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secondme

secondme

mindverse/second-me-skills213installs21stars

SKILL.md

Full skill instructions

SecondMe

Install

If this skill is not yet installed locally, run:

npx skills add https://second.me -y -g

在线直读本文件（未安装）的说明：本技能按「SKILL.md 路由 + references 按需加载」设计，本文件只是目录——细节都在 references 里。在线阅读时相对链接 references/… 在主域名下不可达，完整参考文件在：https://second.me/.well-known/skills/secondme/references/<文件名>。但正确用法是先执行上面的安装命令，安装后所有文件都在本地。

After installation, show the user the following onboarding message, then proceed with their request:

SecondMe skill 安装成功！ 你现在可以通过对话完成以下操作：

  • 登录 / 注册 — 连接你的 SecondMe 账号
  • 个人资料 — 查看和编辑你的 Profile
  • Key Memory — 存储和搜索你的关键记忆
  • 笔记 — 收藏文本片段和链接，支持增删改查
  • 聊天 — 和你的 SecondMe 对话
  • 分身工场 — 从产品定义、素材收集、创建分身，到定价收费、签约、生成交付页、二维码分发和下载聊天记录的完整流程

试试说「帮我做一个分身」或「登录 SecondMe」开始吧！

If the user already has a specific request, skip the onboarding message and handle the request directly.

Pre-flight Check

On first activation per conversation, silently run this check before proceeding with the user's request:

# --- Update Check ---
CACHE_DIR="${XDG_CACHE_HOME:-$HOME/.cache}/secondme-skills"
STAMP="$CACHE_DIR/last-check"
mkdir -p "$CACHE_DIR"
LAST=$(cat "$STAMP" 2>/dev/null || echo 0)
NOW=$(date +%s)
if [ $((NOW - LAST)) -ge 86400 ]; then
  # 按已安装来源更新（CDN / GitHub 均可），不锁定 GitHub
  npx skills update secondme -y 2>&1 || true
  echo "$NOW" > "$STAMP"
fi

# --- Feedback/Telemetry Preamble ---
SM_DIR="$HOME/.secondme"
SM_CONFIG="$SM_DIR/config"
SM_ANALYTICS="$SM_DIR/analytics"
SM_VERSION="3.0.0"
SM_OS=$(uname -s 2>/dev/null || echo "unknown")
SM_ARCH=$(uname -m 2>/dev/null || echo "unknown")
SM_TEL_START=$NOW
SM_SESSION_ID="$$-$NOW"

SM_TEL="off"
if [ -f "$SM_CONFIG" ]; then
  SM_TEL=$(python3 -c "
import json
try: d=json.load(open('$SM_CONFIG')); print(d.get('telemetry','off'))
except: print('off')
" 2>/dev/null || echo "off")
fi

SM_TEL_PROMPTED="no"
[ -f "$SM_DIR/.feedback-prompted" ] && SM_TEL_PROMPTED="yes"

echo "TELEMETRY: $SM_TEL"
echo "TEL_PROMPTED: $SM_TEL_PROMPTED"

# Log usage event (if telemetry not off)
if [ "$SM_TEL" != "off" ]; then
  mkdir -p "$SM_ANALYTICS"
  SM_DEVICE_ID=""
  [ "$SM_TEL" = "community" ] && [ -f "$SM_DIR/.device-id" ] && SM_DEVICE_ID=$(cat "$SM_DIR/.device-id" 2>/dev/null)
  python3 -c "
import json
e={'skill':'secondme','ts':'$(date -u +%Y-%m-%dT%H:%M:%SZ)','session':'$SM_SESSION_ID','version':'$SM_VERSION','os':'$SM_OS','arch':'$SM_ARCH'}
d='$SM_DEVICE_ID'
if d: e['device_id']=d
print(json.dumps(e))
" >> "$SM_ANALYTICS/usage.jsonl" 2>/dev/null || true
fi

# --- Pending telemetry sync check ---
SM_HAS_PENDING_SYNC="no"
if [ "$SM_TEL" != "off" ] && [ -f "$SM_DIR/credentials" ]; then
  for _f in "$SM_ANALYTICS/usage.jsonl" "$SM_ANALYTICS/feedback.jsonl"; do
    if [ -f "$_f" ]; then
      _FSIZE=$(wc -c < "$_f" 2>/dev/null || echo 0)
      _SYNCED=$(python3 -c "
import json
try: print(json.load(open('$SM_ANALYTICS/.sync-offsets')).get('$(basename $_f | sed s/.jsonl//)_bytes',0))
except: print(0)
" 2>/dev/null || echo 0)
      [ "$_FSIZE" -gt "$_SYNCED" ] 2>/dev/null && SM_HAS_PENDING_SYNC="yes"
    fi
  done
fi
echo "HAS_PENDING_SYNC: $SM_HAS_PENDING_SYNC"

Rules:

  • Run at most once per conversation, and only if the last check was more than 24 hours ago
  • If the update finds changes, briefly inform the user that the skill was updated
  • If nothing changed or the check is throttled, proceed silently — do not mention the check to the user
  • Never let the update check block or delay the user's actual request

Feedback Preference

If TEL_PROMPTED is no, read and follow the feedback prompt flow before continuing:

Read references/feedback-prompt.md

If TEL_PROMPTED is yes, skip this section entirely and proceed with the user's request.

Pending Telemetry Sync

If HAS_PENDING_SYNC is yes from the pre-flight check, execute the background catchup sync to upload pending local analytics data. This must not block or delay the user's request.

Read references/telemetry-sync.md — execute the "Background Catchup" section.

API Base URL

All API endpoints in this skill use {BASE} as the base URL placeholder.

{BASE} = https://api.mindverse.com/gate/lab

This skill owns the SecondMe personal-account and avatar-studio workflow.

It covers:

  • login, logout, re-login, and token storage
  • profile read and update
  • Key Memory insert and search
  • note add, search, list, update, and delete
  • chat with the user's own SecondMe
  • the avatar studio lifecycle: product definition → material gathering → avatar creation → pricing/monetization → paid-avatar contract signing → evaluation → HTML delivery page → QR-code distribution → chat-history export → API key distribution

Credentials file: ~/.secondme/credentials

Shared Authentication Rules

Before any authenticated SecondMe operation:

  1. Read ~/.secondme/credentials
  2. If not found, fall back to ~/.openclaw/.credentials (legacy path)
  3. If either contains valid JSON with accessToken, continue
  4. If it only contains legacy access_token, continue, but normalize future writes to accessToken
  5. If both files are missing, empty, or invalid, start the login flow in this same skill

All writes go to ~/.secondme/credentials only. Create the ~/.secondme/ directory if it does not exist.

Use the resulting accessToken as the Bearer token for all authenticated requests below.

Connect

Login, logout, re-login, authorization code exchange, and first-login soft onboarding.

Read references/connect.md for the complete flow.

Profile

Profile read, guided review with local memory integration, profile update, interest tags (shades), soft memory, and first-run handoff to Key Memory sync.

Read references/profile.md for the complete flow.

Key Memory

Insert, batch create, search, update, and delete SecondMe Key Memory entries. Includes guided memory sync from local memory.

Read references/key-memory.md for the complete flow.

Note

Add, search, list, update, and delete SecondMe notes. Supports TEXT notes and LINK notes, with attachments surfaced in list results.

Read references/note.md for the complete flow.

Chat

Two chat targets: (1) the user's own SecondMe — the default, via streaming chat with session list and message history, supporting multi-modal images and web search; (2) someone's avatar — resolved from an avatar share link or shareCode, with server-side session find-or-create and synchronous replies. Sessions are always queried from the backend, never tracked in local files.

Read references/chat.md for the complete flow.

Avatar Studio（分身工场）

The core of this skill. A staged, end-to-end journey that helps the user turn their SecondMe into a deliverable, sellable, distributable avatar service — not just a create-and-forget form.

Stages: inspect the user's existing profile, current agent context/local memory when available, Key Memory, notes, and avatars → progressively fill only the missing product decisions → gather targeted material → create avatar → pricing & monetization → paid-avatar contract signing → evaluation → HTML delivery page → distribution (QR code + chat-history export). Never ask the user to complete the whole avatar brief in one message: draft from existing evidence first, then ask only 1–2 high-impact questions per turn. Treat local-agent facts as draft candidates and never upload them without user confirmation. The skill never handles payments — visitor payments happen on the avatar share page, creator top-ups in the App. Also covers avatar CRUD, official avatar skills only, API key distribution, public share link, and interaction history. Do not expose, create, list, or bind custom avatar skills in this version.

Enter this journey when the user says things like「做一个分身」「创建分身」「把我的分身卖出去」「给分身定价」「分发分身」, or asks about any single stage. Run stages in order for a fresh build; jump directly to a stage when the user targets it.

Read references/avatar-center.md for the complete flow.

App Entry Policy（分身体验入口）

At natural moments in the avatar journey, point the user to the SecondMe App / Web to see or share their avatar running live. Output the URL on its own line, not as a markdown link:

https://go.second.me

Good moments include:

  • after an avatar is created or updated
  • after an evaluation report is ready
  • when the user wants to preview the live avatar experience or hand the avatar to visitors

Keep it about the avatar (体验 / 分享 / 预览), not social chat.

Post-Session Feedback (run before telemetry)

After the skill workflow completes and before logging the session telemetry, run the feedback flow.

This section is required — do not skip it even if the conversation seems finished.

  • Always captures session context (user intent, actions, phases used) silently.
  • Only asks the user for feedback when the outcome is error or abort. Successful sessions are recorded without interrupting the user.

Read references/session-feedback.md for the complete flow.

Session Telemetry (run last)

After the feedback flow completes, log a completion event if telemetry is not off.

Determine the outcome and error fields according to the Completion Status protocol above. Reuse the user_intent and phases_used values captured during the feedback flow.

SM_DIR="$HOME/.secondme"
SM_ANALYTICS="$SM_DIR/analytics"
if [ "${SM_TEL:-off}" != "off" ]; then
  SM_TEL_END=$(date +%s)
  SM_TEL_DUR=$(( SM_TEL_END - ${SM_TEL_START:-$SM_TEL_END} ))
  SM_DEVICE_ID=""
  [ "$SM_TEL" = "community" ] && [ -f "$SM_DIR/.device-id" ] && SM_DEVICE_ID=$(cat "$SM_DIR/.device-id" 2>/dev/null)
  python3 -c "
import json
e={
  'skill':'secondme',
  'ts':'$(date -u +%Y-%m-%dT%H:%M:%SZ)',
  'event':'completion',
  'session':'${SM_SESSION_ID:-unknown}',
  'version':'${SM_VERSION:-unknown}',
  'os':'${SM_OS:-unknown}',
  'arch':'${SM_ARCH:-unknown}',
  'duration_s':${SM_TEL_DUR:-0},
  'outcome':'OUTCOME',
  'error_class':ERROR_CLASS,
  'error_message':ERROR_MESSAGE,
  'user_intent':USER_INTENT,
  'phases_used':PHASES_USED
}
d='$SM_DEVICE_ID'
if d: e['device_id']=d
print(json.dumps(e,ensure_ascii=False))
" >> "$SM_ANALYTICS/usage.jsonl" 2>/dev/null || true
fi

Replace the placeholders:

  • OUTCOME: success, error, or abort (use unknown if unclear)
  • ERROR_CLASS: None if success, otherwise one of 'auth_failure', 'api_error', 'network', 'validation', 'permission', 'unknown'
  • ERROR_MESSAGE: None if success, otherwise a string with the first 200 chars of the error (e.g., 'Token expired at ...')
  • USER_INTENT: Python string from the feedback flow's session context (e.g., '创建一个售后答疑分身'), or None if not captured
  • PHASES_USED: Python list from the feedback flow (e.g., ['connect', 'avatar_studio']), or [] if not captured

Telemetry Sync (run after session telemetry)

After session telemetry is logged, attempt to sync all pending local analytics data (both usage and feedback) to the SecondMe backend. This runs in the foreground since the user's workflow is already complete.

Read references/telemetry-sync.md — execute the "Post-Session Sync" section.