7e26de1b6c
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>
CommiTea
AI project manager built on Gitea. A deterministic scheduler does the math
(Monte Carlo forecasts over your own estimate-vs-actual history); the agent —
Reginald — captures work via interview, negotiates priorities, and explains the
consequences. Gitea holds human-authored intent; a pm-state repo holds
machine-derived state. The LLM never does math.
Structure
apps/desktop— Electron app (electron-vite, React, Tailwind)packages/core— pure TypeScript: label schema, scheduler, gitea client (no Electron imports)docs/PLAN.md— product plan (goals, phases, not-doing list)docs/design/— design handoff: tokens, component contracts, interactive prototype (docs/design/ui_kits/app/index.htmlvia a static server), brand voice rules indesign_system_readme.md
Development
yarn # install
yarn dev # electron app, logs tee to apps/desktop/desktop.log
yarn test # unit tests (vitest)
yarn typecheck
End-to-end (Electron + Playwright)
yarn workspace @commitea/desktop e2e # build, then drive the built app
yarn workspace @commitea/desktop e2e:only # reuse existing out/ build (tight loop)
yarn workspace @commitea/desktop e2e:report # open the last HTML report
Tests launch the built app (out/main/index.js) through Playwright's
_electron API — no browser project, no chromium download. Fixtures and page
objects live in apps/desktop/e2e/; screenshots land in
e2e/.artifacts/screens/ for visual review. Page objects use user-facing
locators (getByRole/getByText), never CSS/DOM structure.
Conventions
- Yarn 4 workspaces; ESM everywhere;
.jsextensions on relative imports @commitea/corestays pure — unit-testable without Electron or network- Design tokens are the source of truth (
apps/desktop/src/renderer/src/design/, mirrored fromdocs/design/); Tailwind maps onto the CSS custom properties, never redefines them - Reginald's voice rules live in
docs/design/design_system_readme.md— no emoji, no point-date forecasts, wit in sentences never in buttons