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
Crawler Summary
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
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
Public facts
4
Change events
1
Artifacts
0
Freshness
Feb 25, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 2/25/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Feb 25, 2026
Vendor
Mikonos
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
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.gitSetup 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.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Mikonos
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
1
Snippets
0
Languages
typescript
Parameters
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
Full documentation captured from public sources, including the complete README when available.
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
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.
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)」。默认位置: 05_每日记录 (Daily)/YYYY/MM/DD
[标题]_结构笔记.md。YYYYMMDD_00_[标题]_结构笔记.md。结构笔记接入 (二选一,见 Phase 6):
## Inbox,在 Inbox 下追加本书入口(如 - [[本书结构笔记]] — 书名,YYYYMMDD)。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。
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。
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:
Agent: Mortimer Adler
templates/structure_note_template.md,含核心命题与逻辑支撑链。structure-note skillAgent: Niklas Luhmann
"不要问它属于哪个分类,问它和谁对话。"
templates/index_note_template.md,含关键词与多入口。index-note skill;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。
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。05_每日记录/...)移动到 03_索引 (Index)/ 下合适位置:
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/)。[[本索引笔记名]] 仍有效(仅文件名,不依赖路径)。Agent: Luhmann & Feynman
核心创新:边创建边发现 (Luhmann Scan)。
流程:
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).Atomic Note 规范:
templates/atomic_note_template.mdAgent: The Pragmatist
将 Phase 3 发现的方法论创建为 Method Note。
Method Note 规范 (高优先级):
templates/method_note_template.mdAgent: Richard Feynman
用 Feynman 标准审视整个知识网络:
Agent: Niklas Luhmann
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。03_索引/ 下其他索引(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 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:
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。.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。
在 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
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
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"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
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
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
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
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
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
Rank
70
The Frontend for Agents & Generative UI. React + Angular
Traction
No public download signal
Freshness
Updated 23d ago
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.