Crawler Summary

learning-system answer-first brief

AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。 --- name: learning-system description: "AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。" argument-hint: "[--mode deep-dive|recap|review|health] [--topic name] [--quick]" --- Learning System 将零散的资讯、调研、代码实战转 Capability contract not published. No trust telemetry is available yet. 4 GitHub stars reported by the source. Last updated 4/15/2026.

Freshness

Last checked 4/15/2026

Best For

learning-system is best for general automation workflows where MCP and 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: 94/100

learning-system

AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。 --- name: learning-system description: "AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。" argument-hint: "[--mode deep-dive|recap|review|health] [--topic name] [--quick]" --- Learning System 将零散的资讯、调研、代码实战转

MCPself-declared
OpenClawself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Apr 15, 2026

Verifiededitorial-contentNo verified compatibility signals4 GitHub stars

Capability contract not published. No trust telemetry is available yet. 4 GitHub stars reported by the source. Last updated 4/15/2026.

4 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

MCP, OpenClaw

Freshness

Apr 15, 2026

Vendor

Echovic

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Unpublished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. 4 GitHub stars reported by the source. Last updated 4/15/2026.

Setup snapshot

git clone https://github.com/echoVic/learning-system-skill.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

Echovic

profilemedium
Observed Apr 15, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

MCP, OpenClaw

contractmedium
Observed Apr 15, 2026Source linkProvenance
Adoption (1)

Adoption signal

4 GitHub stars

profilemedium
Observed Apr 15, 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

3

Snippets

0

Languages

typescript

Parameters

Executable Examples

text

notes/areas/
├── ai-knowledge-map.md           # 知识图谱(掌握程度标记)
├── deep-dives/                    # 深度学习笔记
│   ├── mcp-tool-call-design.md
│   └── ...
└── weekly-reviews/                # 每周学习回顾
    ├── 2026-W07.md
    └── ...

bash

python3 scripts/health_check.py

bash

python3 scripts/mastery_score.py          # 表格报告
python3 scripts/mastery_score.py --json   # 附加 JSON 输出

Docs & README

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

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。 --- name: learning-system description: "AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。" argument-hint: "[--mode deep-dive|recap|review|health] [--topic name] [--quick]" --- Learning System 将零散的资讯、调研、代码实战转

Full README

name: learning-system description: "AI 领域系统学习体系。管理知识图谱、深度学习笔记、实战复盘和关联网络。触发场景:学习计划、知识图谱更新、深度研究某个 AI 主题、实战复盘总结、调研后沉淀知识、每周学习回顾。当用户说'学了什么'、'总结一下'、'沉淀知识'、'复盘'、'更新图谱'、'深入研究'、'写笔记'、'学习回顾'、'review what I learned'、'update knowledge map'、'deep dive'、'recap'、'what did I learn' 时使用。当改完代码/读完论文/做完调研后需要提炼和归纳时使用。" argument-hint: "[--mode deep-dive|recap|review|health] [--topic name] [--quick]"

Learning System

将零散的资讯、调研、代码实战转化为体系化的 AI 领域专业知识。

核心理念

输入不等于学习。 看了 100 篇推文不代表懂了推理优化。改了 3 个 MCP bug 不代表吃透了 MCP 协议。学习 = 输入 + 加工 + 关联 + 输出。

模式选择

根据 $ARGUMENTS 或用户意图选择模式:

| 参数 | 模式 | 说明 | |------|------|------| | --mode deep-dive | 深度研究 | 选题 → 研究 → 写笔记 → 更新图谱 | | --mode recap | 实战复盘 | 分析 PR/改动 → 提炼知识点 → 关联图谱 | | --mode review | 每周回顾 | 汇总本周 → 更新图谱 → 生成周报 | | --mode health | 健康检查 | 运行 scripts/health_check.py 输出报告 | | 无参数 | 自动判断 | 根据上下文推断最合适的模式 |

附加参数:

  • --topic <name>: 指定主题(deep-dive 模式)
  • --quick: 跳过确认节点,全自动执行

文件结构

notes/areas/
├── ai-knowledge-map.md           # 知识图谱(掌握程度标记)
├── deep-dives/                    # 深度学习笔记
│   ├── mcp-tool-call-design.md
│   └── ...
└── weekly-reviews/                # 每周学习回顾
    ├── 2026-W07.md
    └── ...

Mode: 深度研究 (deep-dive)

Copy this checklist and check off items as you complete them:

Deep Dive Progress:

  • [ ] Step 1: 选题 ⚠️ REQUIRED
    • [ ] 1.1 如果 --topic 已指定,直接使用
    • [ ] 1.2 否则,检查最近 3 天的 memory 日志和 PR 记录
    • [ ] 1.3 问自己:哪个技术点是我刚接触但还没真正理解的?
    • [ ] 1.4 问自己:这个主题能串联哪些已有知识?(越多越好)
    • [ ] 1.5 确认选题范围不要太宽("推理优化"太大,"vLLM PagedAttention 实现"刚好)
  • [ ] Step 2: 确认选题 ⚠️ REQUIRED (除非 --quick)
    • [ ] 向用户确认:选题 + 预计关联的知识点 + 预计产出
  • [ ] Step 3: 研究
    • [ ] 3.1 Load references/deep-dive-template.md 获取笔记模板
    • [ ] 3.2 查找相关源码、论文、文档
    • [ ] 3.3 如果有对应的 AI/ML skill,按需加载参考
  • [ ] Step 4: 写笔记
    • [ ] 4.1 在 notes/areas/deep-dives/ 创建笔记文件
    • [ ] 4.2 问自己:我能用自己的话向别人解释清楚吗? 如果不能,说明还没真正理解
    • [ ] 4.3 建立关联:→ 关联: [主题](相对路径)
  • [ ] Step 5: 更新知识图谱
    • [ ] 5.1 Load references/knowledge-map-rules.md 获取升级标准
    • [ ] 5.2 更新 notes/areas/ai-knowledge-map.md 中对应主题的掌握程度
  • [ ] Step 6: 交付检查
    • [ ] Load references/quality-checklist.md 逐项验证

Mode: 实战复盘 (recap)

Recap Progress:

  • [ ] Step 1: 识别改动 ⚠️ REQUIRED
    • [ ] 1.1 确认要复盘的 PR/Issue/改动
    • [ ] 1.2 问自己:这次改动中,哪个技术点是我之前不知道的?
    • [ ] 1.3 问自己:如果下次遇到类似问题,我能直接解决吗?
  • [ ] Step 2: 提炼知识点
    • [ ] 2.1 Load references/recap-template.md 获取复盘模板
    • [ ] 2.2 每个知识点关联到知识图谱的具体领域
    • [ ] 2.3 问自己:两个请求同时打到这段代码会怎样?(如果涉及并发)
    • [ ] 2.4 问自己:在检查权限和实际操作之间,状态有没有可能被改变?(如果涉及安全)
  • [ ] Step 3: 写入日志
    • [ ] 在当天的 memory/YYYY-MM-DD.md 中增加复盘 section
  • [ ] Step 4: 更新图谱(条件)
    • [ ] 如果有知识点升级,Load references/knowledge-map-rules.md 并更新

Mode: 每周回顾 (review)

Weekly Review Progress:

  • [ ] Step 1: 收集本周输入 ⚠️ REQUIRED
    • [ ] 1.1 读取本周的 memory 日志(最近 7 天)
    • [ ] 1.2 检查本周新增/修改的深度笔记
    • [ ] 1.3 检查本周的 PR 和代码改动
  • [ ] Step 2: 评估学习深度
    • [ ] 2.1 Load references/knowledge-map-rules.md
    • [ ] 2.2 对每个输入项判断:只是看了?理解了原理?有实战经验?
    • [ ] 2.3 问自己:这周我在 AI 领域变强了吗?哪里变强了?
    • [ ] 2.4 问自己:哪些输入转化成了真正的知识?
  • [ ] Step 3: 更新知识图谱
    • [ ] 确认变更列表 ⚠️ REQUIRED (除非 --quick)
    • [ ] 更新 notes/areas/ai-knowledge-map.md
  • [ ] Step 4: 生成周报
    • [ ] Load references/weekly-review-template.md
    • [ ] 写入 notes/areas/weekly-reviews/2026-Wxx.md
  • [ ] Step 5: 发送摘要
    • [ ] 通过飞书发送给用户

Mode: 健康检查 (health)

python3 scripts/health_check.py

输出知识图谱统计、深度笔记状态、本周活动量、改进建议。


Mode: Mastery Score (mastery)

python3 scripts/mastery_score.py          # 表格报告
python3 scripts/mastery_score.py --json   # 附加 JSON 输出

自动计算每个知识图谱主题的掌握分数,基于:

  • Recency(时间衰减): 指数衰减,半衰期 30 天。今天接触 = 1.0,30 天前 = 0.5,60 天前 = 0.25
  • Repetition(重复次数): 跨不同日期的接触次数累加
  • Depth(深度权重): deep-dive 笔记 ×3.0,PR/复盘 ×2.0,普通提及 ×1.0

输出包含:分数排名、建议升降级、衰减警告(60 天未接触)。


关联网络

在深度笔记和复盘中主动建立关联。格式:→ 关联: [主题](相对路径)

| 关联类型 | 示例 | |----------|------| | 技术关联 | vLLM → PagedAttention → KV Cache 管理 | | 实战关联 | gemini-cli OAuth PR → OAuth 2.1 协议 | | 对比关联 | Flash Attention vs PagedAttention |

与其他 skill 的关系

  • para-second-brain: 学习笔记存在 PARA 的 areas/ 下,自动被 memory_search 索引
  • 85 个 AI/ML skills: 作为参考资料,深度学习时按需加载对应 skill
  • openclaw-feeds / news-summary: 资讯输入源,但不等于学习——需要加工和关联

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

MCP: self-declaredOpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/snapshot"
curl -s "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/contract"
curl -s "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/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
GITLAB_AI_CATALOGgitlab-mcp

Rank

83

A Model Context Protocol (MCP) server for GitLab

Traction

No public download signal

Freshness

Updated 2d ago

MCP
GITLAB_PUBLIC_PROJECTSgitlab-mcp

Rank

80

A Model Context Protocol (MCP) server for GitLab

Traction

No public download signal

Freshness

Updated 2d ago

MCP
GITLAB_AI_CATALOGrmcp-openapi

Rank

74

Expose OpenAPI definition endpoints as MCP tools using the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)

Traction

No public download signal

Freshness

Updated 2d ago

MCP
GITLAB_AI_CATALOGrmcp-actix-web

Rank

72

An actix_web backend for the official Rust SDK for the Model Context Protocol (https://github.com/modelcontextprotocol/rust-sdk)

Traction

No public download signal

Freshness

Updated 2d ago

MCP
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/echovic-learning-system-skill/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/echovic-learning-system-skill/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/echovic-learning-system-skill/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/echovic-learning-system-skill/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "MCP",
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-17T00:22:29.455Z"
    }
  },
  "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": "MCP",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    }
  ],
  "flattenedTokens": "protocol:MCP|unknown|profile 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": "Echovic",
    "href": "https://github.com/echoVic/learning-system-skill",
    "sourceUrl": "https://github.com/echoVic/learning-system-skill",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T01:15:43.419Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "MCP, OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-04-15T01:15:43.419Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "4 GitHub stars",
    "href": "https://github.com/echoVic/learning-system-skill",
    "sourceUrl": "https://github.com/echoVic/learning-system-skill",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T01:15:43.419Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/echovic-learning-system-skill/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
  }
]

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