memory-ingest

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Memory Ingest

Turn raw, unstructured input into structured Basic Memory entities. Meeting transcripts, conversation logs, pasted documents, email threads — anything with information worth preserving gets parsed, cross-referenced against existing knowledge, and written as proper notes.

将原始非结构化输入转换为结构化Basic Memory实体。会议转录文本、对话日志、粘贴的文档、邮件线程——任何值得留存的信息都会被解析，与现有知识交叉比对，并以规范笔记的形式存储。

When to Use

适用场景

User pastes a meeting transcript or conversation log
User says "process these notes" or "add this to Basic Memory"
User pastes a document, article, or email for knowledge extraction
Any time raw external text needs to become structured knowledge

用户粘贴会议转录文本或对话日志
用户指示「处理这些笔记」或「将其添加到Basic Memory」
用户粘贴文档、文章或邮件用于知识提取
任何需要将原始外部文本转换为结构化知识的场景

Workflow Overview

工作流概览

1. Parse raw input           → identify structure, extract key info
2. Extract entities          → people, orgs, topics, action items
3. Search existing entities  → multi-variation queries
4. Research new entities     → optional web research (see memory-research)
5. Present entity proposal   → get approval before creating
6. Create source note        → verbatim content + observations + relations
7. Create approved entities  → structured notes for each new entity
8. Extract action items      → follow-ups and commitments

1. Parse raw input           → identify structure, extract key info
2. Extract entities          → people, orgs, topics, action items
3. Search existing entities  → multi-variation queries
4. Research new entities     → optional web research (see memory-research)
5. Present entity proposal   → get approval before creating
6. Create source note        → verbatim content + observations + relations
7. Create approved entities  → structured notes for each new entity
8. Extract action items      → follow-ups and commitments

Step 1: Parse Raw Input

步骤1：解析原始输入

Read the pasted content and identify its structure:

Format: Meeting transcript, email thread, conversation log, article, freeform notes
Date: When this happened (extract from content or ask)
Participants: Who was involved (names, roles, organizations)
Sections: Any existing structure (headings, speaker labels, timestamps)

Don't rewrite or summarize the source content. Preserve it verbatim in the note — you'll add structured observations alongside it.

读取粘贴的内容并识别其结构：

格式：会议转录文本、邮件线程、对话日志、文章、自由格式笔记
日期：事件发生时间（从内容中提取或询问用户）
参与者：涉及的人员（姓名、角色、所属组织）
章节：任何现有结构（标题、发言者标签、时间戳）

不要重写或总结源内容，在笔记中原样留存——你可以在旁边添加结构化观察结果。

Step 2: Extract Entities

步骤2：提取实体

Scan the content for entities worth tracking in the knowledge graph:

Entity Type	Signals
Person	Names with roles, titles, or affiliations mentioned
Organization	Company names, agencies, institutions
Topic/Concept	Technical domains, methodologies, standards discussed substantively
Action Item	Commitments, deadlines, "I'll do X by Y" statements

Infer type from context. If someone is introduced as "CTO of Acme Corp", that's both a Person and an Organization entity. If a technology is discussed in depth, it might warrant a Concept entity.

Exclude noise. Not every name mentioned is worth an entity. Filter for:

People with substantive roles or interactions (not passing mentions)
Organizations discussed in business/technical context
Topics with enough detail to warrant their own note

扫描内容，找出值得在知识图谱中追踪的实体：

实体类型	识别信号
Person	提到的带有角色、头衔或所属关系的姓名
Organization	公司名称、机构、院校
Topic/Concept	被实质性讨论的技术领域、方法论、标准
Action Item	承诺、截止日期、「我会在Y时间前完成X」类表述

根据上下文推断类型。如果有人被介绍为「CTO of Acme Corp」，则同时属于Person和Organization实体。如果某项技术被深入讨论，可能需要为其创建Concept实体。

排除噪音。不是每个提到的名字都值得创建实体，筛选标准：

拥有实质性角色或互动的人员（不是一笔带过的提及）
在业务/技术场景下讨论的组织
有足够细节支撑独立笔记的主题

Step 3: Search Existing Entities

步骤3：搜索现有实体

For each extracted entity, search Basic Memory with multiple query variations:

python

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对每个提取到的实体，使用多种查询变体在Basic Memory中搜索：

python

undefined

Person — try full name, last name

search_notes(query="Sarah Chen") search_notes(query="Chen")

Organization — try full name, abbreviation, acronym

search_notes(query="National Renewable Energy Laboratory") search_notes(query="NREL")

Topic — try the full term and keywords

search_notes(query="edge computing") search_notes(query="edge inference")


Classify each entity as:
- **Existing** — found in Basic Memory. Will link to it with `[[wiki-link]]`.
- **Proposed** — not found. Will propose creation pending approval.

search_notes(query="edge computing") search_notes(query="edge inference")


将每个实体分类为：
- **已存在**：在Basic Memory中找到，将通过`[[wiki-link]]`关联
- **待提议**：未找到，将提议创建，等待审批

Step 4: Research New Entities (Optional)

步骤4：调研新实体（可选）

For proposed entities where more context would be valuable, do a brief web search (2-3 queries max per entity):

Organizations: What they do, size, public/private, key products
People: Current role, background, expertise
Topics: Brief definition, relevance

Use hedging language ("appears to be", "estimated", "based on public information"). Never fabricate details.

This step is optional — skip it if the source material provides enough context, or if the user is in a hurry. See the memory-research skill for deeper research workflows.

对于需要更多上下文的待提议实体，进行简短的网页搜索（每个实体最多2-3次查询）：

Organization：业务范围、规模、公立/私有、核心产品
Person：当前职位、背景、专业领域
Topic：简短定义、相关性

使用谨慎表述（「appears to be」、「estimated」、「based on public information」），绝对不要编造细节。

该步骤为可选——如果源材料已经提供足够上下文，或者用户赶时间，可以跳过。更深入的调研工作流请参考memory-research技能。

Step 5: Present Entity Proposal

步骤5：展示实体提议

Before creating anything, present what you found and what you'd like to create:

Entities found in Basic Memory:
  - [[Sarah Chen]] (Person — existing)
  - [[Acme Corp]] (Organization — existing)

Proposed new entities:
  - Jordan Rivera (Person — VP Engineering at NovaTech, mentioned as project lead)
  - NovaTech (Organization — SaaS platform, Series B, discussed as integration partner)
  - Federated Learning (Concept — core technical topic of the discussion)

Approve all / select individually / skip entity creation?

Include enough context with each proposed entity for the user to make a quick decision.

在创建任何内容前，先展示你发现的内容和计划创建的实体：

Entities found in Basic Memory:
  - [[Sarah Chen]] (Person — existing)
  - [[Acme Corp]] (Organization — existing)

Proposed new entities:
  - Jordan Rivera (Person — VP Engineering at NovaTech, mentioned as project lead)
  - NovaTech (Organization — SaaS platform, Series B, discussed as integration partner)
  - Federated Learning (Concept — core technical topic of the discussion)

Approve all / select individually / skip entity creation?

为每个待提议实体提供足够上下文，方便用户快速决策。

Step 6: Create the Source Note

步骤6：创建源笔记

Create the primary note for the ingested content. This is the "record of what happened" — it preserves the raw material and adds structured metadata.

为摄入的内容创建主笔记。这是「事件记录」——留存原始材料并添加结构化元数据。

Meeting / Conversation Note

会议/对话笔记

python

write_note(
  title="NovaTech Meeting - Jordan Rivera - Feb 22, 2026",
  directory="meetings/2026",
  note_type="meeting",
  tags=["meeting", "novatech", "federated-learning"],
  metadata={"date": "2026-02-22"},
  content="""

python

write_note(
  title="NovaTech Meeting - Jordan Rivera - Feb 22, 2026",
  directory="meetings/2026",
  note_type="meeting",
  tags=["meeting", "novatech", "federated-learning"],
  metadata={"date": "2026-02-22"},
  content="""

NovaTech Meeting - Jordan Rivera - Feb 22, 2026

Brief one-sentence summary of what this meeting was about.

Transcript

[Preserve all source content verbatim — do not summarize or rewrite]

Observations

[opportunity] NovaTech interested in integration partnership
[insight] Their platform handles 10K concurrent sessions, relevant to our scale needs
[next_step] Send technical spec document by Friday
[sentiment] Strong enthusiasm from their engineering team
[decision] Agreed to start with a proof-of-concept integration

[opportunity] NovaTech interested in integration partnership
[insight] Their platform handles 10K concurrent sessions, relevant to our scale needs
[next_step] Send technical spec document by Friday
[sentiment] Strong enthusiasm from their engineering team
[decision] Agreed to start with a proof-of-concept integration

Relations

attended [[Jordan Rivera]]
with [[NovaTech]]
discussed [[Federated Learning]]
follow_up [[Send NovaTech Technical Spec]] """ )

undefined

attended [[Jordan Rivera]]
with [[NovaTech]]
discussed [[Federated Learning]]
follow_up [[Send NovaTech Technical Spec]] """ )

undefined

Document / Article Note

文档/文章笔记

python

write_note(
  title="Edge Computing Architecture Whitepaper",
  directory="references",
  note_type="reference",
  tags=["edge-computing", "architecture", "reference"],
  metadata={"source": "https://example.com/whitepaper.pdf", "date_ingested": "2026-02-22"},
  content="""

python

write_note(
  title="Edge Computing Architecture Whitepaper",
  directory="references",
  note_type="reference",
  tags=["edge-computing", "architecture", "reference"],
  metadata={"source": "https://example.com/whitepaper.pdf", "date_ingested": "2026-02-22"},
  content="""

Edge Computing Architecture Whitepaper

Source Content

[Preserve relevant content — for long documents, include key sections rather than the entire text]

Observations

[key_finding] Latency drops 40% with edge inference vs cloud-only
[technique] Model sharding across heterogeneous edge nodes
[limitation] Requires minimum 8GB RAM per edge node

[key_finding] Latency drops 40% with edge inference vs cloud-only
[technique] Model sharding across heterogeneous edge nodes
[limitation] Requires minimum 8GB RAM per edge node

Relations

relates_to [[Edge Computing]]
relates_to [[Model Optimization]] """ )

undefined

relates_to [[Edge Computing]]
relates_to [[Model Optimization]] """ )

undefined

Observation Categories

观察结果分类

Use categories that capture the nature of the information. Common categories for ingested content:

Category	Use For
`opportunity`	Business or collaboration opportunities identified
`decision`	Decisions made or agreed upon
`insight`	Non-obvious understanding gained
`next_step`	Concrete action items or follow-ups
`sentiment`	Enthusiasm, concerns, hesitations expressed
`risk`	Risks or concerns identified
`requirement`	Requirements or constraints discovered
`key_finding`	Important facts from reference material
`technique`	Methods, approaches, or patterns described
`context`	Background information that may be useful later

Invent categories as needed — these are suggestions, not a fixed list.

使用能体现信息性质的分类。摄入内容的常用分类如下：

分类	适用场景
`opportunity`	识别到的业务或合作机会
`decision`	已做出或达成一致的决策
`insight`	获得的非显而易见的认知
`next_step`	具体的待办事项或后续跟进动作
`sentiment`	表达出的积极性、顾虑、犹豫情绪
`risk`	识别到的风险或顾虑
`requirement`	发现的需求或约束
`key_finding`	参考材料中的重要事实
`technique`	描述的方法、路径或模式
`context`	后续可能有用的背景信息

可根据需要创建新分类——以上仅为建议，并非固定列表。

Step 7: Create Approved Entities

步骤7：创建已审批实体

For each entity the user approved, create a structured note. Match the entity type to an appropriate template.

对每个用户审批通过的实体，创建结构化笔记。根据实体类型匹配合适的模板。

Person

python

write_note(
  title="Jordan Rivera",
  directory="people",
  note_type="person",
  tags=["person", "novatech", "engineering"],
  content="""

python

write_note(
  title="Jordan Rivera",
  directory="people",
  note_type="person",
  tags=["person", "novatech", "engineering"],
  content="""

Jordan Rivera

Overview

VP of Engineering at NovaTech. Met during integration partnership discussion.

Background

[Role, expertise, context from meeting + any web research]

Observations

[role] VP Engineering at NovaTech
[expertise] Distributed systems, federated learning
[met] 2026-02-22 during integration discussion

[role] VP Engineering at NovaTech
[expertise] Distributed systems, federated learning
[met] 2026-02-22 during integration discussion

Relations

works_at [[NovaTech]]
discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]] """ )

undefined

works_at [[NovaTech]]
discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]] """ )

undefined

Organization

python

write_note(
  title="NovaTech",
  directory="organizations",
  note_type="organization",
  tags=["organization", "saas", "integration-partner"],
  content="""

python

write_note(
  title="NovaTech",
  directory="organizations",
  note_type="organization",
  tags=["organization", "saas", "integration-partner"],
  content="""

NovaTech

Overview

SaaS platform company. Series B stage. [Additional context from meeting + web research]

Products & Services

[What they offer, if discussed or researched]

Observations

[stage] Series B, ~200 employees
[relevance] Potential integration partner for our platform
[first_contact] 2026-02-22

[stage] Series B, ~200 employees
[relevance] Potential integration partner for our platform
[first_contact] 2026-02-22

Relations

employs [[Jordan Rivera]]
discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]] """ )

undefined

employs [[Jordan Rivera]]
discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]] """ )

undefined

Concept / Topic

python

write_note(
  title="Federated Learning",
  directory="concepts",
  note_type="concept",
  tags=["concept", "machine-learning", "distributed-systems"],
  content="""

python

write_note(
  title="Federated Learning",
  directory="concepts",
  note_type="concept",
  tags=["concept", "machine-learning", "distributed-systems"],
  content="""

Federated Learning

Overview

[Brief description of the concept from the discussion context]

Observations

[definition] Machine learning approach where models train across decentralized data sources
[relevance] Core technique discussed in NovaTech integration

[definition] Machine learning approach where models train across decentralized data sources
[relevance] Core technique discussed in NovaTech integration

Relations

discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]] """ )


Adapt templates to your domain. The key elements are: type and tags as parameters, an overview section, observations with categories, and relations linking back to the source.

discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]] """ )


可根据你的领域调整模板。核心要素为：作为参数的类型和标签、概述部分、带分类的观察结果，以及关联回源笔记的链接。

Step 8: Extract Action Items

步骤8：提取待办事项

Review the source content for commitments and follow-ups:

Action Items:
  - Send NovaTech technical spec document by Friday (your commitment)
  - Jordan will share their API documentation by next week (their commitment)

Follow-Up Reminders:
  - 1 week: Check if Jordan sent API docs
  - 2 weeks: Schedule follow-up call to discuss POC scope

If using the memory-tasks skill, create Task notes for your action items. Otherwise, capture them as observations in the source note.

查看源内容，找出承诺和后续跟进项：

Action Items:
  - Send NovaTech technical spec document by Friday (your commitment)
  - Jordan will share their API documentation by next week (their commitment)

Follow-Up Reminders:
  - 1 week: Check if Jordan sent API docs
  - 2 weeks: Schedule follow-up call to discuss POC scope

如果使用memory-tasks技能，为你的待办事项创建Task笔记。否则，将其作为观察结果记录在源笔记中。

Guidelines

指导原则

Preserve source content verbatim. The original text is the ground truth. Structure and observations are your interpretation layered on top.
Search before creating. Always check if entities already exist (see memory-notes search-before-create pattern). Update existing entities with new information rather than creating duplicates.
Get approval for new entities. Present proposed entities and let the user decide which to create. Don't silently populate the knowledge graph.
Infer, don't interrogate. Extract entity types and relationships from context. Only ask the user when genuinely ambiguous.
Be selective about entities. Not every name mentioned deserves its own note. Focus on entities the user will want to reference again.
Use hedging for researched info. Web research supplements — don't present it as fact. "Appears to be", "estimated", "based on public information".
Link everything back. Every created entity should relate back to the source note. The source note should link to all entities discussed.
Prose and observations together. Notes work best with both narrative context and structured observations. Prose gives meaning and tells the story; observations make individual facts searchable. Use the body for context, then distill key facts into categorized observations.

原样留存源内容。原始文本是事实依据，结构和观察结果是你附加的解读。
创建前先搜索。始终检查实体是否已存在（参考memory-notes的创建前搜索模式）。使用新信息更新现有实体，而不是创建重复内容。
新实体需获得审批。展示待提议实体，让用户决定创建哪些，不要静默填充知识图谱。
推断而非盘问。从上下文提取实体类型和关系，仅在确实存在歧义时询问用户。
实体筛选要严格。不是每个提到的名字都值得单独创建笔记，重点关注用户后续会需要引用的实体。
调研信息使用谨慎表述。网页搜索仅作为补充，不要将其表述为事实，使用「appears to be」、「estimated」、「based on public information」等表述。
所有内容相互关联。每个创建的实体都应该关联回源笔记，源笔记应该链接到所有讨论到的实体。
叙述和观察结果结合。同时包含叙事上下文和结构化观察结果的笔记效果最好。叙述赋予意义、还原事件经过；观察结果让单个事实可搜索。正文部分放上下文，然后将关键事实提炼为带分类的观察结果。