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Consolidate Notes

OrganizationPopular
inkeep
consolidate-notes

Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.

Overview

Publisherinkeep
Repositoryopen-knowledge
Skill nameconsolidate-notes
Stars
4.2K
Forks
279
Bundled files
Instructions only
LicenseGPL-3.0
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 inkeep on GitHub. Read the source before you install it.

Installation

Install the Consolidate Notes 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/inkeep/open-knowledge.git /tmp/open-knowledge
mkdir -p .claude/skills
cp -r /tmp/open-knowledge/packages/server/assets/skills/packs/knowledge-base/consolidate .claude/skills/consolidate-notes
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Consolidate Notes 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 Consolidate Notes 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 Consolidate Notes 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.

Consolidate — promote research into a canonical article

This skill is pack guidance. The platform /open-knowledge skill (read/write/preview/linking/grounding rules) still governs every markdown operation — this layers the procedure on top.

Promote existing research on a topic into a canonical article under articles/. Canonical, not provisional — the output is the source of truth for future agents, not a snapshot of uncertainty.

The content directory is the resolved content.dir — read it with config({ key: 'content.dir' }) if you don't already know it. Paths below are relative to it.

Legacy reads: Existing research and articles may use status: provisional / canonical, and existing sources: may contain string paths. Treat those as draft / stable and source resources. Do not mass-rewrite them; new writes use the OKF forms below.

STOP gate: has a decision actually been made?

Consolidation is promotion, not creation. If the team hasn't decided, the resulting "canonical" article lies about the team's state of understanding — future agents read it, act on it, and the false certainty compounds.

Before any write, confirm out loud with the user:

  • What is the actual decision? (e.g., "We chose Yjs for CRDT" — not "Yjs is one option")
  • What alternatives were considered and rejected? (these go in "Alternatives considered," not as equals)
  • What's the rationale the team used? (not your reconstruction from sources)

If the decision is still open, do not consolidate. Tell the user: "The research is still provisional. When the team decides, come back and consolidate with the outcome." Then stop.

When to use this procedure

  • A team has made a decision after research and wants the outcome committed as canonical knowledge
  • You want to compact several provisional research notes into one authoritative article
  • A developer asks to "consolidate" or "finalize" the knowledge on a topic

Do NOT consolidate when:

  • The team has not actually decided (the output would be misleading — keep it as research)
  • You have not read the underlying sources (the output would lack evidence)

Principle: canonical, not provisional

A consolidated article is the source of truth. Agents reading it should not need to dig further for context — it should stand on its own. That means:

  • Clear, direct statements (no "tentative", no "initial findings")
  • Decisions stated as decisions, not options
  • Rationale explained so future readers understand the why
  • Trade-offs acknowledged but framed against the chosen path, not as a menu
  • Evidence linked but not the whole story — this article is the destination, not a trail

Steps

1. Load the research + sources

Locate research articles on this topic:

  • Use exec("grep -rn <topic-keyword> <content-dir>") to find prior research, or exec("ls -A research") if the project groups research in a known location
  • Read each research article fully via exec("cat <path>") (rich enrichment gives frontmatter + shadow-repo activity + project git history + backlinks)
  • Follow its sources: frontmatter list — read every referenced source file
  • Also read any existing canonical article on the topic — if one already exists, you may be updating it rather than creating a new one

If there is no research to consolidate, stop. Consolidation is promotion, not creation. Do the /research-with-sources skill first.

2. Re-confirm the decision

You already confirmed the decision at the STOP gate at the top. This step is a brief re-check after loading the research in Step 1 — occasionally the research surfaces something that makes the "decision" look less decided than the user initially claimed (an un-rebutted open question, an alternative they forgot about). If the loaded research reveals that, pause and re-confirm with the user before writing.

3. Write the canonical article

Persist as you go (MUST). For a large consolidation drawing on several research docs, create the article skeleton — frontmatter + the headings below — first, then edit each section in as you finish it; don't hold the whole synthesis in context for one final write. A rate limit or crash mid-synthesis then costs you one section, not the entire article. (The platform skill's Writing section carries this rule for all long-running work: the knowledge base is your checkpoint.)

Save inside the content directory. Path convention depends on the project:

  • If the project uses the three-layer lifecycle (external-sources/research/articles/), save under articles/, grouped by topic subfolder when the area is broad (e.g., articles/editor/crdt-architecture.md)
  • If the project has an existing canonical-docs layout (docs/, guides/, etc.), save there in a location that matches the project's conventions
  • Ask the user when the canonical location is ambiguous

Frontmatter:

yaml
---
title: Descriptive title
description: One-line summary of what this article covers
type: article
status: stable
date: YYYY-MM-DD
tags:
  - article
  - canonical
  - topic-tag
supersedes:
  - <path-to-research-article>.md
---

Structure:

markdown
## Summary

[One paragraph: what the decision is and why. A reader who reads only this paragraph should know the outcome.]

## Context

[What problem does this solve? What constraints shaped the decision?]

## Decision

[The chosen approach, stated directly. Not "we recommend" — "we chose".]

## Rationale

[Why this path over alternatives. Grounded in the constraints from Context.]

## Trade-offs

[What we gave up by choosing this path. Frame against the chosen decision, not as a menu.]

## Alternatives considered

[Briefly: what else was on the table, why it was rejected. Link to the research article for deeper analysis.]

## Implementation notes

[How this gets realized in the codebase — key files, patterns, gotchas.]

## Further reading

[Links to research articles and external sources for readers who want the trail.]

4. Link aggressively

Canonical articles are destinations — they should be linked heavily from everywhere they're relevant and link out to every related page themselves. Underlinked canonical articles lose most of their value.

  • Inside this article: every noun-phrase that names another document (other canonical articles, related research, external-source pages, sibling topics) should be a standard markdown link, not plain prose.
  • Every link must resolve. Only link to docs that exist. If you mention a concept that should have its own page but doesn't yet, do NOT emit a broken link — either create that page in this pass, or record it as a tracked task (your host's task tool; if the host has none, tell the user) and leave the mention as plain prose. A broken link is debt, not a to-do marker.
  • Update neighbors. After writing, find 2-3 closely-related existing pages (via exec("grep -rn <topic> <content-dir>")) and add a link to the new article from each — usually under a "See also" section or inline where the new article is relevant. This makes the article discoverable via backlinks, not just by remembering the path.
  • Link to the sources and superseded research from "Further reading" — readers who want the trail can follow.

5. Supersede the research

Add a supersedes: list in the new article's frontmatter pointing at the research article(s) it consolidates. This creates an audit trail.

Do NOT delete the research articles — they remain as historical context for how the decision was reached. Edit their frontmatter to add:

yaml
status: deprecated
superseded_by: <path-to-new-canonical-article>.md

6. Verify

  • File exists at the chosen path under the content directory
  • Has type: article and status: stable frontmatter
  • Lists the research articles it supersedes
  • Research articles updated with status: deprecated and a superseded_by pointer
  • exec("ls -A <target-dir>") shows the new file

Non-goals

  • Don't consolidate research that hasn't reached a decision — the article would misrepresent the team's actual state of understanding
  • Don't delete research articles — they are the trail; keep them with a superseded_by marker
  • Don't rewrite research prose verbatim — canonical articles have a different voice (direct, decided) than research (exploratory, provisional)
  • Don't skip the supersedes / superseded_by links — the audit trail matters for future readers

Frequently asked questions

What does the Consolidate Notes AI skill do?

Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.

Why use Consolidate Notes on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/inkeep/open-knowledge/tree/main/packages/server/assets/skills/packs/knowledge-base/consolidate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Consolidate Notes?

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 Consolidate Notes?

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

Is the Consolidate Notes AI skill free?

Yes. It is published on GitHub by inkeep under the GPL-3.0 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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