5ae191be49d86c220c0199450d498502531d3084
The last big agent capability. In the Capture screen, a rough braindump runs real
big-model decomposition into a small, estimated issue set; you review/edit the
labels and approve, and the issues are opened in gitea. This is the one place the
big model earns its keep (docs/agent-tools.md).
core (@commitea/core):
- capture-work: PROPOSE_ISSUES_TOOL + CAPTURE_SYSTEM; captureWork(complete, dump)
forces a single structured decomposition and returns validated issues; parseCaptureArgs
drops blank titles + invalid est/p labels. ProposedIssue / CaptureProposal.
- gitea client: createIssue({title, body?, labelIds?}) → POST /issues, normalized.
app:
- model bridge model:capture runs captureWork on the (loaded) big model.
- gitea bridge gitea:createIssues opens each approved issue with its est/* + p/*
labels (reusing the #41 label-id resolver — zero-pollution, no invented labels).
- Capture screen: when a model is configured, "Brew tickets" runs real capture and
"Approve all" files the set; otherwise the scripted demo interview runs. Fixed a
race — the brew handler re-checks model status at click time so a configured
model never falls into the scripted path before status resolves.
Verified: 108 core tests green (7 capture + createIssue added), desktop typecheck
clean, 14 fixture e2e green. Gated live e2e against gemma-4-26b: the auth braindump
→ 3 real tickets ("Resolve token refresh + session staleness" est/3d p/1, "Fix
webhook double-firing" est/2d p/2, "Write auth setup docs" est/1d p/3), reviewable
and editable; Discard so the test files nothing (createIssue POST is unit-tested).
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
Description
Languages
TypeScript
97.8%
CSS
1.7%
JavaScript
0.4%