dsh-go-game
DeepSeek Harness 的围棋（围棋/weiqi）：完整规则引擎、三级 AI 对手、死子估算器、LLM 复盘、SGF 保存/加载、DSH 面板和独立页面
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:raymondkm2025/dsh-go-game#6e7eea1077065c0225ae1f6e7253856dccf5adf6Go Game 以 dsh-go-game 发布，当前版本为 1.0.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
圍棋 for the DeepSeek Harness. A full rules engine, a three-level AI partner,
a playout dead-stone estimator, optional LLM review, SGF save/load — surfaced
both as a DSH panel (shell.overlay) and as a standalone page at
/dsh-go-game/.
Rules (host half, pure JS). 19×19 / 13×13 / 9×9, Chinese area scoring (stones + surrounded empty points), configurable komi and handicap, capture by liberty exhaustion, no suicide, simple ko (immediate recapture forbidden), pass / two-pass end, resignation, unlimited undo, SGF import and export. Dead-stone adjudication is explicit: both sides pass, dead stones are marked (manually or by the estimator), then the area is counted.
The AI. Three levels, all run in the host process:
| level | how it plays |
|---|---|
easy 入門 | random legal moves that avoid filling its own eyes |
medium 中級 | greedy tactics: capture, rescue from atari, atari the opponent, shape/line preference |
hard 進階 | the medium candidate list, each scored by light random playouts inside a wall-clock budget |
Honest limit: this is a practice partner, not an engine. On 19×19 even
hard is far below a human amateur; on 9×9 it plays a recognisable game.
Dead-stone estimator. 形勢判斷 runs random playouts (avoiding own-eye
fills) from the current position and reports which stones rarely survive, plus a
per-point ownership map the board renders as a translucent overlay. It is an
estimate, not an adjudication — correct anything it gets wrong with 標記死子.
LLM review (optional). 覆盤 asks the harness llm service (no API keys in
this plugin) for a short Cantonese commentary plus a suggested move, which the
panel offers as a one-click play. Without a configured model it degrades to the
local scoring summary.
| file | face | role |
|---|---|---|
lib/engine.js | shared | rules: board, capture, ko, score, SGF — no DOM, no DSH imports |
lib/ai.js | host | heuristics, playout opponent, dead-stone estimator |
lib/gtp.js | host | GTP bridge to an external neural-network engine (KataGo) |
lib/index.js | host | sessions + every /dsh-go-game/* route, LLM review, SGF files |
lib/client.js | web | the React panel registered in shell.overlay |
lib/page.html, lib/page.js | web | the standalone page (a thin view over the same routes) |
cordis.patch.yml | — | inserts the plugin row into the web profile roster |
test/*.test.mjs | — | 98 cases: 26 engine, 11 AI, 22 host routes, 26 GTP, 13 engine fallback |
check-plugin.ps1 / .cmd | — | read-only install check |
No rules live in the browser: both views post intent and re-render the state the host returns.
node test/engine.test.mjs
node test/ai.test.mjs
node test/host.test.mjs
node test/gtp.test.mjs # GTP bridge, driven by an injected fake engine
node test/engine-fallback.test.mjs # engine selected but absent -> built-in AI
node test/katago-live.mjs --root=<katago dir> # real engine, skipped without --root
Run each file directly; node --test fails on this machine (the runner spawns a
piped child process and the sandbox rejects it). The host suite drives the real
routes through a mock webserver, mock req/res and a mock llm, so the whole
non-UI chain is covered offline.
lib/ai.js is a heuristic/playout practice partner. For real strength,
lib/gtp.js drives an external KataGo process over GTP, side by side with the
built-in AI: the panel's 引擎 selector picks one per game, and a missing or
broken engine falls back to the built-in opponent automatically (the reason is
shown in the panel, not swallowed).
Layout it looks for (relative to the workspace, i.e. one level above the plugin):
katago/engine/katago.exe Eigen (CPU) build, or any other backend
katago/engine/default_gtp.cfg optional, ships with the release
katago/weights/*.bin.gz one or more networks
Environment overrides:
| variable | default | meaning |
|---|---|---|
DSH_GO_GAME_KATAGO_ROOT | <plugin>/../../katago | engine + weights root |
DSH_GO_GAME_KATAGO_THREADS | 4 | search threads (stay below the core count) |
DSH_GO_GAME_KATAGO_VISITS | 250 | hard visit ceiling per move |
DSH_GO_GAME_KATAGO_TIME | 8 | seconds per move — bounds CPU latency |
DSH_GO_GAME_KATAGO_TIMEOUT | 90000 | GTP command timeout (ms) |
GET /dsh-go-game/engine reports availability, the network list and live status;
POST /dsh-go-game/engine {model:"<file>"} switches network, restarting the
process (KataGo loads exactly one network at startup).
Measured on this machine — i5-8250U, 4 threads, no GPU, b18c384 on the AVX2 Eigen
build: first move ≈ 11 s (it loads the 93 MB network), later moves ≈ 8 s with
maxTime=8. Because the search is time-bounded, slower hardware loses strength
rather than latency. Every query replays the game onto a clean engine board after
an explicit clear_board: KataGo does not wipe the stones on a same-size
boardsize, which otherwise surfaces as a spurious "illegal move".
The agent sandbox cannot write outside the workspace, so profile registration is a manual step — run these in a normal terminal:
In the active profile package.json (%APPDATA%\dsh-desktop\harness\profiles\web):
add "dsh-go-game": "link:C:/AppDev/DSH_WORKSPACE/dsh-go-game" to
dependencies, and append "dsh-go-game" to dsh.profile.bundles.
Point node_modules at the source directory (DSH does not run pnpm install
on boot, and a missing link fails silently):
mklink /J "%APPDATA%\dsh-desktop\harness\profiles\web\node_modules\dsh-go-game" "C:\AppDev\DSH_WORKSPACE\dsh-go-game"
Restart DSH.
check-plugin.cmd verifies all four links of the chain (package → registration →
node_modules → live route) at any point.
Routes are unauthenticated on loopback, matching the other local plugins:
GET /dsh-go-game/status answering JSON is the fastest proof of life.
<workspace>/.dsh-go-game/games/.shell.overlay, its pointer-event behaviour, and the LLM review path against a
real provider. The engine, AI and every route are covered by the offline
suites.