Meeting Ingestion
garrytan/gbrain
Ingest meeting transcripts from ANY meeting recorder into brain pages with attendee enrichment, entity propagation, and timeline merge.
Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities.
AGPL-3.0Auto-check passed
Install Memory Ingest
$ npx skills add basicmachines-co/basic-memory --skill memory-ingest -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install basicmachines-co/basic-memory memory-ingest --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/memory-ingest .claude/skills/memory-ingest && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "memory-ingest" agent skill from https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingest into .claude/skills/memory-ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-ingest", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingestType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add basicmachines-co/basic-memory --skill memory-ingest -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install basicmachines-co/basic-memory memory-ingest --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/memory-ingest .agents/skills/memory-ingest && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "memory-ingest" agent skill from https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingest into .agents/skills/memory-ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-ingest", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add basicmachines-co/basic-memory --skill memory-ingest -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install basicmachines-co/basic-memory memory-ingest --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/memory-ingest .cursor/skills/memory-ingest && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "memory-ingest" agent skill from https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingest into .cursor/skills/memory-ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-ingest", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/basicmachines-co/basic-memory.git --path skills/memory-ingest--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add basicmachines-co/basic-memory --skill memory-ingest -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install basicmachines-co/basic-memory memory-ingest --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/memory-ingest .gemini/skills/memory-ingest && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "memory-ingest" agent skill from https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingest into .gemini/skills/memory-ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-ingest", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install basicmachines-co/basic-memory memory-ingestInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add basicmachines-co/basic-memory --skill memory-ingest -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/memory-ingest .github/skills/memory-ingest && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "memory-ingest" agent skill from https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingest into .github/skills/memory-ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-ingest", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add basicmachines-co/basic-memory --skill memory-ingest -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install basicmachines-co/basic-memory memory-ingest --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/basicmachines-co/basic-memory.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/memory-ingest .opencode/skills/memory-ingest && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "memory-ingest" agent skill from https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-ingest into .opencode/skills/memory-ingest/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-ingest", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
memory-ingest- GitHub stars
- 4.1k
- Used in
- 1 other repo
- Token cost
- ~2.9k tokens
- SKILL.md length
- 816 words
- Files
- 1
- Skills in repo
- 49
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities.
- Works in 8 steps: Parse Raw Input → Extract Entities → Search Existing Entities → …
- SKILL.md covers When to Use, Workflow Overview, Step 1: Parse Raw Input and Step 2: Extract Entities, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Memory Ingest is an agent skill from basicmachines-co/basic-memory. Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities. Extracts entities, searches for existing matches, proposes new entities with approval, creates notes with observations and relations, and captures action items.
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: AI conversations that actually remember. Never re-explain your project to your AI again. Join our Discord: https://discord.gg/tyvKNccgqN. The licence is AGPL-3.0.
Example prompts
- “/memory-ingest”
Requirements
- Python 3
Workflow steps
8 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 6982cfc. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Memory Ingest loads about 2.9k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 816 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from basicmachines-co/basic-memory at commit 6982cfc, republished under its AGPL-3.0 licence (© basicmachines-co). 816 words, ~2,884 tokens.
Download SKILL.mdSave it as
.claude/skills/memory-ingest/SKILL.md (or your agent's skills folder).- name
- memory-ingest
- description
- Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities. Extracts entities, searches for existing matches, proposes new entities with approval, creates notes with observations and relations, and captures action items.
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.
When to Use
- 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
Workflow Overview
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 commitmentsStep 1: Parse Raw Input
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.
Step 2: Extract Entities
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
Step 3: Search Existing Entities
For each extracted entity, search Basic Memory with multiple query variations:
# 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.
Step 4: Research New Entities (Optional)
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.
Step 5: Present Entity Proposal
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.
Step 6: Create the Source Note
Create the primary note for the ingested content. This is the "record of what happened" — it preserves the raw material and adds structured metadata.
Meeting / Conversation Note
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
## Relations
- attended [[Jordan Rivera]]
- with [[NovaTech]]
- discussed [[Federated Learning]]
- follow_up [[Send NovaTech Technical Spec]]
"""
)Document / Article Note
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
## Relations
- relates_to [[Edge Computing]]
- relates_to [[Model Optimization]]
"""
)Show full SKILL.md (361 more words)Show less
Observation Categories
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.
Step 7: Create Approved Entities
For each entity the user approved, create a structured note. Match the entity type to an appropriate template.
Person
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
## Relations
- works_at [[NovaTech]]
- discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]]
"""
)Organization
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
## Relations
- employs [[Jordan Rivera]]
- discussed_in [[NovaTech Meeting - Jordan Rivera - Feb 22, 2026]]
"""
)Concept / Topic
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
## 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.
Step 8: Extract Action Items
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 scopeIf using the memory-tasks skill, create Task notes for your action items. Otherwise, capture them as observations in the source note.
Guidelines
- 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.
© basicmachines-co, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/memory-ingest of basicmachines-co/basic-memory.
Open the folder on GitHubat commit 6982cfc
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in basicmachines-co/basic-memory, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Memory Ingest next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Memory Ingest this skillbasicmachines-co/basic-memory | 4.1k | 1 repos | ~2.9k | Automated safety check: Pass | AGPL-3.0 | |
| Meeting Ingestiongarrytan/gbrain | 31k | — | ~7.1k | Automated safety check: Pass | MIT | |
| Meetingsalirezarezvani/claude-skills | 28k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Meeting Distiller Prosickn33/agentic-awesome-skills | 47k | 1 repos | ~1k | Automated safety check: Pass | MIT | |
| Ingestcoreyhaines31/makerskills | 844 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Meeting Notes And Actionscomposio-community/awesome-codex-skills | 17k | — | ~357 | Automated safety check: Pass | None |
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Questions about Memory Ingest
What does Memory Ingest do?
Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities. Memory Ingest is an agent skill from basicmachines-co/basic-memory. Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities.
How do I install Memory Ingest in Claude Code?
Run `npx skills add basicmachines-co/basic-memory --skill memory-ingest -a claude-code`. Or copy the skill folder (skills/memory-ingest in basicmachines-co/basic-memory) into .claude/skills/memory-ingest in your project. Claude Code loads it when a task matches its description.
How do I install Memory Ingest in Codex?
Run `npx skills add basicmachines-co/basic-memory --skill memory-ingest -a codex`. Or copy the skill folder (skills/memory-ingest in basicmachines-co/basic-memory) into .agents/skills/memory-ingest in your project. Codex loads it when a task matches its description.
Can I use Memory Ingest in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add basicmachines-co/basic-memory --skill memory-ingest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-ingest, .gemini/skills/memory-ingest, .github/skills/memory-ingest and .opencode/skills/memory-ingest in your project.
What does Memory Ingest need to run?
SKILL.md names no scripts, command-line tools or credentials: Memory Ingest is instructions for the agent only. Our summary lists: Python 3.
Does Memory Ingest access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Memory Ingest safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Memory Ingest use?
Memory Ingest is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Memory Ingest use?
About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Memory Ingest?
Skills that share tags, products or a category with Memory Ingest: Meeting Ingestion (garrytan/gbrain, 31k stars), Meetings (alirezarezvani/claude-skills, 28k stars), Meeting Distiller Pro (sickn33/agentic-awesome-skills, 47k stars) and Ingest (coreyhaines31/makerskills, 844 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Memory Ingest?
basicmachines-co (a GitHub organization) maintains it in basicmachines-co/basic-memory, which has 4,107 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 7, 2026.
Source: basicmachines-co/basic-memory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.