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Ingest

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coreyhaines31
ingest

When you paste raw human input — a call transcript (Grain, Zoom, Granola, Fathom), a text or email from a client/partner/friend, a voice-memo dump, or meeting notes — and want it converted into structured work. Extracts decisions, action items (yours vs theirs), bugs/feature requests, and facts worth keeping; files GitHub issues in the right repo, captures to the second-brain vault, and drafts (never sends) the reply. Triggers on "/ingest", "ingest this", "here's my call with X," "here's the transcript," "this is from [person]," "[person] asked me this," "from [person]:", a pasted transcript with speaker labels, or a forwarded client message that clearly expects processing. Person→project routing lives in a private config; unknown senders get asked about once, then remembered.

Overview

Publishercoreyhaines31
Repositorymakerskills
Skill nameingest
Stars
855
Forks
71
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by coreyhaines31 on GitHub. Read the source before you install it.

Installation

Install the 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.
    https://github.com/coreyhaines31/makerskills/tree/main/skills/ingest
  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/coreyhaines31/makerskills.git /tmp/makerskills
mkdir -p .claude/skills
cp -r /tmp/makerskills/skills/ingest .claude/skills/ingest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

/ingest — Raw human input → structured work

You are a relay hub: clients text you, partners email you, calls get transcribed. Each of these carries decisions, action items, bugs, and facts — and processing one by hand means re-explaining the same routine every time. This skill is that routine, written down.

The contract: internal, reversible outputs (vault captures, filed issues) happen without ceremony. Outward-facing outputs (replies) are always drafted, never sent.

Files

PathWhat
${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/ingest/people.yamlPerson → project/repo/vault-page/reply-channel routing (private, gitignored)
<vault>/raw/call-<slug>.md / <vault>/raw/message-<slug>.mdCaptures, in second-brain's schema
references/people.yaml.exampleConfig schema with a worked example

<vault> is ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}.

Step 0 — Get the input and classify it

In order: content in the prompt → clipboard (pbpaste on macOS, wl-paste / xclip -o on Linux; skip on agents without one) → ask.

Classify by shape, not by what the user called it:

ShapeType
Speaker labels + timestamps, or a Grain/Zoom/Granola/Fathom URL or headercall
"from X:" / forwarded text or DM, first-person, shortmessage
Email headers or greeting/sign-off structureemail (treat as message with formal tone)
Unstructured first-person stream ("okay so I was thinking…")voice-note (no reply draft; capture + actions only)

Long transcripts arrive truncated in chat sometimes — if the content visibly cuts off mid-sentence, say so and ask for the file (or a path) rather than processing a fragment.

Step 1 — Identify who and which project

Read people.yaml. Match participants/senders against name and aliases.

  • Matched → you now have the repo, vault page, reply channel, and tone notes. Say which routing you're using in one line ("Routing: Jane → acme-app").
  • Unmatched → ask once: "Who is this and what project does it belong to?" Then offer to append them to people.yaml so the question never repeats. If the user declines to add them, process the input with explicit destinations instead of routed defaults.
  • Multiple projects in one call (common on partner calls) → split extraction by project; route each piece separately.

Step 2 — Extract

Read the whole input first. Then pull out, with a short verbatim quote or paraphrase anchoring each:

  1. Decisions made — anything settled, including "we're NOT doing X."
  2. Action items — mine — things the user owes someone. Include any stated deadline.
  3. Action items — theirs — things owed to the user (these become the follow-up section of the reply, not issues).
  4. Bugs & feature requests — anything that should become a GitHub issue. One issue per item, never a grab-bag.
  5. Questions to answer — asked but unanswered in the input.
  6. Facts worth keeping — durable context (pricing mentioned, a person's situation, a tool they use) → vault; a genuinely reusable reference → Keep note (only if the keep CLI is authed; skip silently otherwise).

Name verification rule: transcripts mishear proper nouns constantly (brand names, people, tools). Before a name lands in an issue title, vault page, or reply, verify the spelling against the routing config, the repo, or a quick search — never trust the transcript's spelling of a name you can check.

Step 3 — Show the routing plan

One compact table before acting:

#ItemDestination
1"Bulk-export button on the reports page"Issue → myorg/acme-app
2Decision: monthly billing defaultvault Projects/Acme.md
3Reply to Janedraft below

Then proceed without waiting — everything in the table is internal or a draft. Pause for confirmation only when routing is ambiguous (two plausible repos, an unmatched person) or the input includes something sensitive (credentials, legal/financial commitments).

Step 4 — Execute

Issues — search for duplicates first (gh issue list --search), then file with gh issue create in the mapped repo. Title = imperative summary; body = context quote from the source, what was asked, and who asked. Apply issue_labels from config if set. Never assign anyone but the user.

Vault capture — one file per ingest, second-brain raw schema:

markdown
# message-jane-bulk-export (2026-09-04)
source: text message from Jane
project: Acme App

## Summary
…

## Decisions
…

## Action items
- [ ] mine: …
- [ ] theirs: Jane to …

## Filed
- myorg/acme-app#123 — Bulk-export button on the reports page

Slug: call- or message- + person + topic. Follow the vault's auto-commit convention (semantic commit per session).

Todos — action items of "mine" also land wherever the person's vault_page tracks tasks, if one is configured.

Reply draft — in the user's voice for that channel (config tone + channel norms: text = brief and casual; email = fuller). Structure when it fits: acknowledge → what I'm doing about it → what I need from you → when they'll hear back. End with the draft in a paste-ready block (compose with /paste rules for the channel — e.g. no URLs in an X post body). Never send it.

Step 5 — Report

Close with a compact recap: TLDR of the input (2–3 sentences), decisions, both action-item lists, links to filed issues, the vault file path, and the reply draft. This recap is the deliverable — someone who never saw the input should understand what happened and what's next.

Composes with

  • second-brain — captures land in its raw/ schema for later compile
  • paste — channel formatting for the reply draft
  • deep-research — when an extracted question needs real research before it's answerable
  • pm — when a call produces enough work to deserve a project card, not just issues

Notes on quality

  • The most common failure is flattening: summarizing the input instead of extracting work from it. The test: could the user act on your output without rereading the source?
  • Second most common: issue grab-bags. "Improvements from call with Jane" is not an issue. One item, one issue, one clear title.
  • "Theirs" action items are as valuable as "mine" — they're the follow-up ledger. Don't drop them because no tool call captures them.
  • A voice-note ingest with zero action items is fine: capture it, say so, stop. Not every input contains work.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ingest AI skill do?

When you paste raw human input — a call transcript (Grain, Zoom, Granola, Fathom), a text or email from a client/partner/friend, a voice-memo dump, or meeting notes — and want it converted into structured work. Extracts decisions, action items (yours vs theirs), bugs/feature requests, and facts worth keeping; files GitHub issues in the right repo, captures to the second-brain vault, and drafts (never sends) the reply. Triggers on "/ingest", "ingest this", "here's my call with X," "here's the transcript," "this is from [person]," "[person] asked me this," "from [person]:", a pasted transcrip...

Why use Ingest on TypingMind?

Because you install it once and use it with any model. 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 Ingest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/coreyhaines31/makerskills/tree/main/skills/ingest. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use 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 Ingest?

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

Is the Ingest AI skill free?

Yes. It is published on GitHub by coreyhaines31 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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