feat: calibration from closed-issue actuals → forecast flips off cold-start (#1)

Close the D3 loop. The forecast now learns from the team's own estimate-vs-actual
history (the working time #5 infers from git events) instead of guessing forever.

core (@commitea/core/calibration-v0):
- fitCalibration(samples): lognormal fit on log(actual/estimate) — global +
  per-bucket (once a bucket clears the floor) + per-person bias. coldStart until
  n >= 20 closed-with-estimate issues.
- calibrationSamples(): pull those samples from the closed backlog via lifecycle
  inference (estimate label vs inferred actualWorkingDays).
- toDurationModel(): project the fit to the params forecast consumes.
- forecast() gains options.model: when past cold-start, fitted params drive the
  sim (per bucket, global fallback); otherwise the code priors do. Forecast.coldStart
  now reflects the model. nearestBucket extracted + exported.

app:
- AppShell fits calibration once from the reconciled backlog, feeds the model into
  forecastBacklog (cone), and drives the Calibration screen + Runway header.
- Focus cone footer, Runway note, and Calibration screen now say cold-start (N/20)
  vs calibrated (on N closed) from real data; Calibration scatter / bucket bias /
  per-person all fitted, degrading honestly on a thin dataset.

Known refinement: same-day closes yield 0 working-day actuals (day-granular) and
are excluded, so a fast-moving repo can sit at n=0 — honest, but a fractional
(hours-based) actual would let those count. Per-person uses gitea login, not
display name, until the person map lands.

Note: also re-lands #10 (Monte Carlo) and #5 (lifecycle) which merged into their
stacked base branches but never propagated to main (stacked-merge trap); this
branch is cut from main and carries all three so main is whole again.

Verified: 74 core tests green (9 calibration + 2 forecast-switch added), desktop
typecheck clean, 14 fixture e2e green, live spec asserts the real cold-start
calibration surface (Runway note + screen badge fitted from actuals).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

apps/desktop/e2e/live-backlog.spec.ts

@@ -34,6 +34,17 @@ test.describe('live backlog', () => {
     await expect(win.getByText(/Cold-start priors/)).toBeVisible()
     await win.screenshot({ path: join(here, '.artifacts', 'screens', 'live-focus.png'), fullPage: true, animations: 'disabled' })
 
+    // Runway → calibration surface, fitted from real closed-issue actuals (#1).
+    // With <20 estimated closes the repo is honestly cold-start; the note proves
+    // the fit ran on real data, not the fixture's "calibrated on 27".
+    await rail.getByRole('button', { name: 'Runway' }).click()
+    await expect(
+      win.getByText(/cold-start priors · \d+\/20 closed issues estimated|calibrated on \d+ closed/),
+    ).toBeVisible()
+    await win.getByRole('button', { name: 'Full report' }).click()
+    await expect(win.getByText(/cold-start · \d+\/20|curve active · n ≥ 20/)).toBeVisible()
+    await win.screenshot({ path: join(here, '.artifacts', 'screens', 'live-calibration.png'), fullPage: true, animations: 'disabled' })
+
     await app.close()
   })
 })

apps/desktop/src/renderer/src/components/screens/calibration-screen.tsx

@@ -1,11 +1,12 @@
 import React from 'react'
 
-import { CALIBRATION } from '../../data/fixtures.js'
+import { CALIBRATION, type CalibrationData } from '../../data/fixtures.js'
 import { Badge, Card, Icon } from '../ui/index.js'
 
-// Calibration report — estimate-vs-actual evidence behind the cones
+// Calibration report — estimate-vs-actual evidence behind the cones.
-export function CalibrationScreen({ onBack }: { onBack: () => void }) {
+// `data` (real fit from closed-issue actuals) overrides the demo fixture.
-  const c = CALIBRATION
+export function CalibrationScreen({ onBack, data }: { onBack: () => void; data?: CalibrationData }) {
+  const c = data ?? CALIBRATION
 
   // scatter chart geometry
   const W = 420,
@@ -68,7 +69,11 @@ export function CalibrationScreen({ onBack }: { onBack: () => void }) {
             <h1 style={{ font: 'var(--text-display)', color: 'var(--ink-1)', margin: 0 }}>Calibration</h1>
             <p style={{ font: 'var(--text-data)', color: 'var(--ink-3)', margin: '6px 0 0', whiteSpace: 'nowrap' }}>{c.n} closed issues with estimates · evidence, not opinion</p>
           </div>
+          {c.active ? (
             <Badge tone="ok" dot>curve active · n ≥ 20</Badge>
+          ) : (
+            <Badge tone="warn" dot>cold-start · {c.n}/20</Badge>
+          )}
         </header>
       </div>
 
@@ -170,7 +175,9 @@ export function CalibrationScreen({ onBack }: { onBack: () => void }) {
               <span style={{ font: '500 12.5px var(--font-mono)', color: 'var(--ink-1)', whiteSpace: 'nowrap' }}>{c.effect.banded}</span>
             </div>
             <p style={{ font: 'var(--text-agent)', color: 'var(--ink-2)', margin: '10px 0 0' }}>
-              You are not bad at estimating; you are optimistic in a very stable way. Stable, I can work with.
+              {c.active
+                ? 'You are not bad at estimating; you are optimistic in a very stable way. Stable, I can work with.'
+                : 'Not enough closed history yet — I’m forecasting from cold-start priors and widening the cone to stay honest. The curve takes over at 20.'}
             </p>
           </Card>
         </div>

apps/desktop/src/renderer/src/components/screens/focus-screen.tsx

@@ -109,7 +109,9 @@ export function FocusScreen({
         <BurnUpCone data={forecast?.cone} />
         <p style={{ font: 'var(--text-agent)', color: 'var(--ink-2)', margin: '10px 0 0' }}>
           {forecast
-            ? `${forecast.scope} open ${forecast.scope === 1 ? 'issue' : 'issues'} in scope. Cold-start priors — the cone tightens as the team closes work.`
+            ? forecast.coldStart
+              ? `${forecast.scope} open ${forecast.scope === 1 ? 'issue' : 'issues'} in scope. Cold-start priors — ${forecast.calibratedN}/20 estimated closes so far; the cone tightens as the team closes work.`
+              : `${forecast.scope} open ${forecast.scope === 1 ? 'issue' : 'issues'} in scope, calibrated on ${forecast.calibratedN} closed ${forecast.calibratedN === 1 ? 'issue' : 'issues'} of your own.`
             : 'The cone has narrowed since Friday. I’m quietly pleased.'}
         </p>
       </Card>

apps/desktop/src/renderer/src/components/screens/runway-screen.tsx

@@ -8,15 +8,22 @@ import { RUNWAY, CAPACITY } from '../../data/fixtures.js'
 export function RunwayScreen({
   onOpenCalibration,
   onOpenMilestone,
+  calibration,
 }: {
   onOpenCalibration: () => void
   onOpenMilestone: () => void
+  calibration?: { n: number; coldStart: boolean }
 }) {
+  const calibNote = calibration
+    ? calibration.coldStart
+      ? `cold-start priors · ${calibration.n}/20 closed issues estimated`
+      : `calibrated on ${calibration.n} closed ${calibration.n === 1 ? 'issue' : 'issues'}`
+    : 'calibrated on 27 closed issues'
   return (
     <div style={{ display: 'flex', flexDirection: 'column', gap: 16 }}>
       <header style={{ borderBottom: 'var(--rule-double)', paddingBottom: 14 }}>
         <h1 style={{ font: 'var(--text-display)', color: 'var(--ink-1)', margin: 0 }}>Runway</h1>
-        <p style={{ font: 'var(--text-data)', color: 'var(--ink-3)', margin: '6px 0 0' }}>capacity vs milestone dates · calibrated on 27 closed issues</p>
+        <p style={{ font: 'var(--text-data)', color: 'var(--ink-3)', margin: '6px 0 0' }}>capacity vs milestone dates · {calibNote}</p>
       </header>
 
       <Card overline="Milestones" flush>

apps/desktop/src/renderer/src/components/shell/app-shell.tsx

@@ -2,7 +2,7 @@ import React, { useEffect, useState } from 'react'
 
 import logoIcon from '../../design/assets/logo-icon.png'
 import type { IssueRef } from '../../data/fixtures.js'
-import { forecastBacklog, issuesToBoardColumns, scheduleFocus } from '../../lib/backlog.js'
+import { backlogCalibration, forecastBacklog, issuesToBoardColumns, scheduleFocus } from '../../lib/backlog.js'
 import { useBacklog } from '../../lib/use-backlog.js'
 import { PrimitivesGallery } from '../gallery.js'
 import { BoardScreen } from '../screens/board-screen.js'
@@ -90,8 +90,12 @@ export function AppShell() {
     backlog.status === 'ready' ? issuesToBoardColumns(backlog.issues, backlog.timelines) : undefined
   const focus =
     backlog.status === 'ready' ? scheduleFocus(backlog.issues, backlog.deps, backlog.timelines) : undefined
+  const calibration =
+    backlog.status === 'ready' ? backlogCalibration(backlog.issues, backlog.timelines) : undefined
   const forecast =
-    backlog.status === 'ready' ? (forecastBacklog(backlog.issues, backlog.deps) ?? undefined) : undefined
+    backlog.status === 'ready'
+      ? (forecastBacklog(backlog.issues, backlog.deps, new Date(), calibration?.model) ?? undefined)
+      : undefined
 
   useEffect(() => {
     document.documentElement.setAttribute('data-theme', dark ? 'dark' : 'light')
@@ -178,10 +182,11 @@ export function AppShell() {
           <RunwayScreen
             onOpenCalibration={() => setView('calibration')}
             onOpenMilestone={() => setView('milestone')}
+            calibration={calibration ? { n: calibration.model.n, coldStart: calibration.model.coldStart } : undefined}
           />
         )
       case 'calibration':
-        return <CalibrationScreen onBack={() => setView('runway')} />
+        return <CalibrationScreen onBack={() => setView('runway')} data={calibration?.data} />
       case 'milestone':
         return <MilestoneScreen onBack={() => setView('runway')} onOpenIssue={openIssue} />
       case 'inbox':

apps/desktop/src/renderer/src/lib/backlog.ts

@@ -1,17 +1,24 @@
 import {
+  type CalibrationModel,
+  type CalibrationSample,
+  calibrationSamples,
+  COLD_START_THRESHOLD,
   type DependencyEdge,
+  fitCalibration,
   forecast,
   type GiteaIssue,
   inferLifecycle,
   type LifecycleColumn,
   type LifecycleEvent,
   type LifecycleInference,
+  PRIOR_BUCKETS,
   schedule,
   type ScheduledItem,
   selectFocus,
+  toDurationModel,
 } from '@commitea/core'
 
-import { type BoardColumn, type BoardIssue, type FocusIssue } from '../data/fixtures.js'
+import { type BoardColumn, type BoardIssue, type CalibrationData, type FocusIssue } from '../data/fixtures.js'
 import { type BurnUpData, buildBurnUpData } from './dates.js'
 
 type Timelines = Record<number, LifecycleEvent[]>
@@ -100,22 +107,109 @@ export interface ForecastView {
   cone: BurnUpData
   p80Label: string
   rangeLabel: string
+  /** true while the forecast still runs on code priors (calibration not yet trusted). */
+  coldStart: boolean
+  /** Closed-with-estimate issues feeding calibration so far. */
+  calibratedN: number
 }
 
 /**
  * Monte Carlo forecast over the open backlog, mapped onto a calendar-anchored
- * burn-up cone. Returns null when there's nothing to forecast (no open scope) —
+ * burn-up cone. When a calibration model is supplied and past cold-start, its
- * the UI then falls back to the demo cone. `today` is injectable for tests.
+ * fitted params drive the sim. Returns null when there's nothing to forecast.
+ * `today` is injectable for tests.
  */
 export function forecastBacklog(
   issues: GiteaIssue[],
   deps: DependencyEdge[],
   today: Date = new Date(),
+  calibration?: CalibrationModel,
 ): ForecastView | null {
-  const f = forecast(toSchedulable(issues), deps)
+  const model = calibration ? toDurationModel(calibration) : undefined
+  const f = forecast(toSchedulable(issues), deps, model ? { model } : {})
   const cone = buildBurnUpData(f, today)
   if (!cone) return null
-  return { scope: f.scope, cone, p80Label: cone.p80Label, rangeLabel: cone.rangeLabel }
+  return {
+    scope: f.scope,
+    cone,
+    p80Label: cone.p80Label,
+    rangeLabel: cone.rangeLabel,
+    coldStart: f.coldStart,
+    calibratedN: calibration?.n ?? 0,
+  }
+}
+
+/** Fit the calibration model from the closed backlog's inferred actuals (#1). */
+export function calibrateBacklog(
+  issues: GiteaIssue[],
+  timelines: Timelines = {},
+  asOf: Date = new Date(),
+): CalibrationModel {
+  return fitCalibration(calibrationSamples(issues, timelines, asOf))
+}
+
+/** Calibration model + its screen view in one pass over the closed backlog. */
+export function backlogCalibration(
+  issues: GiteaIssue[],
+  timelines: Timelines = {},
+  asOf: Date = new Date(),
+): { model: CalibrationModel; data: CalibrationData } {
+  const samples = calibrationSamples(issues, timelines, asOf)
+  const model = fitCalibration(samples)
+  return { model, data: calibrationData(model, samples, issues) }
+}
+
+const pctFromMu = (mu: number) => Math.round((Math.exp(mu) - 1) * 100)
+
+/**
+ * Shape the calibration model + its samples into the screen's view. Buckets and
+ * people only earn a bias once their sample clears the fit floor; everything
+ * degrades honestly on a thin (cold-start) dataset.
+ */
+export function calibrationData(
+  model: CalibrationModel,
+  samples: CalibrationSample[],
+  openIssues: GiteaIssue[],
+): CalibrationData {
+  const labels = PRIOR_BUCKETS.map((b) => {
+    const inBucket = samples.filter((s) => s.bucket === b)
+    const fit = model.byBucket[b]
+    const mu = fit ? fit.mu : model.global.mu
+    return {
+      label: `est/${b}d`,
+      n: fit ? fit.n : inBucket.length,
+      median: inBucket.length ? `${(b * Math.exp(mu)).toFixed(1)}d` : '—',
+      bias: fit ? pctFromMu(fit.mu) : null,
+    }
+  })
+
+  const people = Object.entries(model.byPerson).map(([who, pb]) => ({
+    who,
+    n: pb.n,
+    bias: pctFromMu(model.global.mu + pb.biasMu),
+    note: '',
+  }))
+
+  const openEst = openIssues
+    .filter((i) => i.state === 'open')
+    .reduce((sum, i) => sum + (i.facts.estimateDays ?? 2), 0)
+  const effect = model.coldStart
+    ? { raw: `${model.n}/${COLD_START_THRESHOLD} estimated closes`, banded: 'cold-start priors', p50: '—' }
+    : {
+        raw: `${openEst}d estimated`,
+        banded: `×${Math.exp(model.global.mu).toFixed(2)} median drift`,
+        p50: `≈${Math.round(openEst * Math.exp(model.global.mu))}d`,
+      }
+
+  return {
+    n: model.n,
+    active: !model.coldStart,
+    labels,
+    people,
+    scatter: samples.map((s) => [s.estimateDays, s.actualWorkingDays]),
+    fit: Number(Math.exp(model.global.mu).toFixed(2)),
+    effect,
+  }
 }
 
 /**

packages/core/src/calibration/calibration-v0.test.ts

@@ -0,0 +1,121 @@
+import { describe, expect, it } from 'vitest'
+
+import { extractLabelFacts } from '../labels/label-schema.js'
+import type { LifecycleEvent } from '../lifecycle/lifecycle-v0.js'
+import type { GiteaIssue } from '../gitea/types.js'
+import {
+  CALIBRATION_BUCKET_FLOOR,
+  calibrationSamples,
+  type CalibrationSample,
+  COLD_START_THRESHOLD,
+  fitCalibration,
+  toDurationModel,
+} from './calibration-v0.js'
+
+function sample(over: Partial<CalibrationSample> = {}): CalibrationSample {
+  return { issue: 1, estimateDays: 2, actualWorkingDays: 2, bucket: 2, person: null, ...over }
+}
+
+describe('fitCalibration', () => {
+  it('is cold-start below the threshold and reports the honest n', () => {
+    const m = fitCalibration([sample(), sample({ actualWorkingDays: 4 })])
+    expect(m.n).toBe(2)
+    expect(m.coldStart).toBe(true)
+  })
+
+  it('flips off cold-start at the threshold', () => {
+    const many = Array.from({ length: COLD_START_THRESHOLD }, (_, i) =>
+      sample({ issue: i, estimateDays: 2, actualWorkingDays: 3, bucket: 2 }),
+    )
+    const m = fitCalibration(many)
+    expect(m.n).toBe(COLD_START_THRESHOLD)
+    expect(m.coldStart).toBe(false)
+  })
+
+  it('recovers the global median ratio (mu = mean log-ratio)', () => {
+    // every actual is exactly 2x its estimate → mu = ln 2
+    const m = fitCalibration(Array.from({ length: 25 }, (_, i) => sample({ issue: i, estimateDays: 2, actualWorkingDays: 4 })))
+    expect(m.global.mu).toBeCloseTo(Math.log(2), 6)
+  })
+
+  it('fits a bucket only once it clears the floor', () => {
+    const twos = Array.from({ length: CALIBRATION_BUCKET_FLOOR }, (_, i) =>
+      sample({ issue: i, estimateDays: 2, actualWorkingDays: 3, bucket: 2 }),
+    )
+    const oneThin = [sample({ issue: 99, estimateDays: 5, actualWorkingDays: 9, bucket: 5 })]
+    const m = fitCalibration([...twos, ...oneThin])
+    expect(m.byBucket[2]?.n).toBe(CALIBRATION_BUCKET_FLOOR)
+    expect(m.byBucket[5]).toBeUndefined() // only 1 sample, below floor
+  })
+
+  it('drops non-positive estimates/actuals', () => {
+    const m = fitCalibration([sample({ actualWorkingDays: 0 }), sample({ estimateDays: 0 }), sample()])
+    expect(m.n).toBe(1)
+  })
+
+  it('derives a per-person bias relative to global', () => {
+    // one person consistently runs longer than the mean
+    const base = Array.from({ length: 20 }, (_, i) => sample({ issue: i, actualWorkingDays: 2, person: 'ak' }))
+    const slow = Array.from({ length: 3 }, (_, i) => sample({ issue: 100 + i, actualWorkingDays: 6, person: 'sm' }))
+    const m = fitCalibration([...base, ...slow])
+    expect(m.byPerson['sm'].biasMu).toBeGreaterThan(0)
+    expect(m.byPerson['ak'].biasMu).toBeLessThan(0)
+  })
+})
+
+describe('toDurationModel', () => {
+  it('projects the fit down to the params forecast needs', () => {
+    const m = fitCalibration(
+      Array.from({ length: 25 }, (_, i) => sample({ issue: i, estimateDays: 2, actualWorkingDays: 3, bucket: 2 })),
+    )
+    const dm = toDurationModel(m)
+    expect(dm.coldStart).toBe(false)
+    expect(dm.byBucket[2].mu).toBeCloseTo(m.byBucket[2].mu, 6)
+    expect(dm.global.mu).toBeCloseTo(m.global.mu, 6)
+  })
+})
+
+describe('calibrationSamples', () => {
+  const asOf = new Date('2026-02-01T00:00:00Z')
+
+  function issue(over: Partial<GiteaIssue>): GiteaIssue {
+    const labels = over.labels ?? []
+    return {
+      number: 1,
+      title: '#1',
+      body: '',
+      state: 'closed',
+      labels,
+      facts: extractLabelFacts(labels),
+      milestone: null,
+      assignee: null,
+      assignees: [],
+      createdAt: '2026-01-05T09:00:00Z',
+      updatedAt: '2026-01-12T09:00:00Z',
+      closedAt: '2026-01-12T09:00:00Z',
+      url: '',
+      ...over,
+    }
+  }
+
+  const events = (i: number): Record<number, LifecycleEvent[]> => ({
+    [i]: [{ type: 'commit', at: '2026-01-07T09:00:00Z' }, { type: 'close', at: '2026-01-12T09:00:00Z' }],
+  })
+
+  it('samples closed, estimated issues with a resolvable actual', () => {
+    const i = issue({ number: 7, labels: ['est/2d'], assignee: 'sm', closedAt: '2026-01-12T09:00:00Z' })
+    const [s] = calibrationSamples([i], events(7), asOf)
+    expect(s.issue).toBe(7)
+    expect(s.estimateDays).toBe(2)
+    expect(s.bucket).toBe(2)
+    expect(s.person).toBe('sm')
+    // Wed 2026-01-07 → Mon 2026-01-12 = Wed,Thu,Fri = 3 working days
+    expect(s.actualWorkingDays).toBe(3)
+  })
+
+  it('skips open issues and closed ones without an estimate', () => {
+    const open = issue({ number: 8, state: 'open', labels: ['est/2d'], closedAt: null })
+    const noEst = issue({ number: 9, labels: [] })
+    expect(calibrationSamples([open, noEst], { ...events(8), ...events(9) }, asOf)).toEqual([])
+  })
+})

packages/core/src/calibration/calibration-v0.ts

@@ -0,0 +1,127 @@
+/**
+ * Calibration, v0 — fit the team's own estimate-vs-actual history so the
+ * forecast stops guessing (D3). The "actual" is the working time lifecycle
+ * inference derives from git events (#5), never manual tracking. Fit a
+ * lognormal on log(actual / estimate) globally and per estimate bucket; until
+ * the sample clears the cold-start threshold, the forecast keeps using the
+ * code-resident priors and this model just reports progress toward it.
+ */
+
+import { type DurationModel, type LognormalPrior, nearestBucket } from '../forecast/forecast-v0.js'
+import { inferLifecycle, type LifecycleEvent } from '../lifecycle/lifecycle-v0.js'
+import type { GiteaIssue } from '../gitea/types.js'
+
+/** Global sample size at which the fit takes over from the cold-start priors. */
+export const COLD_START_THRESHOLD = 20
+/** Minimum per-bucket sample before that bucket earns its own fit. */
+export const CALIBRATION_BUCKET_FLOOR = 3
+/** Fallback spread when a group is too small to estimate one. */
+const DEFAULT_SIGMA = 0.4
+
+/** One closed issue's estimate vs its inferred actual. */
+export interface CalibrationSample {
+  issue: number
+  estimateDays: number
+  actualWorkingDays: number
+  bucket: number
+  person: string | null
+}
+
+export interface BucketFit extends LognormalPrior {
+  n: number
+}
+
+export interface PersonBias {
+  /** Additive to global mu (log space). */
+  biasMu: number
+  n: number
+}
+
+export interface CalibrationModel {
+  /** Closed issues with an estimate + a resolvable actual. */
+  n: number
+  /** true while n < COLD_START_THRESHOLD — forecast keeps the code priors. */
+  coldStart: boolean
+  global: LognormalPrior
+  byBucket: Record<number, BucketFit>
+  byPerson: Record<string, PersonBias>
+}
+
+function mean(xs: number[]): number {
+  return xs.reduce((a, b) => a + b, 0) / xs.length
+}
+
+/** Sample standard deviation; falls back to DEFAULT_SIGMA below 2 points. */
+function stddev(xs: number[], mu: number): number {
+  if (xs.length < 2) return DEFAULT_SIGMA
+  const variance = xs.reduce((a, x) => a + (x - mu) ** 2, 0) / (xs.length - 1)
+  return Math.sqrt(variance) || DEFAULT_SIGMA
+}
+
+/** Fit a calibration model from estimate-vs-actual samples. Pure. */
+export function fitCalibration(samples: CalibrationSample[]): CalibrationModel {
+  const usable = samples.filter((s) => s.estimateDays > 0 && s.actualWorkingDays > 0)
+  const n = usable.length
+  const coldStart = n < COLD_START_THRESHOLD
+
+  const logRatios = usable.map((s) => Math.log(s.actualWorkingDays / s.estimateDays))
+  const globalMu = n ? mean(logRatios) : 0
+  const global: LognormalPrior = { mu: globalMu, sigma: n ? stddev(logRatios, globalMu) : DEFAULT_SIGMA }
+
+  const byBucket: Record<number, BucketFit> = {}
+  const byPerson: Record<string, PersonBias> = {}
+  const groups = new Map<number, number[]>()
+  const people = new Map<string, number[]>()
+  for (const s of usable) {
+    const lr = Math.log(s.actualWorkingDays / s.estimateDays)
+    ;(groups.get(s.bucket) ?? groups.set(s.bucket, []).get(s.bucket)!).push(lr)
+    if (s.person) (people.get(s.person) ?? people.set(s.person, []).get(s.person)!).push(lr)
+  }
+  for (const [bucket, lrs] of groups) {
+    if (lrs.length < CALIBRATION_BUCKET_FLOOR) continue
+    const mu = mean(lrs)
+    byBucket[bucket] = { mu, sigma: stddev(lrs, mu), n: lrs.length }
+  }
+  for (const [person, lrs] of people) {
+    if (lrs.length < CALIBRATION_BUCKET_FLOOR) continue
+    byPerson[person] = { biasMu: mean(lrs) - globalMu, n: lrs.length }
+  }
+
+  return { n, coldStart, global, byBucket, byPerson }
+}
+
+/** The subset of a model `forecast` consumes. */
+export function toDurationModel(model: CalibrationModel): DurationModel {
+  const byBucket: Record<number, LognormalPrior> = {}
+  for (const [bucket, fit] of Object.entries(model.byBucket)) {
+    byBucket[Number(bucket)] = { mu: fit.mu, sigma: fit.sigma }
+  }
+  return { coldStart: model.coldStart, global: model.global, byBucket }
+}
+
+/**
+ * Extract calibration samples from the closed backlog: each closed issue that
+ * carries an estimate and yields an inferred actual working duration.
+ */
+export function calibrationSamples(
+  issues: GiteaIssue[],
+  timelines: Record<number, LifecycleEvent[]>,
+  asOf: Date,
+): CalibrationSample[] {
+  const out: CalibrationSample[] = []
+  for (const issue of issues) {
+    if (issue.state !== 'closed') continue
+    const estimateDays = issue.facts.estimateDays
+    if (estimateDays == null) continue
+    const inf = inferLifecycle(issue, timelines[issue.number] ?? [], asOf)
+    if (inf.actualWorkingDays == null || inf.actualWorkingDays <= 0) continue
+    out.push({
+      issue: issue.number,
+      estimateDays,
+      actualWorkingDays: inf.actualWorkingDays,
+      bucket: nearestBucket(estimateDays),
+      person: issue.assignee,
+    })
+  }
+  return out
+}

packages/core/src/forecast/forecast-v0.test.ts

@@ -97,4 +97,27 @@ describe('forecast', () => {
     expect(f.scope).toBe(2)
     expect(f.p50Day).toBeGreaterThan(0)
   })
+
+  it('a cold-start model changes nothing — the code priors still drive it', () => {
+    const priors = forecast(scope, [], { trials: 1000, seed: 7 })
+    const cold = forecast(scope, [], {
+      trials: 1000,
+      seed: 7,
+      model: { coldStart: true, global: { mu: 5, sigma: 0.1 }, byBucket: {} },
+    })
+    expect(cold.coldStart).toBe(true)
+    expect(cold.p50Day).toBeCloseTo(priors.p50Day, 6)
+  })
+
+  it('a fitted model drives the sim once past cold-start', () => {
+    // an optimistic fit (mu < 0, tight sigma) should land the scope sooner than the pessimistic priors
+    const priors = forecast(scope, [], { trials: 2000, seed: 7 })
+    const fitted = forecast(scope, [], {
+      trials: 2000,
+      seed: 7,
+      model: { coldStart: false, global: { mu: -0.2, sigma: 0.1 }, byBucket: {} },
+    })
+    expect(fitted.coldStart).toBe(false)
+    expect(fitted.p50Day).toBeLessThan(priors.p50Day)
+  })
 })

packages/core/src/forecast/forecast-v0.ts

@@ -41,15 +41,29 @@ export const COLD_START_PRIORS: Record<number, LognormalPrior> = {
   8: { mu: 0.16, sigma: 0.36 },
 }
 
-const PRIOR_BUCKETS = [1, 2, 3, 5, 8]
+export const PRIOR_BUCKETS = [1, 2, 3, 5, 8]
 
 /** Nearest estimate bucket (ties resolve to the smaller bucket). */
-export function priorForEstimate(days: number): LognormalPrior {
+export function nearestBucket(days: number): number {
   let best = PRIOR_BUCKETS[0]
   for (const b of PRIOR_BUCKETS) {
     if (Math.abs(b - days) < Math.abs(best - days)) best = b
   }
-  return COLD_START_PRIORS[best]
+  return best
+}
+
+/** The cold-start prior for the bucket nearest to `days`. */
+export function priorForEstimate(days: number): LognormalPrior {
+  return COLD_START_PRIORS[nearestBucket(days)]
+}
+
+/** The lognormal parameters `forecast` needs, per estimate bucket. */
+export interface DurationModel {
+  coldStart: boolean
+  /** Fallback params (used when a bucket lacks its own fit). */
+  global: LognormalPrior
+  /** Per-bucket fitted params; missing buckets fall back to `global`. */
+  byBucket: Record<number, LognormalPrior>
 }
 
 export interface ForecastOptions {
@@ -57,6 +71,19 @@ export interface ForecastOptions {
   trials?: number
   /** PRNG seed. Fixed by default so a forecast is reproducible. */
   seed?: number
+  /**
+   * Fitted duration model. When present and not cold-start, its params drive
+   * the sim; otherwise the code-resident cold-start priors do.
+   */
+  model?: DurationModel
+}
+
+/** Resolve the lognormal params for an estimate, preferring a fitted model. */
+export function durationParams(days: number, model?: DurationModel): LognormalPrior {
+  if (model && !model.coldStart) {
+    return model.byBucket[nearestBucket(days)] ?? model.global
+  }
+  return priorForEstimate(days)
 }
 
 export interface BurnUpPoint {
@@ -121,13 +148,14 @@ export function forecast(
 ): Forecast {
   const trials = options.trials ?? DEFAULT_TRIALS
   const seed = options.seed ?? DEFAULT_SEED
+  const coldStart = options.model ? options.model.coldStart : true
   const order = schedule(issues, edges).items // empty when a dependency cycle exists
   const n = order.length
   if (n === 0) {
-    return { scope: 0, trials, coldStart: true, p50Day: 0, p80Day: 0, p95Day: 0, curve: [] }
+    return { scope: 0, trials, coldStart, p50Day: 0, p80Day: 0, p95Day: 0, curve: [] }
   }
 
-  const priors = order.map((it) => priorForEstimate(it.durationDays))
+  const priors = order.map((it) => durationParams(it.durationDays, options.model))
   const rng = mulberry32(seed)
 
   // endByRank[k][t] = working day the (k+1)-th scheduled issue completes on trial t.
@@ -156,7 +184,7 @@ export function forecast(
   return {
     scope: n,
     trials,
-    coldStart: true,
+    coldStart,
     p50Day: percentile(total, 0.5),
     p80Day: percentile(total, 0.8),
     p95Day: percentile(total, 0.95),

packages/core/src/index.ts

@@ -45,5 +45,32 @@ export type {
   SchedulePlan,
 } from './scheduler/scheduler-v0.js'
 
-export { COLD_START_PRIORS, forecast, priorForEstimate } from './forecast/forecast-v0.js'
+export {
-export type { BurnUpPoint, Forecast, ForecastOptions, LognormalPrior } from './forecast/forecast-v0.js'
+  COLD_START_PRIORS,
+  durationParams,
+  forecast,
+  nearestBucket,
+  PRIOR_BUCKETS,
+  priorForEstimate,
+} from './forecast/forecast-v0.js'
+export type {
+  BurnUpPoint,
+  DurationModel,
+  Forecast,
+  ForecastOptions,
+  LognormalPrior,
+} from './forecast/forecast-v0.js'
+
+export {
+  CALIBRATION_BUCKET_FLOOR,
+  calibrationSamples,
+  COLD_START_THRESHOLD,
+  fitCalibration,
+  toDurationModel,
+} from './calibration/calibration-v0.js'
+export type {
+  BucketFit,
+  CalibrationModel,
+  CalibrationSample,
+  PersonBias,
+} from './calibration/calibration-v0.js'