KENBOARD

A kanban for BOTs.

For humans

KENBOARD kanban view

Regenerate the screenshot after a UI change: pdm run screenshots

For BOTs

KENBOARD ships ken, a CLI built for Claude Code and other assistants: JSON output, native filters, clean exit codes.

Automatic onboarding

An admin clicks Copy onboard link on a project in the kenboard
The link is handed to the agent (Claude Code, GPT, etc.)
The agent opens the link and gets a complete .ken file with a pre-filled API token — zero human interaction for the API key
The agent runs pip install kenboard, creates the .ken, and starts working immediately

Daily workflow

ken list --status todo --who Claude --json   # assigned tasks
ken show <id> --json                         # task details
ken move <id> --to doing                     # mark in progress
ken add "MODULE / Title" --desc "..." --who Claude  # create
ken move <id> --to review                    # submit
ken wiki groom <id> <section>                # classify for the wiki

Full workflow: todo → doing → review → groom → done. The agent handles todo → doing → review then ken wiki groom. Only the user moves review → done. ken move --to review and ken update --status review print a one-line reminder about the grooming step so the agent doesn't forget.

Wiki — exporting the board as a structured doc tree (#376)

Inspired by Karpathy's LLM Wiki pattern: the kanban board is the source of truth, and an LLM agent classifies each task into a section of ARCHITECTURE.md (a YAML-frontmatter file at the project root that declares the wiki's section tree). The CLI then exports the result as a navigable MD/HTML tree.

ken wiki groom                       # list unclassified + sections
ken wiki groom <id> <section>        # assign a task to a section
ken wiki sync   --out wiki           # MD tree (committed in /wiki)
ken wiki build  --in wiki --out wiki-html   # standalone HTML
ken wiki lint   --strict             # orphans / unclassified (CI gate)

The server stays unaware of ARCHITECTURE.md — it stores opaque (task_id, section_path) pairs, the CLI does the validation.
Each section index splits "En cours" (todo/doing/review) and "Archivé" (done). Per-task pages mirror the board's full-screen task view (header + meta + description).
ken wiki lint --strict exits 1 on orphans / unclassified tasks — wire it into your CI to keep the wiki in sync with the board.

A live snapshot of the export lives under /wiki in this repo.

Browser extension — quick-capture from any page (#480)

Sideload-only browser extension that pops a "create task" dialog from the current tab with Ctrl+Shift+K (Cmd+Shift+K macOS). Pre-fills the title from the page title, attaches a PNG screenshot of the visible viewport, posts to POST /api/v1/tasks with your existing api_key. Zero new server code — reuses the standard bearer auth.

Install + first-run guide: extension/README.md. Pre-zipped sideload package on every release: GitHub Releases.

VS Code extension — the board in your editor (#1127)

Sidebar view of the current project's tasks (todo / doing / review), task detail rendered like the board's fullscreen view, Move to… from the context menu, and a link back to the board. Zero configuration: it reads the same ken.ini / .ken as ken and talks to the REST API with your api_key. List and open detail refresh every 60 s (kenboard.autoRefreshSeconds).

Install: download kenboard-vscode-<version>.vsix from the GitHub Releases, then code --install-extension kenboard-vscode-<version>.vsix. Details: vscode/README.md.

References

Full CLI: doc/ken-cli.md
Agent guide: ken help
REST API: doc/api.md, doc/openapi.yaml

Enterprise

KENBOARD is designed for self-hosted enterprise deployment:

OIDC authentication — sign in through a corporate Identity Provider (Microsoft ADFS, Google Workspace, Authentik, Keycloak, etc.) alongside or instead of password login. See doc/oidc-adfs.md for the ADFS guide.
Self-hosted — no cloud dependencies. MySQL + Flask + gunicorn on your own infrastructure, behind your reverse proxy / WAF.
Per-project API keys — each agent or integration gets a scoped token (read/write) for a specific project. AI agents self-onboard through the runbook served by the server.
Commercial support — setup assistance, IdP integration, and operational support available on request. Contact: 2113.ch

Installation

See INSTALL.md for the full setup (MySQL, users, migrations, reverse proxy, OIDC).

License

MIT — see LICENSE.

Metadata

Release files for kenboard 0.4.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for kenboard 0.4.7
File	Size	Uploaded
kenboard-0.4.7.tar.gz	376.8 kB	Oct 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for kenboard 0.4.7
File	Interpreter	ABI	Platform	Reset
kenboard-0.4.7-py3-none-any.whl 349.2 kB Oct 5, 2026	Python 3	none	any	Details

Total release size: 726.0 kB

Release files / kenboard-0.4.7.tar.gz

Download URL	kenboard-0.4.7.tar.gz
Size	376.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`7b61755f036351336ee90324c5ef1db460088268d2a2f21ea054aa9fff6cc1b5`
BLAKE2b-256 checksum How to use checksums	`12f02f60f6e28e6c77654f3bc9e9e072ded3f484a99d8ed0e96678903fd9e0e5`
Upload date	Oct 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`pdm/2.29.2 CPython/3.13.15 Linux/6.17.0-1022-azure`

Release files / kenboard-0.4.7-py3-none-any.whl

Download URL	kenboard-0.4.7-py3-none-any.whl
Size	349.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`8d0d18074396d97a85d3e4428be441b29bbfc44148a719832e91c70f47300b42`
BLAKE2b-256 checksum How to use checksums	`71d8ed9865fe3f80c085e86b4c86f4e82aa67f2289d450ca0c62abf19a44f3d8`
Upload date	Oct 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`pdm/2.29.2 CPython/3.13.15 Linux/6.17.0-1022-azure`