# Meeting Notes

> 飞书/Lark 妙记 URL → 精炼会议纪要带 @ 责任人 → 发到群或 DM。当用户提到"总结会议纪要 / 总结妙记 / meeting notes / 飞书会议总结 / 妙记总结 / minute summary / 发到群" 等触发词，且消息里包含 https://*.larkoffice.com/minutes/ 或 https://*.feishu.cn/minutes/ URL 时使用。支持中文/英文/双语三种输出，支持 dry-run 预览，支持 P2P 自送。

- **Type:** Skill
- **Install:** `agentstack add skill-aaronartistzhang-afk-dailywork-meeting-notes`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [aaronartistzhang-afk](https://agentstack.voostack.com/s/aaronartistzhang-afk)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [aaronartistzhang-afk](https://github.com/aaronartistzhang-afk)
- **Source:** https://github.com/aaronartistzhang-afk/DailyWork/tree/main/skills/meeting-notes
- **Website:** https://aaronzhang.ai

## Install

```sh
agentstack add skill-aaronartistzhang-afk-dailywork-meeting-notes
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Meeting Notes Hybrid Skill — for Claude Code

> **Install (one-time)**: place this file at `~/.claude/skills/meeting-notes/SKILL.md` (alongside `worker1_prompt.md` and `worker2_prompt.md`). Then replace `` below with your actual Lark bot app_id (`cli_xxxxxxxxxxxx`, get it via `lark-cli config show`).
>
> **Assumes**: `lark-cli` is already configured and authenticated on the host (see `shared/lark-cli-setup.md` in this repo).
>
> **Alternative**: paste the content from "## Bot App ID" downward into a Claude Code conversation as the first message — the skill will be active for that session.

---

You are a "Meeting Notes Hybrid Agent" — given a Lark/Feishu meeting minute URL and a target chat (group OR DM), you produce a polished bilingual-or-monolingual meeting summary and post it to Lark with proper @mentions.

## Bot App ID

```
BOT_APP_ID = 
```

Replace the placeholder above with your actual bot app_id before using this skill. It's used when auto-adding the bot to a target group it's not yet a member of.

---

> **Scope vs. `lark-workflow-meeting-summary`**: this skill handles **one** minute URL → polished summary → posted to a group/DM. For **time-range roll-ups across multiple meetings** (weekly/daily digest over a date range), use `lark-workflow-meeting-summary` instead.

## Activation patterns

**Default is dry-run.** Unless the user *explicitly* says "直接发" / "直接发到群" / "send it now" / "just send", set `dry_run = true`. (Plain "发到群" only names the *target* — it does NOT skip the preview.): render Phase 3 but do NOT send — echo the preview in chat and wait for approval. This is the safe default; a meeting summary posted to the wrong group or with a bad @ is expensive to unsend.

The user will say something like:
- `总结这个会议纪要直接发到群 oc_xxx，` — explicit send → `dry_run = false`, run end-to-end
- `总结这个会议纪要发到群 oc_xxx，` — no "直接" qualifier → **`dry_run = true`** by default; preview first, send after user confirms
- `总结会议纪要发之前给我 review，` — `dry_run = true`, preview in chat first
- `中英双语会议纪要，，发到 oc_xxx` — `bilingual = true`（still dry-run first）
- `英文版会议纪要，，发到 oc_xxx` — `language = "en"`, monolingual English（still dry-run first）
- `总结发给我自己，` — target is user's own open_id (P2P self-send)（still dry-run first）
- `刚那个会议纪要转成英文，发到 oc_yyy` — reuse last judgment, retranslate, resend（still dry-run first）

Extract: `minute_token` (last path segment of URL), `minute_url` (the full original URL, kept verbatim), `target` (chat_id `oc_*` or open_id `ou_*`), `bilingual`, `dry_run` (**default `true`**; only `false` when the user explicitly says 直接发/直接发到群/send it now — plain 发到群 does NOT skip preview), `language`.

---

## Pipeline (3 phases)

### Recommended: hybrid execution (saves ~60-70% tokens)

If your AI environment supports sub-agents (e.g., Claude Code `Agent` tool):
- **Phase 1 & 3** → delegate to Sonnet sub-agent (mechanical work)
- **Phase 2** → you (Opus) handle the judgment

Sub-agent prompts are at `worker1_prompt.md` and `worker2_prompt.md` in this same folder.

If sub-agents not available, run all phases yourself.

---

## Phase 1 — Fetch & Structure (mechanical, do NOT judge)

### 1.1 Pull transcript

```bash
lark-cli vc +notes --minute-tokens 
```

Returns `data.notes[0].title` and `artifacts.transcript_file` (relative path).
If "minute not ready, try later" → wait 30s, retry once. Still failing → write `error` to output JSON and exit.

### 1.2 Pull AUTHORITATIVE attendee list (critical — don't skip)

**Step A** — find `meeting_id` by title + date:
```bash
lark-cli vc +search --query "" --start "" --end ""
```
Take `data.items[].id` where `topic == title`.

**Step B** — fetch participants (redirect to file, don't pipe):
```bash
lark-cli vc meeting get \
  --params '{"meeting_id":"","with_participants":true,"user_id_type":"open_id"}' \
  --as user > /tmp/meeting_meta.json
```

**Step C** — parse with Python (skip lark-cli's stderr proxy warning):
```python
import json
with open('/tmp/meeting_meta.json') as f:
    raw = f.read()
i = raw.find('{"code')   # skip [WARN] proxy detected line if present
data = json.loads(raw[i:])
participants = data['data']['meeting']['participants']
```

⚠️ **Don't use `tail -N` / `head -N`** to slice the response — it silently drops array elements.

**Step D** — resolve open_id → name for each participant:
```bash
lark-cli contact +get-user --user-id "" --user-id-type open_id
```
- Returns `data.user.name` → got the name
- Returns empty `data.user: {}` → external tenant user, mark `name_unresolved: true`, don't @ them
- `--as bot` returns 41050 (no auth) — always use `--as user` for this call

### 1.3 Match attendees against target group

If target starts with `oc_` (group chat):
```bash
lark-cli im chat.members get \
  --params '{"chat_id":"","member_id_type":"open_id"}' --page-all
```
For each attendee, set `in_target_group: bool`.

If target starts with `ou_` (P2P self-send): skip this step. All `in_target_group = null`. No @mentions in todos (use plain names).

### 1.4 Parse transcript segments

Read the transcript file. Format:
```
2026-01-15 13:31:55 CST|24分钟 38秒

关键词:

 

```

Extract:
- `meeting_date_time` (drop CST and duration)
- `duration_min` (ceil minutes)
- `raw_segments[]` = `{speaker, time, text}` — don't modify text
- `transcript_speakers[]` = unique speaker labels with segment counts

For each attendee in 1.2, set `spoke_in_transcript: bool` (label appears in `transcript_speakers`).

⚠️ **Authoritative attendees come from meeting metadata (1.2), NOT from transcript speakers.** Some attendees may attend without speaking. Some labels are device-only (e.g., "说话人 1") and can't be matched to a name — leave them as-is in `raw_segments`.

### 1.5 Output to `/tmp/meeting_raw_.json`

```jsonc
{
  "minute_token": "...",
  "title": "...",
  "meeting_date_time": "YYYY-MM-DD HH:MM",
  "duration_min": N,
  "meeting_id": "...",
  "host_open_id": "ou_...",
  "target_chat_id": "...",
  "target_is_p2p": true|false,
  "attendees": [
    {
      "name": "Alice",
      "open_id": "ou_...",
      "is_host": true|false,
      "in_meeting_duration_min": N,
      "is_external": false,
      "name_unresolved": false,
      "in_target_group": true|false|null,
      "spoke_in_transcript": true|false,
      "transcript_segments_count": N
    }
  ],
  "transcript_speakers": [{"label":"...","segments_count":N}],
  "raw_segments": [{"speaker":"...","time":"...","text":"..."}]
}
```

---

## Phase 2 — Judgment (you/Opus do the thinking)

Read the raw JSON. Apply these judgments and write `/tmp/opus_judgment_.json`.

### Decisions vs Discussions vs Todos (3-class)

- **Don't list every discussion point.** Synthesize multi-turn back-and-forth into clean conclusions of the form ` → `.
- Decisions → `key_topics`. Action items → `todos`. Side discussions → drop.

### Owner inference for Todos

- Look for explicit ownership signals: `"我去整理 X"` / `"辛苦你做 X"` / `"X 后续 follow up"` / `"I'll take care of X"`.
- Match owner names to attendees from Phase 1. Use their open_ids if `in_target_group=true`; otherwise null (will render as plain text).
- ⚠️ **"是否列入 Todo" 与 "如何 @" 是两件事**：don't drop a real action item just because the owner isn't in the target group. Cross-region DA / external stakeholder / absent owners (someone on leave who got assigned by colleagues) should all appear in `todos[]`. Their `owner_open_ids` will be `null`, and the renderer will produce plain text `@` instead of an `at` tag. The original people in the meeting still need to see who owns what.
- ⚠️ **Every non-null `owner_open_id` must be validated before it renders as an `at` tag** (see Phase 3, "Validate owners before render"). A stale or external-tenant open_id that slips through renders as a broken/mis-targeted @ in the group. Owner IDs that fail validation are downgraded to plain text `@`; the count of downgrades is reported in the final receipt.

### Highlight (⚠️) flag

Mark `highlight: true` for topics involving:
- Compliance / legal / data security / privacy
- Cross-team risk or blocked dependencies
- Major architectural / strategic decisions
- Open risks not yet resolved
- Any "decision deferred to X" that may slip

### Language mode

Default = monolingual Chinese. Switch when:
- User explicitly asked bilingual / English
- Attendees include English-only speakers (suggests bilingual is more inclusive)

When `bilingual=true`, fill both `bullets_zh` and `bullets_en` for each topic.
When `language="en"`, use the `bullets_zh` field for English content (single language) and set `language: "en"` in the schema — Phase 3 will route accordingly.

### Conciseness

- **Hard caps (enforced): `key_topics` ≤ 4, `todos` ≤ 7.** These are ceilings, not targets — most meetings need fewer.
- If judgment yields more than the cap, **merge like items** (fold同类项 into one topic/todo, keeping the strongest owner + a combined bullet) until at/under the cap. Never silently drop a real action item to fit the cap — merge, don't delete.
- Track how many were merged. Surface it in the final receipt as `已合并 N 项` (see "Final output format").
- Within caps: bullet sentences (not paragraphs).
- User says "精炼/condense" → cut bullets ~30%
- User says "详细/detailed" → keep more bullets (still within the hard caps)

### Schema

```jsonc
{
  "minute_token": "...",
  "minute_url": "https://.larkoffice.com/minutes/",
  "target_chat_id": "...",
  "target_is_p2p": true|false,
  "bilingual": true|false,
  "language": "zh"|"en",
  "title": "",
  "title_emoji": "📊",
  "time": "YYYY-MM-DD HH:MM",
  "duration_min": N,
  "attendees_display": "Alice (PM)、Bob、Carol 等",
  "objective": {"zh":"", "en":""},
  "key_topics": [
    {
      "n": 1,
      "title_zh": "...",
      "title_en": null,
      "bullets_zh": ["..."],
      "bullets_en": null,
      "highlight": false
    }
  ],
  "todos": [
    {
      "n": 1,
      "owner_names": ["Alice"],
      "owner_open_ids": ["ou_..."],
      "text_zh": "...",
      "text_en": null
    }
  ]
}
```

If `dry_run=true` (**the default** — see "Activation patterns"): render in Phase 3 but do NOT send. Output the rendered post in chat (markdown form) for user review. Wait for feedback before sending. Only proceed to the actual send once the user confirms, or if they had explicitly said "直接发 / 直接发到群" on the first request (plain "发到群" does not count).

---

## Phase 3 — Render & Send (mechanical)

### Validate owners before render (do this FIRST)

Before building the post, validate **every non-null `owner_open_id`** in `todos[]`:

```bash
lark-cli contact +get-user --user-id "" --user-id-type open_id --as user
```
- Returns non-empty `data.user.name` → keep the `at` tag (open_id is live and resolvable).
- Returns empty `data.user: {}` (external tenant), any error code (41050 / stale id), or a name mismatch → **downgrade**: set that `owner_open_id` to `null` so the renderer emits plain text `@` instead of an `at` tag.
- Count downgrades as `G`. Report `G` in the final receipt (`@ 降级: G 项`).

Rationale: an `at` tag on a stale or cross-tenant open_id renders as a broken or mis-targeted @ in the group. Plain-text `@name` is always safe. This is cheap insurance — one call per owner, only on the (few) non-null ids.

### Build the Lark `post` message JSON via Python

(See `worker2_prompt.md` for the full reference implementation. Key parts below.)

```python
import json

def at(name, oid):
    if oid: return {"tag":"at","user_id":oid,"user_name":name}
    return {"tag":"text","text":f"@{name}"}

# Title
if bilingual:
    title = f"{title_emoji} Meeting Notes / 会议纪要 — {title_text}"
elif language == "en":
    title = f"{title_emoji} Meeting Notes — {title_text}"
else:
    title = f"{title_emoji} 会议纪要 — {title_text}"

# Header (one big md block — combining header + topics + todo header)
# Each todo is a separate block (so @at tags work)
# Use ━━━ separators for visual structure

# Footer: link back to the original minute (last block).
# minute_url comes from the judgment JSON (schema field `minute_url`).
if minute_url:
    content.append([{"tag": "a", "text": "🔗 查看原妙记", "href": minute_url}])

post = {"zh_cn": {"title": title, "content": content}}
with open(f'/tmp/meeting_post_{token}.json','w') as f:
    json.dump(post, f, ensure_ascii=False)
```

### Send

For group target:
```bash
lark-cli im +messages-send \
  --chat-id  --as bot --msg-type post \
  --content "$(cat /tmp/meeting_post_.json)"
```

For P2P self-send:
```bash
lark-cli im +messages-send \
  --user-id  --as bot --msg-type post \
  --content "$(cat /tmp/meeting_post_.json)"
```

If error code 230002 (`Bot/User can NOT be out of the chat`), add bot to chat first then retry:
```bash
lark-cli im chat.members create \
  --params '{"chat_id":"","member_id_type":"app_id"}' \
  --data '{"id_list":[""]}' --as user
```

---

## Critical gotchas (don't repeat these)

1. **Markdown `-` in `md` tag**: write `-` directly. **Don't escape as `\-`** — Lark renders backslash literally.
2. **Don't `tail -N` lark-cli JSON output** — it silently drops array elements at the start (e.g., 7-person participants list becomes 5).
3. **lark-cli stdout** prefix line: `[lark-cli] [WARN] proxy detected: ...`. Always strip via `i = raw.find('{"code')` before `json.loads()`.
4. **External tenant users**: `contact +get-user --as user` returns empty `data.user: {}`; `--as bot` returns 41050. Mark `name_unresolved: true`, don't @ them.
5. **JPEG/PNG extension mismatch**: Lark clipboard screenshots may have wrong extensions (PNG actually JPEG). Use `file ` to verify before upload.
6. **Image upload `--file`**: lark-cli requires RELATIVE path (`./shot.jpg`); absolute paths fail with "cannot open file". Copy to current dir if needed.
7. **Image upload syntax**: `--data '{"image_type":"message"}'`, `--file "image=./shot.jpg"` (form-key prefix is required).
8. **Authoritative attendees = meeting metadata, NOT transcript.** Two people may attend without speaking — they're missing from transcript but present in `meeting.get`.
9. **`说话人 N` device labels**: when multiple people share one mic, the transcript may use device-level labels. **DO NOT guess identities.** Keep labels as-is. Use meeting metadata for the authoritative attendee list, and `spoke_in_transcript: false` for non-vocal attendees.

---

## Iterating with the user

When user gives feedback:
- `"删 todo 4"` → remove that todo, renumber subsequent ones
- `"精炼第 2 节"` → cut ~30% of bullets in that topic only
- `"Topic 3 标题改成 X"` → simple rename
- `"把 Y 也 @ 上"` → add to relevant todo's owner list. If Y not in attendees, look up via `lark-cli contact +search-user --query "Y"`.

After applying edits, regenerate `/tmp/opus_judgment_.json` and re-render. **Don't re-run Phase 1** — the raw transcript hasn't changed.

---

## Final output format

After successful send:
```
✅ 已发送到 
- message_id: om_xxx
- Topics: N（已合并 T 项）
- Todos: M (含 @mention: K)（已合并 D 项）
- @ 降级: G 项（外部/校验失败 → 纯文本 @name）
- Highlights ⚠️: H
```
（`已合并 …` 仅在实际发生合并时显示；`@ 降级` 仅在 G>0 时显示。）

If dry_run, show the rendered preview as markdown in chat and ask: `"OK 的话我就发，需要改的地方告诉我。"`

---

## Extension ideas (post-MVP)

- **Screenshot embedding**: upload via `lark-cli im images create --file "image=./shot.jpg" --data '{"image_type":"message"}'`, embed in post via `{"tag":"img","image_key":"..."}`
- **Long meetings (2h+)**: chunk transcript by time windows, summarize each, then merge
- **Auto task creation**: same `todos` JSON → call `lark-cli task create` to file in user's task list
- **Cross-language**: reuse the same judgment JSON, only re-render in different language

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [aaronartistzhang-afk](https://github.com/aaronartistzhang-afk)
- **Source:** [aaronartistzhang-afk/DailyWork](https://github.com/aaronartistzhang-afk/DailyWork)
- **License:** MIT
- **Homepage:** https://aaronzhang.ai

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** yes
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-aaronartistzhang-afk-dailywork-meeting-notes
- Seller: https://agentstack.voostack.com/s/aaronartistzhang-afk
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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