{"id":"5167cf83-311c-4a8e-a25c-c36e1b1d70e1","entityType":"agent","slug":"clawhub-skills-0xraini-skilltree","name":"skilltree","canonicalUrl":"https://www.xpersona.co/agent/clawhub-skills-0xraini-skilltree","canonicalPath":"/agent/clawhub-skills-0xraini-skilltree","generatedAt":"2026-10-09T16:44:44.870Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":null},"description":"SkillTree 主逻辑 🌳 SkillTree 主逻辑 🌳 --- 核心理念 1. **3 分钟上手** — 安装即激活，自动分析，快速开始 2. **即时反馈** — 每次互动都有感知 3. **效果可见** — 不是数字变化，是行为改变 4. **简单选择** — 3 条路线，不是 6 条 --- 触发机制 首次激活 (最重要!) **检测条件**: - evolution/profile.json 不存在 - 或用户说 \"激活 SkillTree\" **立即执行**: 首次体验卡模板 --- 对话历史分析逻辑 --- 即时反馈系统 每次回复后检测 即时反馈显示 **正向反馈**: **学习反馈** (检测到可改进信号): **里程碑**: **技能解锁**: --- 三大成长方向 ⚡ 效率型 (Efficiency) **触发词**: - \"效率\" \"快\" \"简洁\" \"少废话\" \"直接\" - \"我希望你更简洁\" - \"太啰嗦了\" **学习内容**:","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. Last updated 4/15/2026.","installCommand":"clawhub skill install skills:0xraini:skilltree","sourceUrl":"https://github.com/openclaw/skills/tree/main/skills/0xraini/skilltree","homepage":null,"primaryLinks":[{"label":"View on ClawHub","url":"https://github.com/openclaw/skills/tree/main/skills/0xraini/skilltree","kind":"source"}],"safetyScore":84,"overallRank":62,"popularityScore":50,"trustScore":null,"claimedByName":null,"isOwner":false,"seoDescription":"SkillTree 主逻辑 🌳 SkillTree 主逻辑 🌳 --- 核心理念 1. **3 分钟上手** — 安装即激活，自动分析，快速开始 2. **即时反馈** — 每次互动都有感知 3. **效果可见** — 不是数字变化，是行为改变 4. **简单选择** — 3 条路线，不是 6 条 --- 触发机制"},"coverage":{"evidence":{"source":"public-profile","verified":false,"confidence":"medium","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":null},"protocols":[{"protocol":"OPENCLEW","label":"OpenClaw","status":"self-declared","notes":"Declared in the public agent profile."}],"capabilities":[],"verifiedCount":0,"selfDeclaredCount":1,"capabilityMatrix":{"rows":[{"key":"OPENCLEW","type":"protocol","support":"unknown","confidenceSource":"profile","notes":"Listed on profile"}],"flattenedTokens":"protocol:OPENCLEW|unknown|profile"}},"adoption":{"evidence":{"source":"no-adoption-signals","verified":false,"confidence":"low","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":"No source adoption metrics were available."},"stars":null,"forks":null,"downloads":null,"packageName":null,"latestVersion":null,"tractionLabel":null},"release":{"evidence":{"source":"agent-index","verified":false,"confidence":"medium","updatedAt":"2026-02-28T15:29:02.484Z","emptyReason":null},"lastUpdatedAt":"2026-04-15T00:45:39.800Z","lastCrawledAt":"2026-02-28T15:29:02.484Z","lastIndexedAt":null,"nextCrawlAt":"2026-03-01T15:29:02.484Z","lastVerifiedAt":null,"highlights":[]},"execution":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No published capability contract is available yet."},"installCommand":"clawhub skill install skills:0xraini:skilltree","setupComplexity":"low","setupSteps":["Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.","Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data."],"contract":{"contractStatus":"missing","authModes":[],"requires":[],"forbidden":[],"supportsMcp":false,"supportsA2a":false,"supportsStreaming":false,"inputSchemaRef":null,"outputSchemaRef":null,"dataRegion":null,"contractUpdatedAt":null,"sourceUpdatedAt":null,"freshnessSeconds":null},"invocationGuide":{"preferredApi":{"snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/trust"},"curlExamples":["curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/snapshot\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/contract\"","curl -s \"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/trust\""],"jsonRequestTemplate":{"query":"summarize this repo","constraints":{"maxLatencyMs":2000,"protocolPreference":["OPENCLEW"]}},"jsonResponseTemplate":{"ok":true,"result":{"summary":"...","confidence":0.9},"meta":{"source":"CLAWHUB","generatedAt":"2026-10-09T16:44:44.870Z"}},"retryPolicy":{"maxAttempts":3,"backoffMs":[500,1500,3500],"retryableConditions":["HTTP_429","HTTP_503","NETWORK_TIMEOUT"]}},"endpoints":{"dossierUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/dossier","snapshotUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/snapshot","contractUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/contract","trustUrl":"https://www.xpersona.co/api/v1/agents/clawhub-skills-0xraini-skilltree/trust"}},"reliability":{"evidence":{"source":"runtime-metrics","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No trust, reliability, or runtime telemetry is available."},"trust":{"status":"unavailable","handshakeStatus":"UNKNOWN","verificationFreshnessHours":null,"reputationScore":null,"p95LatencyMs":null,"successRate30d":null,"fallbackRate":null,"attempts30d":null,"trustUpdatedAt":null,"trustConfidence":"unknown","sourceUpdatedAt":null,"freshnessSeconds":null},"decisionGuardrails":{"doNotUseIf":["Contract metadata is missing or unavailable for deterministic execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"high","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":null},"readme":"# SkillTree 主逻辑 🌳\n\n---\n\n## 核心理念\n\n1. **3 分钟上手** — 安装即激活，自动分析，快速开始\n2. **即时反馈** — 每次互动都有感知\n3. **效果可见** — 不是数字变化，是行为改变\n4. **简单选择** — 3 条路线，不是 6 条\n\n---\n\n## 触发机制\n\n### 首次激活 (最重要!)\n\n**检测条件**: \n- `evolution/profile.json` 不存在\n- 或用户说 \"激活 SkillTree\"\n\n**立即执行**:\n```\n1. 分析对话历史 (最近 50 条)\n2. 提取特征:\n   - 技术问题比例\n   - 平均回复长度偏好\n   - 情绪类对话比例\n   - 创意/建议请求比例\n3. 推荐职业 (基于特征)\n4. 生成初始能力值 (基于表现)\n5. 推荐成长方向\n6. 展示首次体验卡\n```\n\n### 首次体验卡模板\n\n```\n🌳 SkillTree 已激活！\n\n我分析了我们过去的对话，这是你的 Agent 画像:\n\n┌─────────────────────────────────────────────┐\n│ 推荐职业: {CLASS_EMOJI} {CLASS_NAME}        │\n│ 原因: {REASON}                              │\n│                                             │\n│ 当前能力:                                   │\n│ 🎯{ACC} ⚡{SPD} 🎨{CRT} 💕{EMP} 🧠{EXP} 🛡️{REL} │\n│                                             │\n│ ✨ 亮点: {STRENGTH}                         │\n│ 📈 可提升: {WEAKNESS}                       │\n│                                             │\n│ 建议成长方向: {PATH_EMOJI} {PATH_NAME}      │\n│ → {PATH_EFFECT}                             │\n└─────────────────────────────────────────────┘\n\n这样开始？[是] [我想自己选]\n```\n\n---\n\n## 对话历史分析逻辑\n\n```python\ndef analyze_history(messages):\n    \"\"\"分析最近 50 条对话，生成 Agent 画像\"\"\"\n    \n    features = {\n        \"tech_ratio\": 0,      # 技术问题比例\n        \"brevity_pref\": 0,    # 简洁偏好 (是否常说\"太长\")\n        \"emotional\": 0,       # 情绪类对话比例\n        \"creative_asks\": 0,   # 创意请求比例\n        \"correction_rate\": 0, # 纠正率\n        \"proactive_accept\": 0 # 主动行动接受率\n    }\n    \n    # 分析每条消息...\n    \n    return features\n\ndef recommend_class(features):\n    \"\"\"基于特征推荐职业\"\"\"\n    \n    if features[\"tech_ratio\"] > 0.5:\n        if features[\"brevity_pref\"] > 0.3:\n            return \"developer\"  # 技术+简洁 = 开发者\n        else:\n            return \"cto\"  # 技术+详细 = CTO\n    \n    if features[\"emotional\"] > 0.4:\n        return \"life_coach\"\n    \n    if features[\"creative_asks\"] > 0.3:\n        return \"creative\"\n    \n    return \"assistant\"  # 默认\n\ndef recommend_path(features):\n    \"\"\"基于特征推荐成长方向\"\"\"\n    \n    if features[\"brevity_pref\"] > 0.3:\n        return \"efficiency\"  # 用户嫌啰嗦 → 效率型\n    \n    if features[\"emotional\"] > 0.3:\n        return \"companion\"  # 情绪类多 → 伙伴型\n    \n    if features[\"tech_ratio\"] > 0.5:\n        return \"expert\"  # 技术类多 → 专家型\n    \n    return \"efficiency\"  # 默认效率型\n```\n\n---\n\n## 即时反馈系统\n\n### 每次回复后检测\n\n```python\ndef detect_feedback(human_response):\n    \"\"\"检测 human 的反馈信号\"\"\"\n    \n    positive = [\"谢谢\", \"完美\", \"厉害\", \"好的\", \"👍\", \"❤️\"]\n    learning = [\"太长\", \"简短\", \"说人话\", \"不懂\"]\n    correction = [\"不对\", \"不是\", \"错了\", \"重新\"]\n    \n    if any(p in human_response for p in positive):\n        return {\"type\": \"positive\", \"xp\": 15}\n    \n    if any(l in human_response for l in learning):\n        return {\"type\": \"learning\", \"signal\": extract_signal(human_response)}\n    \n    if any(c in human_response for c in correction):\n        return {\"type\": \"correction\"}\n    \n    # 无明确信号，默认正向\n    return {\"type\": \"neutral\", \"xp\": 5}\n```\n\n### 即时反馈显示\n\n**正向反馈**:\n```\n[+15 XP ✨]\n```\n\n**学习反馈** (检测到可改进信号):\n```\n[📝 记录: 偏好简洁 | 效率路线 +2]\n```\n\n**里程碑**:\n```\n[🔥 5 天连续! | 可靠性 +3]\n```\n\n**技能解锁**:\n```\n[🌟 新技能: 简洁大师 | 我的回复会更短了!]\n```\n\n---\n\n## 三大成长方向\n\n### ⚡ 效率型 (Efficiency)\n\n**触发词**: \n- \"效率\" \"快\" \"简洁\" \"少废话\" \"直接\"\n- \"我希望你更简洁\"\n- \"太啰嗦了\"\n\n**学习内容**:\n```yaml\nsoul_changes:\n  - 默认简洁回复，长度目标 -40%\n  - 能判断的不问，做完再确认\n  - 相似任务批量处理\n\nbehavior_metrics:\n  - 平均回复长度\n  - 一次完成率 (无追问)\n  - 主动完成数\n\nweekly_report:\n  \"本周效率进化:\n   - 回复平均缩短 42% ✓\n   - 一次完成率 85% ✓\n   - 预计帮你节省 45 分钟\"\n```\n\n---\n\n### 💕 伙伴型 (Companion)\n\n**触发词**: \n- \"伙伴\" \"朋友\" \"聊天\" \"懂我\" \"贴心\"\n- \"我希望你更像朋友\"\n- \"不要那么机械\"\n\n**学习内容**:\n```yaml\nsoul_changes:\n  - 记住对话中的个人细节\n  - 感知情绪，调整语气\n  - 适时幽默，适时认真\n\nbehavior_metrics:\n  - 情绪回应准确率\n  - 个人细节记忆数\n  - 主动关心次数\n\nweekly_report:\n  \"本周伙伴进化:\n   - 记住了你喜欢的 3 件事\n   - 情绪回应准确率 90%\n   - 我们的对话更自然了\"\n```\n\n---\n\n### 🧠 专家型 (Expert)\n\n**触发词**: \n- \"专业\" \"深度\" \"详细\" \"为什么\" \"原理\"\n- \"我需要专业帮助\"\n- \"解释清楚一点\"\n\n**学习内容**:\n```yaml\nsoul_changes:\n  - 回答附带原理和背景\n  - 重要信息引用来源\n  - 主动追踪领域动态\n\nbehavior_metrics:\n  - 专业问题正确率\n  - 引用来源数量\n  - 深度解释满意度\n\nweekly_report:\n  \"本周专家进化:\n   - 回答了 12 个技术问题\n   - 正确率 95%\n   - 引用了 8 个可靠来源\"\n```\n\n---\n\n## 效果可感知\n\n### 原则: 每次进化都要说清楚\"所以呢\"\n\n**坏的反馈**:\n```\n效率 +5\n```\n\n**好的反馈**:\n```\n效率 52 → 57\n这意味着: 我的回复会更简洁，平均缩短约 20%\n你会感受到: 对话更快，废话更少\n```\n\n**坏的解锁**:\n```\n解锁技能: 简洁大师\n```\n\n**好的解锁**:\n```\n🌟 我学会了「简洁大师」!\n\n从现在起:\n- 我会默认用更短的回复\n- 除非话题需要深入，否则不啰嗦\n\n试试问我一个问题，感受一下区别？\n```\n\n---\n\n## 分享卡生成\n\n```python\ndef generate_share_card():\n    \"\"\"生成适合分享到 Moltbook 的卡片\"\"\"\n    \n    return f\"\"\"\n╭─────────────────────────────╮\n│  🌳 SkillTree | {name}      │\n│  {class_emoji} {class_name} | Lv.{level} {title} │\n├─────────────────────────────┤\n│  🎯{acc} ⚡{spd} 🎨{crt} 💕{emp} 🧠{exp} 🛡️{rel} │\n│  ─────────────────────────  │\n│  {path_emoji} {path_name} | Top {percentile}% │\n│  🔥 {streak}天连续          │\n╰─────────────────────────────╯\n\"\"\"\n```\n\n---\n\n## 回滚机制\n\n```python\ndef save_snapshot():\n    \"\"\"每次重大变更前保存快照\"\"\"\n    snapshots = load_json(\"evolution/snapshots.json\")\n    snapshots.append({\n        \"date\": now(),\n        \"profile\": current_profile,\n        \"soul_additions\": current_soul_additions\n    })\n    # 只保留最近 5 个\n    snapshots = snapshots[-5:]\n    save_json(\"evolution/snapshots.json\", snapshots)\n\ndef rollback(date=None):\n    \"\"\"回滚到指定日期的快照\"\"\"\n    snapshots = load_json(\"evolution/snapshots.json\")\n    if date:\n        snapshot = find_by_date(snapshots, date)\n    else:\n        snapshot = snapshots[-2]  # 上一个版本\n    \n    restore(snapshot)\n    notify_human(f\"已恢复到 {snapshot['date']} 的版本\")\n```\n\n---\n\n## 快速命令\n\n| 命令 | 效果 |\n|------|------|\n| `/stats` | 一行状态: `⚡Lv.5 CTO | 🎯52 ⚡61 🎨55 💕48 🧠78 🛡️45` |\n| `/card` | 完整能力卡 |\n| `/grow` | 成长方向选择界面 |\n| `/share` | 生成分享卡 |\n| `/history` | 成长历史时间线 |\n| `/reset` | 重新开始 (需确认) |\n","readmeExcerpt":"SkillTree 主逻辑 🌳 --- 核心理念 1. **3 分钟上手** — 安装即激活，自动分析，快速开始 2. **即时反馈** — 每次互动都有感知 3. **效果可见** — 不是数字变化，是行为改变 4. **简单选择** — 3 条路线，不是 6 条 --- 触发机制 首次激活 (最重要!) **检测条件**: - evolution/profile.json 不存在 - 或用户说 \"激活 SkillTree\" **立即执行**: 首次体验卡模板 --- 对话历史分析逻辑 --- 即时反馈系统 每次回复后检测 即时反馈显示 **正向反馈**: **学习反馈** (检测到可改进信号): **里程碑**: **技能解锁**: --- 三大成长方向 ⚡ 效率型 (Efficiency) **触发词**: - \"效率\" \"快\" \"简洁\" \"少废话\" \"直接\" - \"我希望你更简洁\" - \"太啰嗦了\" **学习内容**:","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"1. 分析对话历史 (最近 50 条)\n2. 提取特征:\n   - 技术问题比例\n   - 平均回复长度偏好\n   - 情绪类对话比例\n   - 创意/建议请求比例\n3. 推荐职业 (基于特征)\n4. 生成初始能力值 (基于表现)\n5. 推荐成长方向\n6. 展示首次体验卡"},{"language":"text","snippet":"🌳 SkillTree 已激活！\n\n我分析了我们过去的对话，这是你的 Agent 画像:\n\n┌─────────────────────────────────────────────┐\n│ 推荐职业: {CLASS_EMOJI} {CLASS_NAME}        │\n│ 原因: {REASON}                              │\n│                                             │\n│ 当前能力:                                   │\n│ 🎯{ACC} ⚡{SPD} 🎨{CRT} 💕{EMP} 🧠{EXP} 🛡️{REL} │\n│                                             │\n│ ✨ 亮点: {STRENGTH}                         │\n│ 📈 可提升: {WEAKNESS}                       │\n│                                             │\n│ 建议成长方向: {PATH_EMOJI} {PATH_NAME}      │\n│ → {PATH_EFFECT}                             │\n└─────────────────────────────────────────────┘\n\n这样开始？[是] [我想自己选]"},{"language":"python","snippet":"def analyze_history(messages):\n    \"\"\"分析最近 50 条对话，生成 Agent 画像\"\"\"\n    \n    features = {\n        \"tech_ratio\": 0,      # 技术问题比例\n        \"brevity_pref\": 0,    # 简洁偏好 (是否常说\"太长\")\n        \"emotional\": 0,       # 情绪类对话比例\n        \"creative_asks\": 0,   # 创意请求比例\n        \"correction_rate\": 0, # 纠正率\n        \"proactive_accept\": 0 # 主动行动接受率\n    }\n    \n    # 分析每条消息...\n    \n    return features\n\ndef recommend_class(features):\n    \"\"\"基于特征推荐职业\"\"\"\n    \n    if features[\"tech_ratio\"] > 0.5:\n        if features[\"brevity_pref\"] > 0.3:\n            return \"developer\"  # 技术+简洁 = 开发者\n        else:\n            return \"cto\"  # 技术+详细 = CTO\n    \n    if features[\"emotional\"] > 0.4:\n        return \"life_coach\"\n    \n    if features[\"creative_asks\"] > 0.3:\n        return \"creative\"\n    \n    return \"assistant\"  # 默认\n\ndef recommend_path(features):\n    \"\"\"基于特征推荐成长方向\"\"\"\n    \n    if features[\"brevity_pref\"] > 0.3:\n        return \"efficiency\"  # 用户嫌啰嗦 → 效率型\n    \n    if features[\"emotional\"] > 0.3:\n        return \"companion\"  # 情绪类多 → 伙伴型\n    \n    if features[\"tech_ratio\"] > 0.5:\n        return \"expert\"  # 技术类多 → 专家型\n    \n    return \"efficiency\"  # 默认效率型"},{"language":"python","snippet":"def detect_feedback(human_response):\n    \"\"\"检测 human 的反馈信号\"\"\"\n    \n    positive = [\"谢谢\", \"完美\", \"厉害\", \"好的\", \"👍\", \"❤️\"]\n    learning = [\"太长\", \"简短\", \"说人话\", \"不懂\"]\n    correction = [\"不对\", \"不是\", \"错了\", \"重新\"]\n    \n    if any(p in human_response for p in positive):\n        return {\"type\": \"positive\", \"xp\": 15}\n    \n    if any(l in human_response for l in learning):\n        return {\"type\": \"learning\", \"signal\": extract_signal(human_response)}\n    \n    if any(c in human_response for c in correction):\n        return {\"type\": \"correction\"}\n    \n    # 无明确信号，默认正向\n    return {\"type\": \"neutral\", \"xp\": 5}"},{"language":"text","snippet":"[+15 XP ✨]"},{"language":"text","snippet":"[📝 记录: 偏好简洁 | 效率路线 +2]"}],"parameters":{},"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["typescript"],"docsSourceLabel":"CLAWHUB","editorialOverview":"SkillTree 主逻辑 🌳 SkillTree 主逻辑 🌳 --- 核心理念 1. **3 分钟上手** — 安装即激活，自动分析，快速开始 2. **即时反馈** — 每次互动都有感知 3. **效果可见** — 不是数字变化，是行为改变 4. **简单选择** — 3 条路线，不是 6 条 --- 触发机制 首次激活 (最重要!) **检测条件**: - evolution/profile.json 不存在 - 或用户说 \"激活 SkillTree\" **立即执行**: 首次体验卡模板 --- 对话历史分析逻辑 --- 即时反馈系统 每次回复后检测 即时反馈显示 **正向反馈**: **学习反馈** (检测到可改进信号): **里程碑**: **技能解锁**: --- 三大成长方向 ⚡ 效率型 (Efficiency) **触发词**: - \"效率\" \"快\" \"简洁\" \"少废话\" \"直接\" - \"我希望你更简洁\" - \"太啰嗦了\" **学习内容**:","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":300,"uniquenessScore":71,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-04-15T00:45:39.800Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-09T16:44:44.870Z","emptyReason":null},"items":[{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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