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Minutes Ingest

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silverstein
minutes-ingest

Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.

Overview

Publishersilverstein
Repositoryminutes
Skill nameminutes-ingest
Stars
1.5K
Forks
163
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by silverstein on GitHub. Read the source before you install it.

Installation

Install the Minutes Ingest AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/silverstein/minutes.git /tmp/minutes
mkdir -p .claude/skills
cp -r /tmp/minutes/tooling/skills/goldens/claude/minutes-ingest .claude/skills/minutes-ingest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Minutes Ingest in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Minutes Ingest on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Minutes Ingest is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/minutes-ingest

Process meetings through the knowledge extraction pipeline to update person profiles, append to the knowledge log, and maintain the index.

Prerequisites

The [knowledge] section must be configured in ~/.config/minutes/config.toml:

toml
[knowledge]
enabled = true
path = "/path/to/knowledge/base"
adapter = "wiki"  # or "para", "obsidian"
engine = "none"   # or "agent" for LLM extraction
min_confidence = "strong"

If not configured, explain what's needed and offer to help set it up.

How to run

Single meeting

bash
minutes ingest ~/meetings/2026-04-03-strategy-call.md

All normal meetings (backfill)

bash
minutes ingest --all

Preview without writing (recommended first time)

bash
minutes ingest --all --dry-run

What it does

  1. Reads each meeting's YAML frontmatter (decisions, action_items, entities, intents)
  2. Extracts structured facts with confidence levels and source provenance
  3. Updates person profiles in the knowledge base (adapter-dependent format)
  4. Appends to log.md with a timestamped entry for each ingested meeting
  5. Skips facts that already exist (deduplication) or are below the confidence threshold
  6. Excludes meetings designated sensitivity: restricted from automated knowledge-base ingestion

Safety guarantees

  • engine = "none" (default): Only extracts from parsed YAML frontmatter. No LLM involved, zero hallucination risk.
  • Confidence thresholds: Facts below min_confidence are counted as "skipped" but never written.
  • Provenance: Every fact records which meeting it came from and when.
  • Deduplication: Facts whose text already appears in a person's profile are skipped.
  • Dry-run: Always suggest --dry-run first if the user hasn't used ingest before.

Interpreting the output

Ingesting 73 meeting(s) into knowledge base at /path/to/kb
  2026-04-03-strategy.md — 4 written, 1 skipped — Mat, Dan
  2026-04-05-standup.md — 2 written, 0 skipped — Alice
  SKIP 2026-03-18-test.md: no frontmatter

Done. 6 fact(s) written, 1 skipped, 1 error(s), 3 people updated.
  • written: facts that passed confidence threshold and didn't already exist
  • skipped: facts below confidence threshold (logged, not written)
  • SKIP: files that couldn't be parsed (no frontmatter, invalid YAML, etc.)

Gotchas

  • Meetings without summarization have no structured data — If a meeting was recorded before summarization was enabled, its frontmatter won't have action_items or decisions. The ingest will correctly extract 0 facts. This is expected, not an error.
  • engine = "agent" requires an AI CLI — If the user wants richer LLM-based extraction from transcript body text, they need claude, codex, gemini, opencode, or pi on PATH.
  • PARA adapter writes items.json — If the user's knowledge base uses the PARA format, facts go into areas/people/{slug}/items.json with atomic fact schema (id, status, supersededBy).
  • First run should be dry-run — Always suggest minutes ingest --all --dry-run before the first real run so the user can see what would be extracted.

Frequently asked questions

What does the Minutes Ingest AI skill do?

Extract facts from meetings and update your knowledge base — person profiles, chronological log, and index. Use when the user asks "ingest my meetings", "update my knowledge base", "extract facts from meetings", "sync meetings to wiki", "backfill knowledge", or wants their PARA/Obsidian/wiki profiles updated from conversation data.

Why use Minutes Ingest on TypingMind?

Because you install it once and use it with any model. Minutes Ingest is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Minutes Ingest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/silverstein/minutes/tree/main/tooling/skills/goldens/claude/minutes-ingest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Minutes Ingest?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Minutes Ingest?

As many as you like. As long as a model supports skills, you can use Minutes Ingest with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Minutes Ingest AI skill free?

Yes. It is published on GitHub by silverstein under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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