x-dm-auto-chat

Files (10)

• SKILL.md
• check-composer.py · scripts/
• check-page-state.py · scripts/
• fetch-inbox-api.py · scripts/
• open-conversation-by-user.py · scripts/
• read-conversation.py · scripts/
• scan-inbox-merged.py · scripts/
• scroll-load-history.py · scripts/
• search-users.py · scripts/
• verify-sent.py · scripts/

SKILL.md (first lines)

# X (Twitter) — DM Auto Chat (End-to-End)

> Full X DM automation Skill: inbox scan → conversation read → persona-based reply → send; also supports search-and-outreach. The calling Agent generates reply text based on persona; this Skill handles all mechanical operations.

## Language

All process output to user (progress updates, process notifications) follows the user's language.

## Objective

Encapsulate "refresh DM list → identify pending replies → read context → reply with persona → send" and "search user → enter chat → send first message" into callable end-to-end capabilities.

## Prerequisites

- Browser is open at X site, logged into X account (`[aria-label="Account menu"]` present)
- The 4-digit DM passcode for the current account is available (required for E2E encryption)
- Caller has prepared a "persona description" (used to generate replies), e.g.:
  - `"You are BrowserAct outreach team. Tone: friendly, concise, professional. Goal: invite creators to collaborate."`
- Optional: list of target user search queries (for outreach scenario)

## Pre-execution Checks

### 1. Tool Readiness

If browser-act has been confirmed available in the current session → skip.

Invoke `browser-act` via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

### 2. Open DM Entry + Comprehensive State Check

```
browser-act --session <name> navigate https://x.com/i/chat
browser-act --session <name> wait stable --timeout 15000
browser-act --session <name> eval "$(python scripts/check-page-state.py)"
```

Return format:
```json
{
  "url": "https://x.com/i/chat/pin/recovery?from=%2Fi%2Fchat",
  "logged_in": true,
  "need_passcode": true,
  "on_inbox": false,
  "on_conversation": false,
  "has_panel": false,
  "has_composer": false,
  "inbox_count": 0
}
```

Decision matrix:
- `logged_in: false` → inform user to log in first; wait; retry this step
- `need_passcode: true` → proceed to step 3 below
- `on_inbox: true` and `inbox_count > 0` → ready, enter business flow
- `on_inbox: true` but `inbox_count === 0` → account has no DM conversations; outreach scenario can still proceed, pending-reply scenario has nothing to do

### 3. DM Passcode Unlock (when need_passcode is true)

1. If caller has provided passcode in advance → use it directly; otherwise ask user for 4-digit DM passcode via **AskUserQuestion tool** (do not use plain text prompt — must call AskUserQuestion)
2. `browser-act --session <name> state` — find indexes of 4 `<input maxlength=1 pattern=[0-9]*>` elements (usually 4 consecutive)
3. Enter each digit: `browser-act --session <name> input <idx1> "<d1>"`, `<idx2> "<d2>"`, `<idx3> "<d3>"`, `<idx4> "<d4>"`
   - **Must use `browser-act input` (CDP real keyboard events), cannot use eval to set value** — X ignores non-real keyboard input
4. `browser-act --session <name> wait stable --timeout 10000`
5. Re-run `check-page-state.py`, confirm `need_passcode: false` and `on_inbox: true`
6. 3 consecutive failures still showing `need_passcode: true` → inform user passcode may be wrong; terminate

## Business Flows

> Choose Scenario A, Scenario B, or both. Each scenario is an ordered AI Workflow (not a single JS).

### Scenario A: Scan unread DMs → Persona-based reply

**Flow**: `Scan inbox → Filter unread & latest peer messages → Per-conversation: read context → Generate reply with persona → Send → Next`

**Steps**:

1. **Scan inbox**:
   ```
   browser-act --session <name> eval "$(python scripts/scan-inbox-merged.py)"
   ```
   Returns `items[]`, each containing `conversation_id` / `conversation_url` / `peer_screen_name` / `peer_display_name` / `peer_can_dm` / `latest_message_preview` / `latest_message_from_self` / `unread`, etc.

2. **Filter pending-reply conversations**: from `items`, select conversations meeting all conditions:
   - `unread === true` (has unread) **or** `latest_message_from_self === false` (peer's latest message not yet replied)
   - `peer_can_dm === true` (recipient allows DM)
   - `is_muted !== true` and `is_deleted_by_viewer !== true`
   - Optional caller filters: only reply to specific screen_names, exclude already-replied (use external JSONL ledger)

3. **For each pending-reply conversation** (strictly serial, **random `sleep 8-15` seconds between each**):

   a. **Open conversation**:
      ```
      browser-act --session <name> navigate https://x.com<conversation_url>
      browser-act --session <name> wait stable --timeout 15000
      ```

   b. **If passcode re-triggered** → re-unlock (usually won't re-trigger within same session)

   c. **Read context**:
      ```
      browser-act --session <name> eval "$(python scripts/read-conversation.py)"
      ```
      Returns `messages[]`, each with `direction` (self/peer), `text`, `timestamp_text`, `links`, `images`.

   d. **(Optional) Load full history**: If caller needs longer context, loop:
      ```
      browser-act --session <name> eval "$(python scripts/scroll-load-history.py)"
      ```
      Until `reached_top: true`, then re-read with `read-conversation.py`.

   e. **Generate reply**: **Calling Agent combines persona, message history to generate reply text.** Reply content is entirely the caller's decision; this Skill does not participate in generation. Suggested inputs:
      - Persona prompt (provided by caller)
      - Recent N messages (typically `messages.slice(-6)`)
      - Peer name (`peer_display_name` / `peer_screen_name`) for address
      - Return one string `reply_text`, length < 10,000 characters

   f. **Send reply**:
      1. `browser-act --session <name> eval "$(python scripts/check-composer.py)"` → record `last_message_id`
      2. `browser-act --session <name> state` — find `<textarea placeholder=Message>` index `TA_IDX`

From browser-act/skills, MIT.

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