wechat

name	wechat
description	macOS WeChat CLI + local HTTP bridge + Wechaty Puppet gRPC gateway — send messages, query sessions / contacts / chat history / images / favorites, and expose stable HTTP / gRPC surfaces for agent integration. Use when the user asks to 'send a WeChat message', '发微信', query WeChat contacts/groups/messages, look up who said what in a chat, fetch images from history, export chat history, wire WeChat into Hermes / n8n / Dify / LangChain, or run any wechaty bot on a real macOS WeChat account. Requires WeChat 4.1.8 / 4.1.9 on macOS (Apple Silicon) and a `wxp_act_` activation code. One-time `wechat init` extracts the DB key; no sudo, no re-signing WeChat.app. Optional remote bridge — `wechat tunnel setup --hostname <yours>` exposes the local REST API via Cloudflare Tunnel for remote services to call.
metadata	{"author":"leeguooooo","version":"1.12.1","platform":"macOS-arm64","requires":["macOS >= 14 (Apple Silicon)","WeChat 4.1.8 (CFBundleVersion 36830 / 37335 / 37342) running","LLDB (Xcode Command Line Tools)","Accessibility permission for `wechat-bridge` (macOS Sonoma+, only for `send`; Terminal itself does NOT need it)","Activation code (wxp_act_…) from @WechatCliBot — subscribe the official Telegram channel first"]}

wechat — macOS CLI

Unified CLI for WeChat on macOS. Send messages in pure background (zero UI flash) AND query the local SQLCipher databases for sessions, contacts, chat history, group members, Moments, favorites.

Fast path (read this first)

Send a WeChat message in one call:

wechat send "早上好" Lisa                 # fuzzy name match (remark / nick / alias)
wechat send "hi" filehelper              # wxid — zero DB lookup, fastest
wechat send "提醒一下" 20590343959@chatroom  # group wxid (ends in @chatroom)

Resolution rules (applied in order):

1. RECIPIENT matches a wxid shape (wxid_…, …@chatroom, gh_…, biz_…, or reserved like filehelper) → skip all DB work and send directly.
2. Otherwise, search the local contact DB (remark / nickname / alias / wxid) with session-recency bias:
   • single match → send
   • multiple matches but only one has recent activity (30d) → send to that one
   • otherwise → exit 2 + JSON {"status":"ambiguous","candidates":[...]}; the agent picks and retries with the explicit wxid

On ambiguous, a sample response:

{
  "status": "ambiguous",
  "hint": "Lisa",
  "candidates": [
    {"wxid": "lishuang683451", "display_name": "lisa", "last_seen": "2026-04-20 05:34:55"},
    {"wxid": "wxid_xxx", "display_name": "Lisa (另一个)", "last_seen": ""}
  ],
  "note": "multiple matches; pass one of the wxids explicitly: wechat send <text> <wxid>"
}

Agent should: read candidates[0].wxid, retry wechat send "<text>" <wxid>. Don't ask the user unless the top candidate has no recent activity or multiple candidates do.

HTTP Bridge for agent integration (v1.10+)

wechat-bridge is a separate binary that wraps the daemon's RPCs as a stable localhost HTTP surface. Use this when wiring WeChat into agent platforms (Hermes, n8n, Dify, LangChain, custom bots) — HTTP is cheaper to integrate than spawning the CLI per call.

# Start bridge (binds 127.0.0.1:18400 by default)
wechat-bridge &

# Health + send-readiness
curl http://127.0.0.1:18400/health

# Send
curl -X POST http://127.0.0.1:18400/send \
  -H 'Content-Type: application/json' \
  -d '{"wxid":"filehelper","text":"hi"}'

# SSE message stream — ⚠️ ALWAYS pass ?since=<epoch>
# Without ?since, default is 0 = backfills entire local message history
# (1MB+ in seconds for typical accounts). For agent / long-running flows
# always pass a since timestamp; pick "now" for live-only or last-checkpoint.
SINCE=$(date +%s)
curl -N "http://127.0.0.1:18400/messages/stream?since=$SINCE"

Endpoints:

Method	Path	Maps to
GET	/health	ping + send_status
GET	/chats	sessions
GET	/unread	unread
GET	/contacts	contacts (query + limit)
GET	/chat/:wxid	recent N messages for one chat
GET	/chat/:wxid/history	history (limit + since + until)
GET	/resolve	resolve_recipient
POST	/send	send_text — returns {status: delivered / submitted_unconfirmed / status_unknown / failed, diagnostic, ...}
POST	/typing	typing indicator (only when --shape hermes)
GET	/messages/stream?since=<epoch>	new_messages_since polled into SSE; pass since or you'll get the full backlog on first connect

SSE payload shape (v1.10.28 — Wechaty-aligned + isMentioned)

/messages/stream emits event: messages carrying a JSON array of:

{
  messageId: string,
  chatId: string,                // wxid (DM) or groupid@chatroom
  senderId: string,              // in group: sender's wxid; in DM: the other party's wxid
  senderName: string,
  chatName: string,
  isGroup: boolean,
  body: string,                  // human-readable text. For URL / quote / mini_program, body is the title — raw XML is NOT exposed here.
  hasMedia: boolean,
  mediaType: "image"|"voice"|"video"|"file"|"",
  mediaUrls: string[],           // first entry is CDN URL when applicable
  mentionedIds: string[],        // v1.10.25+ — authoritative @-mention list from WeChat msgsource <atuserlist>
  isMentioned: boolean,          // v1.10.28+ — bridge-authoritative "this row @-mentions ME". Self-sent rows are always false.
  quotedParticipant: string,     // v1.10.27+ — populated from refer.fromUser on quote replies
  botIds: string[],              // legacy heuristic self-marker; NEW consumers should rely on fromSelf instead
  fromSelf: boolean,             // v1.10.25+ — bridge-authoritative "this row was produced by our own POST /send"; DROP THESE to avoid self-echo loops
  messageKind: "text"|"image"|"audio"|"video"|"contact"|"emoticon"|"location"|
               "url"|"attachment"|"mini_program"|"chat_history"|"transfer"|
               "red_envelope"|"recalled"|"system"|"unknown",  // v1.10.27+, aligned to Wechaty's MessageType enum
  urlLink?:     { title, description, url, thumbUrl },                             // present iff messageKind=url
  miniProgram?: { title, description, appId, username, pagePath, thumbUrl },        // present iff messageKind=mini_program
  refer?:       { svrId, fromUser, chatUser, displayName, content },              // present on quote replies
  recall?:      { replacedMsgId, text },                                            // present iff messageKind=recalled
  media?:       { aesKey, md5, cdnUrl, cdnThumbUrl, length, durationSeconds, localPath },  // structured metadata for image/audio/video/attachment
  timestamp: number,
}

The full JSON Schema is committed at wx/schema/sse-payload-v1.10.28.schema.json and enforced by a contract test in the daemon build.

Consumer checklist:

• Filter self-echo with fromSelf === true. Do NOT use senderId === myWxid — in DM both directions share the same senderId.
• In groups, only respond when isGroup && isMentioned — the daemon already does the wxid-vs-atuserlist comparison, so don't reimplement mentionedIds.includes(myWxid) yourself (your wxid may be a remark / lookup that the daemon resolves correctly). The bridge will also drop non-@ group rows automatically when WECHAT_BRIDGE_GROUP_MENTION_ONLY=1.
• Need the URL only? mediaUrls[0]. Need aesKey + md5 to decrypt or verify? media.cdnUrl / media.aesKey / ….
• For messageKind: "image", do not inline base64 image bytes in chat responses. Call wechat image get <messageId> --chat <chatId> --json, parse absolutePath, then use the host agent's file/image Read capability on that path. Default --from auto (since v1.13.11) tries the daemon's heap scan first (fast, works when the user has opened the image at least once in WeChat) and falls back to CDN replay only on miss. If the result is image not yet viewed in WeChat (heap empty), and CDN fallback failed, ask the user to open the image once in WeChat and retry. cdn-expired or needs local-decrypt RE means neither path can recover this image — surface that to the user instead of guessing.
• Expect body for URL / quote / mini_program to be the human title. If you were previously parsing raw <appmsg> XML from body, migrate to the dedicated urlLink / miniProgram / refer objects.
• Backward compatible: every pre-v1.10.25 field is preserved in name + type. New fields are additive.

Security notes for agents:

• Bridge binds 127.0.0.1 — not exposed to LAN without tunnelling.
• Set WECHAT_BRIDGE_BEARER=<secret> env var to require Authorization: Bearer <secret> on non-/health routes. Use this if tunnelling via Tailscale / SSH.
• Activation gating is enforced inside wechatd, not in the bridge. A missing / expired wxp_act_ token → HTTP 401 / 402 on /send. Bridge cannot bypass subscription.

Command groups

Group	Commands	First-time requirement
Diagnostics	doctor	— (run first; checks AX permission, daemon status, dylib SHA-256 fingerprint)
Setup	init	requires user to click 进入 WeChat during the ~5 min window
Send	send	first send after each WeChat restart fails with delivery_verify_timeout until the user manually types + Enters one message in WeChat to warm up the Qt slot_send signal chain (~5 s)
Query (messaging)	sessions, unread, new-messages, contacts, history, search, members, stats, export, image	init first; daemon auto-starts on demand (v1.7.5)
Saved items	favorites	init first; daemon auto-starts on demand
Realtime (v1.3+)	listen	daemon auto-starts on demand (v1.7.5)
Daemon (v1.2+)	daemon start|stop|status|ping	optional — query/listen commands pull it up automatically when needed
HTTP Bridge (v1.10+)	wechat-bridge (separate binary)	agent / Hermes / n8n integration over localhost HTTP — see section below
Wechaty Puppet gateway (v1.10.32+)	wechat-wechaty-gateway (separate binary, gRPC :18401)	for the human writing a wechaty bot — NOT used by this skill. If the user asks "can I run my wechaty bot on this?", point them to https://github.com/leeguooooo/wechat-skill#接-ai-agent and stop. Don't try to write wechaty TS from this skill.
wechat tunnel (v1.11+)	wechat tunnel setup	Expose local REST bridge to a remote service via Cloudflare Tunnel; details in docs/remote-gateway.md, do NOT inline the full setup flow in this skill.
wechat orchestrate (v1.12+)	wechat orchestrate setup --outbox-url= --webhook-url= --bearer= --webhook-secret=	Long-running worker that polls a SaaS outbox API and pushes SSE inbound events to a SaaS webhook. NAT-friendly (Mac all-outbound, no public IP / domain). Used by SaaS integrations (cherry-class). Protocol: docs/v1.12-orchestrate-protocol.md. Don't inline the SaaS-side endpoint design here.
Auth (v1.9.1+)	auth activate | status | renew	mandatory activation before send — code from @WechatCliBot on Telegram

All query commands default to YAML output (agent-friendly, low token). Add --json to get JSON.

---

🛑 Safety rules (CRITICAL — read before calling send)

Every send call must resolve to a known wxid. No silent default to "current chat" — that flag (--current-chat) was removed pre-1.13; if the resolver can't find a recipient, stop and ask the user.

Correct flows for "给 XXX 发 YYY":

1. Just try it: wechat send "YYY" XXX. Fast-path resolver (see top of this doc) handles wxid-shaped targets instantly and fuzzy-matches names against the local contact DB with session-recency bias.
2. On exit 2 + status: "ambiguous": if candidates[0] has last_seen within ~30 days and others are stale/empty, the CLI already auto-picked it and returned success. If it truly was ambiguous (multiple candidates with recent activity), pick one yourself by asking the user — don't guess.
3. On no contact matches "XXX": ask the user for the wxid (or have them confirm a candidate from wechat contacts --query XXX --brief).

Hard rules (the agent MUST follow):

• DO NOT guess or fabricate a wxid. If resolution fails, escalate to the user.
• DO NOT scan the filesystem / grep logs / use AppleScript to hunt for a wxid. The CLI already searches the local contact DB via the fast path — trust it. If it can't find the recipient, stop and ask the user.
• DO NOT invoke wechat contacts followed by wechat send as two separate calls unless the first fast-path send already told you it was ambiguous. The one-liner saves ~400ms and one agent round-trip.

---

Capability matrix

Capability	Status	Command
Extract DB key, cache layout (required first step for query commands)	✅	wechat init
Send text to a specific wxid / 群名 / 昵称	✅	wechat send "..." <recipient>
Any Unicode / emoji / CJK / length	✅	built-in
Zero UI flash (no focus steal)	✅	default for send
List recent chat sessions	✅	wechat sessions
Sessions with unread messages	✅	wechat unread
Incremental new messages since last check	✅	wechat new-messages
Contact lookup / fuzzy search	✅	wechat contacts [--query KW]
Chat history (private / group)	✅	wechat history <chat> [-n 500]
LLM-ready group digest	✅ v1.13.33	wechat digest <chat>
Full-DB keyword search	✅	wechat search <kw> [--in CHAT]
Group members	✅	wechat members <group>
Chat statistics (senders / types / hours)	✅	wechat stats <chat>
Export chat → Markdown / JSON	✅	wechat export <chat> --format markdown -o ...
Favorites (text/image/article/...)	✅	wechat favorites [--type ...] [--query KW]
Image media (heap scan + CDN fallback)	✅	wechat image get <messageId> --chat <id>
Voice media (raw SILK_V3)	✅	wechat audio get <svr_id> (1.13.21+)
Voice transcribe (whisper.cpp + SILK pipeline)	✅	wechat audio setup 一次 + wechat audio transcribe <svr_id> (1.13.25+)
InputView warmup (manual, once per WeChat session)	required	first send errors with delivery_verify_timeout; user types one msg in WeChat then re-runs wechat send
Realtime inbound stream (v1.3)	✅	wechat listen — watches new messages, push to stdout
Inbound callback → shell command (v1.3)	✅	wechat listen --on-message "handler.sh" (WECHAT_MSG_* env vars)
Server-side wxid filter (v1.3)	✅	wechat listen --wxid filehelper
Background daemon (v1.2+, lazy-start v1.7.5)	✅	wechat daemon start — or auto-spawn by any query command
Dylib SHA-256 fingerprint verification (v1.7.2+)	✅	wechat doctor surfaces drift after Tencent hot-fix updates
Send image / file	⏳ roadmap	—
Group broadcast	❌ disallowed	anti-abuse; LICENSE forbids
Linux / Windows / Intel Mac	❌	macOS arm64 only
WeChat build ≠ 36830 / 37335	⚠️ unverified	offsets may drift; wechat doctor flags it

---

Agent: first-use setup

Step 1 — Check wechat is on PATH:

command -v wechat

If missing:

curl -fsSL https://raw.githubusercontent.com/leeguooooo/wechat-skill/main/install.sh | bash
# Ensure ~/.local/bin is on PATH
case "${SHELL##*/}" in
  fish) fish_add_path "$HOME/.local/bin" ;;
  zsh)  grep -q '.local/bin' ~/.zshrc  2>/dev/null || echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc ;;
  bash) grep -q '.local/bin' ~/.bashrc 2>/dev/null || echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc ;;
esac
export PATH="$HOME/.local/bin:$PATH"

Step 2 — Run wechat init (required before any query command):

wechat init

This restarts WeChat (closes current session + relaunches) in order to capture the decryption key at login. Tell the user:

"Going to briefly close and relaunch WeChat to extract the local database key. Any draft messages in WeChat will be lost — confirm before proceeding. After WeChat relaunches, you must click 「进入 WeChat」 (or scan QR if no cached account) within ~5 minutes — the key is only written during that sign-in."

Key is only written to memory during the login moment, so init attaches LLDB with a conditional breakpoint and waits up to 300 s. If the user misses the window or WeChat was already logged in before init ran, the breakpoint never fires — rerun wechat init --force.

Result saved to ~/.wx-rs/key.hex (mode 0600) + ~/.wx-rs/config.json. Re-run init whenever WeChat restarts.

init also prints the detected WeChat version/build and the wechat.dylib SHA-256 fingerprint check. If the hash isn't in the verified set (e.g. Tencent pushed a hot-fix dylib), send/query may silently fail at the LLDB layer — reinstall the official dmg from https://mac.weixin.qq.com/en and verify the auto-update toggle at WeChat → 设置 → 通用 → 「有更新时自动升级」 is off.

Step 3 — (For send only) Accessibility permission:

Run wechat doctor. If the terminal hasn't been granted Accessibility yet, this pops the native macOS dialog and opens the Privacy & Security → Accessibility pane directly — no hunting. Toggle the terminal app ON, then quit + relaunch the terminal (macOS requires a restart for the permission to take effect).

If you prefer the manual path: System Settings → Privacy & Security → Accessibility → add the terminal app you're using (Terminal / iTerm / Warp / …).

Step 4 — (For send only) One-time InputView warmup per WeChat session:

send needs WeChat's Qt slot_send signal chain to be wired, which only happens after a real user-initiated send. The first wechat send after each WeChat restart fails with:

error: 消息发送路径已执行,但数据库核查窗口内没有找到匹配新消息。常见原因:WeChat 输入框 (Qt slot_send signal chain) 还没 warmup,首次 send 之前需要在 WeChat 里手动发一条让 signal 链建好。

Resolution (the error itself prints these steps):

1. Open WeChat
2. Click "文件传输助手" (filehelper)
3. Type any short message (e.g. hi)
4. Press Enter to send
5. Re-run wechat send — succeeds on retry, and stays warm until WeChat restarts.

This is intrinsic to WeChat's Qt event loop; no automation can substitute. If wechat doctor reports ax_trusted: true and warmup still doesn't help, file an issue.

---

Usage — send

# Recipient resolves wxid / 群名 / 昵称 / 备注 (fuzzy match against local contact DB)
wechat send "你好 🎉" filehelper
wechat send "会议 5 分钟后开始" lishuang683451
wechat send "早上好" Lisa                # 找不到 → friendly error + 候选

# Group send (resolver also handles group display names)
wechat send "今天 19:00 团建" "AI 星球"

# JSON output for agents that parse responses
wechat send "ok" filehelper --json

# Dry-run: resolve recipient + validate args, do NOT actually send. Useful when
# the agent wants to verify a fuzzy name → expected wxid before committing.
wechat send "draft" "李工" --dry-run --json

send arguments

Arg	Required	Description
<TEXT> (positional)	yes	Message body. Any length, any Unicode.
<RECIPIENT> (positional) or --wxid	yes	Target wxid / chatroom id / 昵称 / 群名 / 备注. Resolver picks the most-recently-active match if hint is fuzzy.
--mention <wxid>	no	Visual @<name> prefix (text-only, no real ping ack — see issue #4).
--dry-run	no	Resolve recipient + validate but don't send. Pairs well with --json for agent dry-checks.
--json	no	JSON output

send --json 三态契约 (v1.13.20+)

所有 --json 输出都带顶层 ok: bool,agent 直接 if (r.ok) {...} else {...} 不需要解析三套 schema:

状态	触发	shape (顶层字段)
success	wechat send TEXT RECIPIENT --json 真发成功	{ok: true, sent: true, reason: null, diagnostic: {…SendResult 全字段…}}
dry-run	--dry-run --json(resolver OK + 不真发)	{ok: true, dry_run: true, text, resolved_wxid}
error (early)	--json + 参数错 / resolver 找不到 / ambiguous / 网络断	{ok: false, exit_code: <int>, error: "<msg>"}
error (send fail)	真发失败 (InputView warmup miss / TCC 缺 / dylib mismatch)	{ok: false, sent: false, reason: "<reason>", diagnostic: {…}}

# 推荐:agent 用 jq 分支
wechat send "hi" filehelper --dry-run --json | jq -e '.ok' && echo "✓ resolved" || echo "✗ failed"

stderr 仍然有 human-readable 错误描述(给终端用户看);agent 只需 parse stdout JSON。

---

Usage — query

# Sessions (recent conversations)
wechat sessions -n 20                           # full yaml
wechat sessions --brief -n 20                   # 单行/会话, 带未读数
wechat sessions --filter group --json -n 20     # 只看群聊 (chat_type: group / private / official_account / folded / other)
# JSON 字段命名:未读数是 `unread_count`(下划线全名), 不是裸 `unread`。brief 视图渲染成 [N unread] 仅是显示, 实际字段是 unread_count。

# Contacts
wechat contacts --query 李                      # fuzzy match nickname/remark/wxid
wechat contacts --brief -n 50                   # 单行/联系人 (姓名 + wxid)

# Unread
wechat unread -n 5

# History (chat positional or --chat flag, both accepted)
wechat history "张三" -n 2000
wechat history --chat 21263894984@chatroom -n 200
wechat history "AI 星球" --since "2026-04-01" --until "2026-04-15" -n 200   # ISO date OK
wechat history "AI 星球" --since 1719793200 --until 1720484400 -n 200       # epoch OK too

# Search
wechat search "会议纪要"
wechat search "报销" --in "财务群"

# Group members
wechat members "AI 星球"

# Stats
wechat stats "AI 星球"

history --json payload shape (stable contract for agents)

每条 message row 字段(snake_case):

字段	类型	说明
local_id	int	DB 行主键(per chat 单调)。image get <local_id> --chat <wxid> 用这个取图。
server_id	int	WeChat 服务端 msg id(撤回时引用 replacedMsgId)。
local_type	int	原始 type code。低 16 位 mask 后 = 1 文本 / 3 图 / 34 语音 / 43 视频 / 49 appmsg / 等。
message_kind	string	enum: text / image / audio / video / url / mini_program / recalled / appmsg / 等。Wechaty 对齐。
display_text	string	已清洗后的 human-readable body(text 直接 = body;image/url 抽 title;recalled 给替代文案)。
message_content	string	原始 body(可能是 raw XML / 群消息带 <sender>:\n 前缀)。debug 用,生产逻辑请用 display_text。
sender_wxid	string | null	发送者 wxid(群消息 = 真发送者;DM 自己发的 = null,用 IS NULL 判 self-sent,别用 == self_wxid)。
real_sender_id	string	per-chat 自增 ID(字符串)。永远非空;不是 wxid,别拿来做 self 判定。
chat_id / username	string	会话 wxid(DM)或 xxxx@chatroom(群)。
chat_display_name	string	群名 / 联系人备注 / 昵称(v1.13.9+ 自动解析)。xxxx@chatroom 直接看得懂。
create_time	int	epoch seconds。
is_mentioned	bool	当前账号在群里被 @ 了(daemon 端从 msgsource <atuserlist> 解,客户端别再算一遍)。
media	object	image / voice / video / file 才有: {aesKey, md5, cdnUrl, cdnThumbUrl, length, durationSeconds, localPath, dat_path?, dat_md5?, dat_exists?}。
urlLink / miniProgram / refer / recall	object	type-specific 结构化字段(见 SSE schema)。

字段稳定性:增加 = 默认 null / 缺省;不会重命名 / 改类型(契约由 v1.10.27 起的 SSE schema 单测守)。

# Export
wechat export "张三" --format markdown -o zhang.md
wechat export "AI 星球" --format json -o ai.json -n 5000

# Incremental (since last checkpoint saved in ~/.wx-rs/cursor.json)
wechat new-messages -n 50      # advances checkpoint
wechat new-messages --reset    # rewind checkpoint to "now" so next call starts fresh

# Favorites
wechat favorites                          # all locally-cached items

# Image media (heap scan first, CDN fallback)
wechat image get <local_id> --chat <chat_id>            # decrypts + writes to ~/.wechat/media-cache/<md5>.jpg
wechat image inspect <local_id> --chat <chat_id>        # dump CDN metadata (no key/url leak)

# Voice media — history auto-transcribes by default (v1.13.25+)
wechat audio setup [--model small|medium|large]         # one-time: install deps + download model
wechat audio transcribe <svr_id> [--language zh]        # single-file pipeline
wechat audio get <svr_id>                               # raw SILK_V3 bytes, no decode

Voice — agents reading group history just work (v1.13.25+)

After running wechat audio setup once (~2-3 minutes downloads ~1.5GB medium model + builds silk-decoder), wechat history automatically transcribes voice messages so the agent sees the spoken content in display_text (and structured in media.transcript) — no more [语音消息] placeholders breaking conversation context. Transcripts are cached by SHA-256 of the audio blob, so re-reading the same chat is near-instant.

# One-time setup
wechat audio setup

# Read a chat — voice messages already transcribed inline
wechat history <chat> --json | jq '.[] | {kind: .message_kind, text: .display_text}'

Opt-out (skip transcribe to keep history fast / private):

wechat history <chat> --no-transcribe                   # skip transcribe entirely
wechat history <chat> --transcribe-model small          # smaller / faster model
wechat history <chat> --quiet                           # silence stderr progress lines
                                                        # (auto-on in --json mode)

The media.transcript_status field on each audio row tells the agent where the text came from: cached / transcribed / no_deps (run wechat audio setup) / failed / skipped_svr_id_zero / invalid_input.

wechat doctor reports audio readiness in two rows so machine consumers can check default-model status without parsing strings:

• audio_transcribe_setup — overall ffmpeg / whisper-cli / silk-decoder presence. Always ok: true (audio is optional; missing tools must not flip overall doctor status to needs_init).
• audio_transcribe_default_model — ggml-medium.bin exists. ok reflects reality, but excluded from the overall status calculation so a user without medium still sees status: "ok". Read this row's ok field directly to know whether wechat history default transcribe will work.

For a single voice file outside history:

SVR=$(wechat history <chat> --json | jq -r '.[] | select(.message_kind=="audio") | .server_id' | head -1)
wechat audio transcribe "$SVR"           # prints transcript directly

wechat audio transcribe --json defaults to redacting the transcript text (only metadata in JSON output) so agents can't accidentally log private conversation text. Pass --include-transcript to opt in.

Voice — manual decode if you skip audio setup

wechat audio get <svr_id> writes the raw .silk bytes from media_*.db.VoiceInfo.voice_data (pure SQLCipher BLOB read; no LLDB / heap scan / CDN). To play / share without going through audio transcribe:

# One-time decoder build
git clone https://github.com/kn007/silk-v3-decoder /tmp/silk-v3-decoder
cd /tmp/silk-v3-decoder/silk && make

# Per-file
SVR=691336177198502815
wechat audio get "$SVR"
/tmp/silk-v3-decoder/silk/decoder ~/.wechat/audio-cache/$SVR.silk /tmp/$SVR.pcm
ffmpeg -y -f s16le -ar 24000 -ac 1 -i /tmp/$SVR.pcm /tmp/$SVR.wav

Note: SNS / Moments commands (sns-feed, sns-search, sns-notifications) and the legacy bootstrap subcommand were removed in the v1.13 line. The data is still in sns.db if you query it directly with sqlcipher, but no first-class CLI surface yet — track via roadmap.

---

Usage — realtime listen (v1.3)

wechat listen streams new incoming WeChat messages to stdout as they arrive (latency <500ms). Requires the background daemon.

# One-time: start the daemon (keep running in a separate terminal or `&`)
wechat daemon start

# Stream all new messages
wechat listen

# Stream only messages from one chat (server-side filter)
wechat listen --wxid filehelper

# JSONL output for agent consumption
# IMPORTANT: pipe stdout only — daemon spawn / errors go to stderr.
#   wechat listen --format json | your-adapter      ← OK
#   wechat listen --format json 2>&1 | your-adapter ← WRONG, stderr 混流会把 [daemon] 字样混进 stdin
wechat listen --format json

# Trigger a shell command per message — the handler sees WECHAT_MSG_* env vars
# Handler stdout is routed to /dev/null (so it doesn't pollute the JSONL stream
# that agents pipe to jq); use stderr or write to a file for logging.
wechat listen --on-message "./ai-reply.sh"
wechat listen --wxid lisa --on-message 'echo "[$(date +%H:%M)] $WECHAT_MSG_SENDER_WXID: $WECHAT_MSG_TEXT" >> log.txt'

--on-message env vars

Variable	Meaning
WECHAT_MSG_TEXT	Message body (already cleaned: compressed content decompressed, group <sender>:\n prefix stripped)
WECHAT_MSG_SENDER_WXID	Sender wxid for group messages (empty string for private chats — there the chat wxid = sender)
WECHAT_MSG_TABLE	Msg_<md5(chat_wxid)> — internal table name
WECHAT_MSG_CREATE_TIME	Unix epoch seconds (as string)
WECHAT_MSG_LOCAL_ID / WECHAT_MSG_LOCAL_TYPE	Internal message id + type code
WECHAT_MSG_SENDER_ID	DB real_sender_id (numeric; rarely needed — use SENDER_WXID instead)
WECHAT_MSG_DB	Absolute path of the message DB the message came from

Safety notes:

• Content is passed via env vars, not shell-interpolated into the command. Safe against $(rm -rf) style injection.
• Handler runs async (one subprocess per message); if it takes longer than messages arrive, handlers will pile up. Keep handlers fast or add your own queueing.

Daemon lifecycle

wechat daemon start              # foreground; or `wechat daemon start &` for background
wechat daemon status             # socket + pid + uptime
wechat daemon ping               # round-trip latency sanity check
wechat daemon stop               # graceful shutdown

The daemon caches each SQLCipher DB connection so wechat sessions / contacts / history / unread run in <30ms (vs 400-500ms without it). It also powers wechat listen by watching message_*.db-wal file changes.

Fuzzy chat resolution

history / search --in / stats / export / members accept a <chat> argument that is matched against (in order): exact wxid → remark → nick_name → alias. If ambiguous, the most-recently-active match is picked. Use wechat contacts --query ... first if you need to disambiguate.

Output format

All query commands emit YAML by default. Add --json for JSON:

wechat sessions --json | jq '.[] | select(.chat_type=="private" and .unread>0)'
wechat new-messages --json                # ideal for agents consuming incremental updates

---

When to invoke this skill (agent triggers)

Send:

• "给 Lisa 发消息：..."
• "发微信通知我妈 '到家了'"
• "提醒 XXX 会议 5 分钟后开始"
• "send to filehelper ..."

Query:

• "微信里 Lisa 最近说了什么" → wechat history Lisa
• "搜一下群里谁提过报销" → wechat search 报销
• "AI 星球群有多少人 / 谁发言最多" → wechat members + wechat stats
• "有哪些未读消息" → wechat unread
• "导出我和张三的聊天记录" → wechat export 张三 -o ...
• "XX 群里那张图是什么" → wechat history "XX群" -n 50 (找 message_kind: image 的 local_id) → wechat image get <local_id> --chat <chat_id>
• "最近收藏了什么" → wechat favorites

Realtime:

• "帮我盯着 Lisa 发来的消息，收到就自动回复 XXX" → wechat daemon start then wechat listen --wxid <lisa-wxid> --on-message "..."
• "把微信消息接进我的 AI assistant" → wechat listen --format json --on-message "curl -X POST ..."
• "监控这个群谁提到 '会议'，马上通知我" → wechat listen --wxid <group> + handler that greps

Example user utterances and the right first call:

• "给 Lisa 发消息：会议 5 分钟后开始" → wechat contacts --query Lisa → wechat send --wxid ... --text ...
• "send to filehelper today's summary" → wechat send --text ... --wxid filehelper
• "查一下 XXX 群最近谁发言最多" → wechat stats "XXX"

---

🔐 Security / data scope

• Everything runs 100% locally — no data leaves the machine.
• wechat init caches the raw DB key in ~/.wechat/keys.json (mode 0600). Treat that key like a password — anyone with keys.json + a copy of ~/Library/Containers/com.tencent.xinWeChat/... can decrypt all your WeChat data.
• Never commit ~/.wechat/ to git. Never paste the key into chat windows. If leaked: logout + re-login WeChat to rotate the key.

---

Mechanism (brief)

init — restarts WeChat, sets an LLDB breakpoint at a known write offset, reads the 32-byte raw key as it is written to a register during startup. No codesign --force --deep on WeChat.app, no sudo. Immediately detaches after capture.

Query commands — load the raw key + discovered DB paths from ~/.wechat/keys.json, open each .db via the sqlcipher CLI with WeChat's PRAGMAs (cipher_compat=4, kdf_iter=256000, cipher_page_size=4096, HMAC_SHA512, PBKDF2_HMAC_SHA512), run SELECTs in JSON mode, parse. When the background daemon is running, queries are routed over a local Unix socket to a persistent SQLCipher connection pool — amortizes PBKDF2 cost and cuts latency 5-10×.

listen — watches message_*.db-wal files for mtime changes, queries Msg_* tables for rows newer than the last seen create_time, decodes content (zstd-decompressed if needed), strips the group <sender>:\n prefix, and pushes each new row to subscribed CLI processes over the daemon socket. Zero network traffic; runs entirely locally against the on-disk WeChat databases.

send — uses macOS Accessibility API to set AXHidden=True (silent hide) + setValue into chat_input_field, then LLDB-WriteMemory overwrites InputView+0x2B8 with target wxid SSO std::string, then LLDB expression cold-calls mmui::InputView::Send(0) via its RVA. Zero window activation / focus steal.

---

Caveats

• macOS arm64 only, WeChat 4.1.8 verified. Other versions may need offset rediscovery.
• Binary is a standalone native executable (GitHub Releases). install.sh auto-clears macOS Gatekeeper quarantine attribute.
• Not a WeChat API. Userland research artifact. Can break on any WeChat update.
• LICENSE forbids commercial use — see LICENSE + DISCLAIMER.md.

Updating the CLI

Before starting a session, the agent should check that wechat is reasonably current. The binary is self-contained; upgrading just means pulling a newer release.

Check current version:

wechat --version

Upgrade to latest (safe, idempotent) — re-run install.sh with --force, or without it (it overwrites by default):

curl -fsSL https://raw.githubusercontent.com/leeguooooo/wechat-skill/main/install.sh | bash

That pulls the latest tagged release binary from GitHub, re-installs to ~/.local/bin/wechat, and re-clears any Gatekeeper quarantine attribute. No data is lost — ~/.wechat/keys.json + state.json are untouched.

When to upgrade:

• wechat --version is more than one minor behind the latest release tag at https://github.com/leeguooooo/wechat-skill/releases
• A command that the user asks for doesn't exist yet (the capability matrix on this page reflects the latest release; older binaries may be missing commands)
• WeChat itself was updated — new versions may need a fresh wechat release to track shifted offsets (watch https://github.com/leeguooooo/wechat-skill/releases for a note)

Updating the skill metadata (this SKILL.md itself, when the agent is installed via skills.sh):

# refresh skill files (including this SKILL.md) from the public repo
npx skills update leeguooooo/wechat-skill -g

If the agent sees wechat: command not found after an npx skills update, it still needs to run install.sh — skill updates do not include the binary.

Support

• Issues → https://github.com/leeguooooo/wechat-skill/issues