.

Work the evaluation flow end to end in [QA Evaluations](/guides/quality-evaluations), and stand the whole program — rubric, blind calibration, ack/appeal, leaderboard — up in [Build a contact-center QA program](/guides/quality-management-program). The **Agent scorecards** page composes the same ledger per agent with adherence, AHT, FCR, and CSAT beside the rubric breakdown; read it in [Agent scorecard](/guides/agent-scorecard).

## 4. Practice Studio: coach the pattern before go-live

Practice Studio flips the normal QA loop: instead of scoring an agent after a real call, it hands the agent a safe sandbox to rehearse the hard conversation *before* one happens. A supervisor authors a scenario — a difficult close, a price-objection customer, an angry escalation — sets a passing score, and assigns it. An AI stays in character as the customer; the agent rehearses inside the dashboard until their best session clears the bar. When the session ends, an automatic score with written coaching feedback shows what landed and what to work on. No real customer is ever contacted — sessions are in-app simulations, so no calls, messages, or charges result.

The pairing that works: a rank drop or rubric drift names the coaching target from the leaderboard or scorecard, then a scenario assigned in Practice Studio gives the rehearsal. Practice sessions count toward practice metrics only — they never re-enter the evaluation ledger, so the drill cannot cancel a bad score on an already-graded conversation. Set up scenarios, assign them, and read session feedback in [Practice Studio roleplay](/guides/practice-studio-roleplay).

## 5. Wire the hub into your docs flow

The hub is a map, not a second pipeline — reach for the in-depth guide the moment a question goes past "which page":

* [Quality leaderboard](/guides/quality-leaderboard) — scoring rules, badge thresholds, role-scoped visibility, and reading the rank fairly.
* [Agent scorecard](/guides/agent-scorecard) — the per-agent composite with drill-down to source interactions.
* [Practice Studio roleplay](/guides/practice-studio-roleplay) — scenario authoring, assignment, and the automatic feedback loop.
* [The supervisor loop across the hub](/guides/quality-hub-supervisor-loop) — the end-to-end narrative from coverage counter to coaching.
* [Quality Management API](/api-reference/quality) — the endpoint reference behind every surface above.

Every surface in the hub is tenant-owned — form definitions, evaluation rows, practice scenarios, and recordings live in your workspace only, and reviewer surfaces (leaderboard, evaluations, recordings) render to owner, admin, and supervisor seats while agents get their own scoped views.

## See also

* [QA Evaluations](/guides/quality-evaluations) — forms, status lifecycle, and the system row types
* [Read your own QM scorecard](/guides/my-scorecard) — the agent-facing view of the same pipeline
* [Recording Library](/guides/quality-recordings) — search recordings, filter by score and QC verdict, and grade while you listen
* [Per-agent coaching cards and the compliance-flag queue](/guides/quality-coaching-cards) — the two rollups above the leaderboard
* [Build a contact-center QA program](/guides/quality-management-program) — the whole loop from rubric to leaderboard