Crawler Summary

deep-learning answer-first brief

全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。 --- name: deep-learning description: 全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。 --- 深度阅读 (Deep Reading) **核心理念**:不仅要理解世界(Understand),还要改变世界(Act)。 **适用范围**:Book, Long-form Article, Research Report, Academic Paper. 专家座席 Capability contract not published. No trust telemetry is available yet. Last updated 2/25/2026.

Freshness

Last checked 2/25/2026

Best For

deep-learning is best for general automation workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Claim this agent
Agent DossierGitHubSafety: 89/100

deep-learning

全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。 --- name: deep-learning description: 全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。 --- 深度阅读 (Deep Reading) **核心理念**:不仅要理解世界(Understand),还要改变世界(Act)。 **适用范围**:Book, Long-form Article, Research Report, Academic Paper. 专家座席

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Feb 25, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 2/25/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Mikonos

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 2/25/2026.

Setup snapshot

git clone https://github.com/mikonos/deep-learning.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    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.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Mikonos

profilemedium
Observed Feb 25, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Feb 25, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

1

Snippets

0

Languages

typescript

Parameters

Executable Examples

markdown

# Preparation
- [ ] Pre-game Plan Created (≥6 项 TODO + Context;对话内区块或落盘文件)

# Structure & Index
- [ ] Structure Note Created (Adler)
- [ ] Index Note Created (Luhmann)
- [ ] Index Note Onboarding (Phase 2.5): 挂载到已存在索引 + 移动到 03_索引 文件夹

# Extraction Loop (Phase 3)
- [ ] [[笔记A (Concept)]] + Luhmann Scan
- [ ] [[笔记B (Concept)]] + Luhmann Scan

# Methodology (Phase 4)
- [ ] [[工具A (Method)]] (SOP/Checklist/MVE)

# Review (Phase 5 & 6)
- [ ] Feynman Check (De-jargon check)
- [ ] Network Check (2+ Links per note;索引入网:Inbox 或关键词条目;多索引挂载检查)

# Workflow Audit (Phase 6.5,强制)
- [ ] 调用 **workflow-audit** 做流程执行审查(德明+葛文德视角),产出审查报告并对 ❌ 项补执行,直至 DoD 全部通过

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。 --- name: deep-learning description: 全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。 --- 深度阅读 (Deep Reading) **核心理念**:不仅要理解世界(Understand),还要改变世界(Act)。 **适用范围**:Book, Long-form Article, Research Report, Academic Paper. 专家座席

Full README

name: deep-learning description: 全能深度阅读工具(All-in-One Deep Reading)。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler(结构)、Feynman(解释)、Luhmann(网络)、Pragmatist(工具化)、Critics(辩论);强制 High Fidelity 案例保留与 Actionable 工具提取。关键词:深度阅读、结构笔记、deep learning、卢曼。

深度阅读 (Deep Reading)

核心理念:不仅要理解世界(Understand),还要改变世界(Act)。 适用范围:Book, Long-form Article, Research Report, Academic Paper.

专家座席 (The Council)

  1. Mortimer Adler (The Architect): 负责结构化,提取核心命题和逻辑树。
  2. The Pragmatist (The Engineer): 负责工具化,提取可执行的 SOP、模板和清单。
  3. Richard Feynman (The Teacher): 负责解释力,确保概念去魅,用人话讲清楚。
  4. Niklas Luhmann (The Librarian): 负责连接性,确保知识入网,有机生长。
  5. The Critics (The Stress Testers): Musk, Socrates, Munger 负责压力测试和辩论。

核心法则 (The Iron Rules)

  1. Always Deep: 无论输入长短,默认按最高规格处理(结构+工具+辩论)。
  2. Case Fidelity (案例保真):
    • 有原文/书在手:禁止概括性改写;原子笔记中涉及案例、研究处须保留具体数字、作者/机构、时间线、原话(可标页码)。
    • 无原文、仅摘要或记忆:在笔记中标注 来源: 本书/摘要,未核对原文;保留能确定的专有名词与结论,禁止编造细节;Feynman 验收时标「案例保真:部分(无原文)」。
  3. No Vague Verbs (模糊词禁令): 禁止使用 "优化"、"加强"、"适当" 等虚词。必须转化为具体动作量化指标
  4. Metadata Mandatory (元数据强制): 所有笔记必须包含 YAML Frontmatter (type, tags, links)。禁止省略

存储规则 (Storage Rules)

  1. 默认位置: 05_每日记录 (Daily)/YYYY/MM/DD

    • 获取当前日期 (YYYY, MM, DD),若日期文件夹不存在则创建;创建本次任务文件夹。
    • 任务文件夹命名规范: 任务文件夹默认 [标题]_结构笔记.md
    • 结构笔记命名规范: 结构笔记默认 YYYYMMDD_00_[标题]_结构笔记.md
  2. 结构笔记接入 (二选一,见 Phase 6):

    • 方式 A:若目标索引有 ## Inbox,在 Inbox 下追加本书入口(如 - [[本书结构笔记]] — 书名,YYYYMMDD)。
    • 方式 B:若目标索引无 Inbox,则新建 Inbox章节
  3. 索引笔记入网 (见 Phase 2.5):将新建索引笔记挂载到已存在索引,并移动到 03_索引/ 下合适文件夹;具体 Inbox/入口写法见 Phase 2.5。


工作流程 (The Workflow)

执行顺序:Phase 0 → 1 → 2 → 2.5 → 3 → 4 → 5 → 6 → 6.5(流程执行审查,强制),不可跳步;Phase 2.5 须在 Phase 2 完成后立即执行;Phase 6.5 须在 Phase 6 完成后立即执行。

Phase 0: Pre-game Plan (准备)

在开始 Phase 1 前产出执行计划;落盘为 YYYYMMDD_01_[书名]_执行计划.md(与结构笔记同目录),并在 task.md 的 Preparation 下链接该文件。

确保包含:

  1. TODO List (≥6 项):如全书论证骨架、关键概念、论证链、框架提取、方法提取、案例核实、批判性审查、入网连接。
  2. Context:读取意图(我要解决什么问题?)。

Phase 1: Overview & Structure (概览与骨架)

Agent: Mortimer Adler

  1. 任务: 创建结构笔记;产出须符合 templates/structure_note_template.md,含核心命题与逻辑支撑链。
  2. 说明: 强制调用 structure-note skill

Phase 2: Index Design (索引设计)

Agent: Niklas Luhmann

"不要问它属于哪个分类,问它和谁对话。"

  1. 任务: 为本书创建索引笔记;产出须符合本书 templates/index_note_template.md,含关键词与多入口。
  2. 说明: 强制调用 index-note skill;

Phase 2.5: 索引笔记入网 (Index Note Onboarding)

Agent: Niklas Luhmann

索引笔记创建完成后立即执行;确保新索引被知识网络接纳且物理归位。

  1. 挂载到已存在索引:在 03_索引/ 下选定一个(或多个)与本书主题相关的已存在索引,在其中添加入口指向本索引笔记(如 ## Inbox 下追加 - [[本索引笔记名]] — 书名/主题,YYYYMMDD,或在该索引的「主题结构/子索引」板块添加)。确保双向链接:本索引笔记底部也链回该父索引。
  2. 移动到索引目录:将本索引笔记文件从当前任务目录(如 05_每日记录/...移动03_索引 (Index)/ 下合适位置:
    • 使用 file-organize skill 评估:根目录(与现有 Hub 并列)或某主题子文件夹(如 某主题相关/)。
    • 移动后父索引中的 [[本索引笔记名]] 仍有效(仅文件名,不依赖路径)。
  3. (多索引挂载检查延后至 Phase 6):本索引内提到的笔记还可挂到哪些其他索引,在 Phase 6 统一检查并添加入口。

Phase 3: Recursive Growth (递归生长)

Agent: Luhmann & Feynman

核心创新:边创建边发现 (Luhmann Scan)。

流程:

  1. Round 1 (骨架): 从结构笔记出发,创建所有显式链接的 Atomic Note (概念)
  2. Luhmann Scan (每张必做):见 references/luhmann_scan.md。三项——前置依赖、潜在连接、方法论发现(若概念附带可执行 how → 记入 task,Phase 4 创建详细的 Method Note)。在 task.md 中每张卡记录格式:前置 → Round 2: [[A]]. 连接 → Round 3: [[B]]. 方法论 → [[方法名]] (Phase 4).
  3. Round 2 (血肉): 创建发现的新笔记,继续 Luhmann Scan。
  4. Round 3+ (边缘): 直到完成或超出范围。

Atomic Note 规范:

  • 模板: templates/atomic_note_template.md
  • 聚焦: 定义 (Definition)、机制 (Mechanism)、语境 (Context)。
  • 验收: 费曼测试 (外行能听懂)。

Phase 4: Methodology Consolidation (方法论整理)

Agent: The Pragmatist

将 Phase 3 发现的方法论创建为 Method Note。

Method Note 规范 (高优先级):

  1. 模板: templates/method_note_template.md
  2. 聚焦: 可执行步骤 (SOP)、模板 (Template)、检查清单 (Checklist)。
  3. 约束:
    • No Vague Verbs: 无模糊动词。
    • Mechanism & Leverage: 为什么有效?
    • MVE: 下一步最小可行性实验。

Phase 5: Final Review (终极审视)

Agent: Richard Feynman

用 Feynman 标准审视整个知识网络:

  1. 去魅检验: 术语是否已"翻译"为日常语言?
  2. 比喻检验: 复杂概念是否有恰当比喻?
  3. 逻辑检验: 论证链是否有断裂?
  4. 拓扑检验: 是否形成了"意外的惊喜"连接?

Phase 6: Network Review (入网审视)

Agent: Niklas Luhmann

  1. 检查: 确认每张笔记(非结构父子)至少有 2 条双向链接。
  2. 入网: 在 03_索引/ 下相关索引中完成接入——## Inbox 无则新建 Inbox;结构笔记与原子笔记按关键词/主题入对应索引。必要时调用 index-note 模式三(内容入网)执行单篇/批量入网。
  3. 多索引挂载检查: 针对本索引内提到的笔记(本索引关键词区、主题结构区所链接的笔记),逐条评估是否还应挂载到 03_索引/其他索引(不同查找意图);若某笔记也是其他主题的优质入口,则在对应索引中添加入口,实现多入口可达。输出简要清单:[[笔记A]] → 已入 索引_X;建议补充入 索引_Y(理由)

Phase 6.5: 流程执行审查(强制)

Phase 6 完成后必须执行。调用 workflow-audit skill,以德明+葛文德视角对本流程执行完成度逐项核对与系统闭环检查:

  1. 审查对象:本 skill(deep-learning);产出:本次任务目录(执行计划、结构笔记、索引、原子笔记、task.md 等)。
  2. 产出:落盘审查报告(YYYYMMDD_[任务名]_流程审查报告_德明与葛文德视角.md),含逐项清单、系统闭环、DoD 勾选、多索引挂载清单;对审查中标为 ❌ 的项必须补执行,直至 DoD 全部通过。
  3. workflow-audit 主文件.cursor/skills/workflow-audit/SKILL.md;报告模板:workflow-audit/references/audit_report_template.md

不可跳过。未通过流程执行审查并补全漏项,则本流程视为未完成。


任务追踪 (Task Tracking)

task.md 中维护进度:

# Preparation
- [ ] Pre-game Plan Created (≥6 项 TODO + Context;对话内区块或落盘文件)

# Structure & Index
- [ ] Structure Note Created (Adler)
- [ ] Index Note Created (Luhmann)
- [ ] Index Note Onboarding (Phase 2.5): 挂载到已存在索引 + 移动到 03_索引 文件夹

# Extraction Loop (Phase 3)
- [ ] [[笔记A (Concept)]] + Luhmann Scan
- [ ] [[笔记B (Concept)]] + Luhmann Scan

# Methodology (Phase 4)
- [ ] [[工具A (Method)]] (SOP/Checklist/MVE)

# Review (Phase 5 & 6)
- [ ] Feynman Check (De-jargon check)
- [ ] Network Check (2+ Links per note;索引入网:Inbox 或关键词条目;多索引挂载检查)

# Workflow Audit (Phase 6.5,强制)
- [ ] 调用 **workflow-audit** 做流程执行审查(德明+葛文德视角),产出审查报告并对 ❌ 项补执行,直至 DoD 全部通过

质量验收清单 (Definition of Done)

  • [ ] Phase 0: 执行计划已产出(≥6 项 TODO + Context)。
  • [ ] Overview: 5个问题回答清晰,费曼测试通过。
  • [ ] Fidelity: 有原文时案例含数字/专有名词/原话;无原文时已标注来源限制且未编造细节。
  • [ ] Actionability: Method Note 包含无模糊词的步骤 + 可复制模板。
  • [ ] Network: 所有笔记双向可达 (≥2 links);已在 03_索引 相关索引入网(Inbox 或关键词条目);本索引已挂载到已存在索引并位于 03_索引 下;多索引挂载检查已完成。
  • [ ] Insight: 产生了新的连接或意外发现。
  • [ ] 流程执行审查:Phase 6.5 已执行,workflow-audit 已产出审查报告,且所有 ❌ 项已补执行、DoD 全部通过。

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/mikonos-deep-learning/snapshot"
curl -s "https://xpersona.co/api/v1/agents/mikonos-deep-learning/contract"
curl -s "https://xpersona.co/api/v1/agents/mikonos-deep-learning/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

Rank

70

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

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 5d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/mikonos-deep-learning/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/mikonos-deep-learning/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/mikonos-deep-learning/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-17T01:40:05.041Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Mikonos",
    "href": "https://github.com/mikonos/deep-learning",
    "sourceUrl": "https://github.com/mikonos/deep-learning",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-25T02:28:25.689Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-02-25T02:28:25.689Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/mikonos-deep-learning/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

Sponsored

Ads related to deep-learning and adjacent AI workflows.