Grounded Vault logo

Grounded Vault

CommunityPopular
wshobson
grounded-vault

Use when maintaining a durable Markdown knowledge store that agents compile from sources, when every number or quote in a wiki page must trace back to an immutable source, or when compiled pages need cheap drift detection against the code they describe. Teaches the raw/wiki/archive layout, per-claim provenance links, and git fingerprints for zero-token staleness checks.

Overview

Publisherwshobson
Repositoryagents
Skill namegrounded-vault
Stars
40.1K
Forks
4.3K
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 wshobson on GitHub. Read the source before you install it.

Installation

Install the Grounded Vault 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/wshobson/agents/tree/main/plugins/documentation-standards/skills/grounded-vault
  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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/documentation-standards/skills/grounded-vault .claude/skills/grounded-vault
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grounded Vault 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 Grounded Vault 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 Grounded Vault 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.

Grounded Vault

A grounded vault is a three-layer Markdown store in which every compiled claim can be traced back to an immutable source and every page can be checked for staleness with one git diff. It needs a git repository and nothing else. The convention comes from the llm-wiki-loop project, which is a reference implementation rather than a dependency; this skill teaches the pattern so it works with plain files and whatever agent is in the session.

When to Use

  • An agent compiles notes, papers, transcripts, logs, or code into wiki pages that later sessions rely on.
  • A page cites numbers, dates, or quotes, and a reader must be able to verify each one against its source.
  • Pages describe code, and rereading the codebase every session to check whether they still hold is too expensive.
  • Knowledge must be corrected without losing history: superseded pages are archived, never deleted.

Session state, task queues, and conversation continuity are a different problem; use the context-management or conductor plugins for those. This skill is about provenance and drift on a durable knowledge store.

The three layers

LayerContentsWho writes itRule
raw/source material: notes, papers, transcripts, logs, exported datapeople and ingestion onlyimmutable once added; agents never edit a raw file
wiki/compiled pages built from raw/ and from codeagents and peopleevery number, date, and quote links to its source
archive/pages that drifted or were supersededagents, during garbage collectionmoved, never deleted; the header says why

Two files sit at the vault root. index.md is the map of every current page. log.md is an append-only record of what changed and why. Both change in the same commit as the page they describe.

Page header contract

Every wiki/ page opens with a header block:

markdown
# Authentication architecture

> Raw: [raw/notes/auth-v1.md](../raw/notes/auth-v1.md), [raw/adr/0007-jwt.md](../raw/adr/0007-jwt.md)
> Fingerprint: git:5b237fa
> Monitored: src/auth/jwt.ts, src/auth/session.ts, package.json
> Status: Current
  • Raw: lists every source the page was compiled from. Inline claims link to their specific source as well: Tokens expire after 15 minutes ([raw/adr/0007-jwt.md](../raw/adr/0007-jwt.md)).
  • Fingerprint: is the short commit hash the page was compiled against.
  • Monitored: lists the code paths the page describes. A change to any of them after the fingerprint means the page may be stale.
  • Status: is Current, Outdated (monitored code moved on), or Disputed (a newer source contradicts the page).

Grounding rule

A compiled page states only what a source supports, and every number, date, or quotation appears verbatim in the linked source. A synthesis says it is one and links its inputs. A gap in the sources is written into the page as a gap rather than filled by guessing.

Check it mechanically: for each linked claim, search the linked raw file for the exact figure or quoted phrase. A miss is a grounding error and blocks the commit. The script in references/details.md does this for a whole vault.

Drift detection

Compare the fingerprint with the current tree instead of rereading monitored code:

bash
git diff --stat 5b237fa..HEAD -- src/auth/jwt.ts src/auth/session.ts package.json

Empty output means the page still describes the code it was compiled against. Any output means recompile: reread only the changed files, update the page, and stamp the new fingerprint. The check runs in milliseconds and spends no model tokens.

Workflow

  1. Ingest. Put new material in raw/ under a dated or sourced filename. Never rewrite an existing raw file; add a new one beside it.

  2. Compile. Write or update the wiki/ page with the header block, a source link on every claim, and the fingerprint of the commit the code was read at.

  3. Check. Run the grounding check and the drift check before committing. Fix misses at the source; do not weaken a claim to make the check pass.

  4. Garbage collect. When drift or a contradicting source appears and the page is not recompiled now, change its status, move it to archive/, and record the reason:

    markdown
    > Status: Outdated
    > Reason: src/auth/session.ts changed after git:5b237fa; see log.md 2026-09-01
  5. Update the map. Every add, move, or archive updates index.md and appends one line to log.md in the same commit.

Commit gate

Run both checks from a pre-commit hook or a CI step so a page cannot land with an unverifiable number or a stale fingerprint:

bash
python3 scripts/check_vault.py --strict   # exits 1 on any grounding miss or drifted page

Going deeper

references/details.md covers: the vault check script, batching the drift check across pages, templates for index.md and log.md, renamed or deleted monitored files, sources that are binary or live at external URLs, and the reference implementation.

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 Grounded Vault AI skill do?

Use when maintaining a durable Markdown knowledge store that agents compile from sources, when every number or quote in a wiki page must trace back to an immutable source, or when compiled pages need cheap drift detection against the code they describe. Teaches the raw/wiki/archive layout, per-claim provenance links, and git fingerprints for zero-token staleness checks.

Why use Grounded Vault on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/documentation-standards/skills/grounded-vault. 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 Grounded Vault?

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 Grounded Vault?

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

Is the Grounded Vault AI skill free?

Yes. It is published on GitHub by wshobson 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇