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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.

Install / Use

npx skills add coreyhaines31/makerskills --skill ingest

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Supported Platforms

Universal

Our assessment of ingest

ingest scores 87/100 on our quality scale, 298th of 434 Communication skills we index.

Its SKILL.md is 7.1 KB long, well organised into 15 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

It has 824 GitHub stars, a meaningful sign that others use it.

Substance
29/30
Structure
17/20
Description
15/15
Adoption
12/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 32 days ago, so ingest is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-10-07. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

ingest compared with similar skills

All 4 of these similar skills score higher than ingest; compare them before choosing.

SkillScoreStarsUpdatedFormat
ingest (this skill)by coreyhaines318782432d agoSKILL.md
Agent-Reachby Panniantong10093.0k22d agoCLAUDE.md
LocalAIby mudler10049.4ktodayMCP Server
algorithmic-artby anthropics100177.9k15d agoSKILL.md
pptxby anthropics100177.9k15d agoSKILL.md

Frequently asked questions

How do I install ingest?
Run npx skills add coreyhaines31/makerskills --skill ingest. The install tabs above show the steps for each supported agent.
Which AI agents does ingest work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is ingest safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is ingest still maintained?
The repository was last updated 32 days ago, so ingest is actively maintained.

name: ingest description: 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. metadata: version: 0.1.0

/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

| Path | What | |---|---| | ${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/ingest/people.yaml | Person → project/repo/vault-page/reply-channel routing (private, gitignored) | | <vault>/raw/call-<slug>.md / <vault>/raw/message-<slug>.md | Captures, in second-brain's schema | | references/people.yaml.example | Config 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) → ask.

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

| Shape | Type | |---|---| | Speaker labels + timestamps, or a Grain/Zoom/Granola/Fathom URL or header | call | | "from X:" / forwarded text or DM, first-person, short | message | | Email headers or greeting/sign-off structure | email (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:

| # | Item | Destination | |---|---|---| | 1 | "Bulk-export button on the reports page" | Issue → myorg/acme-app | | 2 | Decision: monthly billing default | vault Projects/Acme.md | | 3 | Reply to Jane | draft 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:

# 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.

Related Skills

View on GitHub
GitHub Stars824
CategoryCommunication
Updated1mo ago
Forks67

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions