go-next-move

从围棋/Weiqi 棋盘照片或文本棋盘分析当前局面，调用本地 KataGo 按初级、中级、高级强度推荐下一手。适用于用户询问黑棋或白棋下一手应下哪里、希望按对手水平选择落点，或想在不改变棋盘的情况下获得更均衡的 AI 辅助建议。

围棋下一手推荐

当前范围

这个 skill 是独立于 count-go-black-stones 的围棋落点推荐层。

预期流程：

1. 将棋盘照片转换成 19 路局面。
2. 在可能时询问或推断轮到黑棋还是白棋行棋。
3. 使用中国规则和固定访问数预算，将局面发送给 KataGo。
4. 根据用户请求的落子强度返回推荐手。
5. 附带候选手和足够的分析数据，方便解释或复核选择。
6. 对于无提子的连续推演，保留原始识别棋盘，并在询问下一手前叠加带编号的 AI/用户落子。

安装与数据边界

此 Skill 需要 Python 3.10 或更高版本。安装已锁定版本的 Python 依赖：

bash

python3 -m pip install -r {baseDir}/requirements.txt

这个 Skill 只在本机运行：读取用户明确指定的棋盘图片或文本，启动本地 katago 子进程，并只向用户指定的结果路径、系统临时目录或本机识别标注目录写入文件。它不调用 H5、微信、飞书或其他远程 API，也不读取账号凭据或保存用户身份、完整分析历史。

普通图片识别只使用本地 OpenCV。只有用户明确表示识别不准确时，才允许宿主智能体直接用自身视觉 LLM 复核原图；Skill 不得为此再调用 codex exec 或其他嵌套 LLM。复核产生的候选棋盘、原 OpenCV 棋盘和差异点会作为本地候选标注保存，默认目录为 ~/.go-next-move/recognition-labels，可用 GO_NEXT_MOVE_RECOGNITION_LABEL_DIR 或 --recognition-label-dir 修改。候选标注不是人工确认的 ground truth。

Local KataGo Defaults

KataGo is installed through Homebrew and verified on this machine:

bash

katago version

Expected important line:

text

Using Metal backend

Use this project config after KataGo's bundled GTP config:

bash

katago gtp \
  -model /opt/homebrew/share/katago/g170e-b20c256x2-s5303129600-d1228401921.bin.gz \
  -config /opt/homebrew/share/katago/configs/gtp_example.cfg \
  -config {baseDir}/config/gtp_skill.cfg

Set komi through GTP, not the config file:

gtp

boardsize 19
komi 7.5
clear_board
genmove b

For scripted next-move analysis, prefer the JSON analysis engine:

bash

python3 {baseDir}/scripts/next_move.py /path/to/board.jpg \
  --input image \
  --side-to-move black \
  --level intermediate \
  --visits 400 \
  --overlay /tmp/go-next-overlay.jpg \
  --source-overlay /tmp/go-source-overlay.jpg \
  --source-result-image /tmp/go-source-result.jpg \
  --result-image /tmp/go-next-result.jpg

For photo input, the default user-facing image should be the combined original-photo result. It marks existing white stones with black W, existing black stones with white B, and the recommended move as a numbered stone so the user can compare the recognition against the real board at a glance. Use --result-image only when you explicitly want the clean warped-board rendering with a red ring/dot.

Use --source-overlay for user-facing recognition verification. It marks detected stones on the original photo. --overlay is a warped/cropped board view for debugging and may not look like the original photo.

For no-capture continuation, pass confirmed post-photo moves with repeatable --move-overlay source:color:move:label arguments:

bash

python3 {baseDir}/scripts/next_move.py /path/to/board.jpg \
  --input image \
  --side-to-move white \
  --level intermediate \
  --move-overlay ai:W:Q4:1 \
  --move-overlay user:B:D16:2 \
  --source-result-image /tmp/go-step-3.jpg

This preserves the original recognized board in base_board_ascii, stores confirmed post-photo moves in move_overlays, sends the composed board in board_ascii to KataGo, and draws all confirmed moves plus the new recommendation in display_move_overlays. Do not use this mode after captures; re-shoot/reset the board and analyze one move from the new photo.

Coordinates default to standard GTP letters, which skip I. When the user's physical board uses sequential A-S letters including I, pass --coordinate-style sequential. Use that same style for every --move-overlay; the returned recommendation, candidate moves, PVs, explanations, and numbered overlays will use it consistently. KataGo communication remains GTP internally.

For an already recognized board:

bash

python3 {baseDir}/scripts/next_move.py /path/to/board_ascii.txt \
  --input ascii \
  --side-to-move white \
  --level beginner

用户认为识别错误时的重试

仅当用户明确指出识别结果与真实棋盘不符时，使用下面的纠错流程：

1. 宿主智能体直接查看用户的原始棋盘图片，逐一复核 19×19 共 361 个交叉点。不要调用本地 Codex CLI，也不要让语言模型代替 KataGo 选择下一手。
2. 生成严格的 19 行 board_ascii：每行 19 个字符，X 为黑子、O 为白子、. 为空点；从照片上边缘到下边缘排列，不得把待推荐落点写进棋盘。
3. 将这 19 行写入系统临时文件，然后以原始图片为输入，通过 --board-override-file 传给脚本。脚本会严格校验数组、保留原图的棋盘几何、用修正棋盘调用 KataGo、生成修正后的原图结果，并保存本地候选标注。

bash

python3 {baseDir}/scripts/next_move.py /path/to/original-board.jpg \
  --input image \
  --side-to-move black \
  --level intermediate \
  --board-override-file /tmp/go-agent-board.txt \
  --source-result-image /tmp/go-agent-corrected-result.jpg

如需指定标注库目录，再加：

bash

--recognition-label-dir /path/to/recognition-labels

返回 JSON 中的 recognition_retry 包含 detector_board_ascii、智能体提供的 board_ascii、changed_points、label_id 和 label_path。向用户展示修正后的结果图，并明确这是 LLM 候选标注；若图上棋子仍与实物不符，不要宣称修正成功，也不要采纳该次 KataGo 推荐。

board_ascii is 19 rows of 19 characters:

• X or B: black stone
• O or W: white stone
• .: empty point

The script returns JSON containing:

• board_ascii
• coordinate_style
• base_board_ascii
• move_overlays
• display_move_overlays
• recommendation
• reason
• recommendations_by_level
• candidate_moves
• root_info
• optional result_image when --result-image is passed
• default source_result_image for photo input, or optional source_result_image when --source-result-image is passed explicitly
• optional recognition metadata when input is an image
• optional recognition_retry metadata when --board-override-file is used

Playing-Strength Levels

The level controls move strength, not explanation depth.

• Beginner: choose a plausible but intentionally softer move from KataGo's candidates. It should usually be playable, but may lose several points compared with the best move.
• Intermediate: choose a solid near-top candidate. It should be close to the best move but not always the engine's first choice.
• Advanced: choose KataGo's top searched candidate.

Use --level all when the caller wants all three recommendations at once. Use recommendation for the selected level and recommendations_by_level to compare the three outputs.

The current script chooses levels by candidate rank plus score/winrate loss from KataGo's best move. These thresholds are a practical first pass, not calibrated ranks. The next improvement should tune them with real game examples.

User-Facing Response

When answering a user, include:

1. The recommended coordinate.
2. The generated source_result_image for photo input, or result_image for ASCII input.
3. Why this move was chosen, using reason.summary plus the bullet-like items in reason.explanation.
4. Technical parameters from reason.technical_parameters, especially winrate, score lead, visits, score loss vs best, and PV.
5. Candidate comparison from reason.comparison_candidates when there are meaningful alternatives.
6. The recognition.source_overlay image when available.
7. A recognition caveat if the rendered board or source overlay does not match the real photo.

Do not only return the coordinate. The user-facing answer should always include enough engine data to audit the recommendation: winrate, score lead, visits, and whether the chosen move is the top KataGo move or a deliberately softer level-based move.

Do not invent tactical explanations that are not supported by KataGo data or visible board context. If recognition looks wrong, say the recommendation is not reliable until the board is corrected.

Notes

• Do not rely on the language model alone for high-strength move choice.
• Use KataGo for candidate moves; use the requested level to choose the playing strength of the move.
• A board photo usually does not prove whose turn it is. Ask or require the side to move unless the surrounding context makes it clear.
• Use --move-overlay only for no-capture continuation. If there are captures, ko/state ambiguity, or an overlay point is occupied, ask the user to re-shoot/reset the board and analyze one move.
• If board recognition is uncertain, surface the uncertainty before giving a move recommendation.
• In the Skill entry point, recognition retry uses the current host agent's visual LLM directly. Never spawn a nested Codex process for this path.
• White-stone classification includes center low-saturation and center/ring contrast checks to reduce false positives from glare or bright wood grain.