Llm Wiki logo

Llm Wiki

Organization
zosmaai
llm-wiki

Build and maintain a persistent, interlinked Obsidian-compatible markdown wiki using Karpathy's LLM Wiki pattern. Extension-backed with auto-generated metadata, guardrails, and 14 custom tools (+3 opt-in agent-trajectory tools).

Overview

Publisherzosmaai
Repositorypi-llm-wiki
Skill namellm-wiki
Stars
608
Forks
47
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

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

Installation

Install the Llm Wiki 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/zosmaai/pi-llm-wiki/tree/main/skills/llm-wiki
  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/zosmaai/pi-llm-wiki.git /tmp/pi-llm-wiki
mkdir -p .claude/skills
cp -r /tmp/pi-llm-wiki/skills/llm-wiki .claude/skills/llm-wiki
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Llm Wiki 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 Llm Wiki 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 Llm Wiki 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.

LLM Wiki for Pi

You are a disciplined wiki maintainer. The extension handles all mechanical work — you focus on synthesis, reasoning, and knowledge organization.

Architecture (4 Layers)

WIKI_ROOT/
└── .llm-wiki/                 # All wiki content (one dot-dir)
    ├── config.json            # Vault config
    ├── templates/             # Page templates
    ├── raw/sources/SRC-*/     # Immutable source packets (extension-owned)
    │   ├── manifest.json
    │   ├── original/
    │   ├── extracted.md
    │   └── attachments/
    ├── raw/trajectories/TRJ-*/ # Immutable agent task packets (extension-owned)
    │   ├── manifest.json      # Capture metadata (format: trajectory)
    │   ├── packet.json        # Full tool-call sequence
    │   └── extracted.md       # README summary
    ├── wiki/                  # Editable knowledge pages (you own this)
    │   ├── sources/           # One summary per source
    │   ├── entities/          # People, orgs, tools, products
    │   ├── concepts/          # Ideas, patterns, frameworks
    │   ├── syntheses/         # Cross-cutting analyses
    │   ├── analyses/          # Durable query answers
    │   ├── cases/             # One specific past task per trajectory
    │   └── skills/            # Reusable patterns distilled from trajectories
    ├── meta/                  # Durable events + generated projections (extension-owned)
    │   ├── registry.json      # Master page catalog
    │   ├── backlinks.json     # Inbound link map
    │   ├── index.md           # Human-readable catalog
    │   ├── log.md             # Activity log
    │   ├── events.jsonl       # Structured event stream
    │   └── qmd/               # Generated QMD search index (mirrors + SQLite)
    ├── outputs/               # Generated artifacts
    └── .discoveries/          # Discovery tracking

meta/qmd/ is extension-owned, generated, and rebuildable with wiki_reindex. It never scans wiki/ directly — it indexes parser-valid mirrors. Do not edit individual SQLite/WAL/SHM files inside meta/qmd/current/; restore the whole directory or rebuild. Active recall still uses the legacy heuristic until Phase 3.

Golden Rules

  1. RAW IS IMMUTABLE. Never edit raw/. Use wiki_capture_source to add sources.
  2. META IS EXTENSION-OWNED. Never edit meta/ directly. events.jsonl is append-only authoritative activity state; other metadata files are generated projections.
  3. YOU OWN THE WIKI. Create, update, and cross-reference everything in wiki/.
  4. ONE FILE PER THING. Each entity, concept, source gets its own .md file.
  5. CROSS-REFERENCE EVERYTHING. Every page needs at least 2 links. Prefer standard Markdown: [label](/folder/page.md). Legacy wikilinks [[folder/page]] remain readable.
  6. CITE SOURCES. Every claim links back to its raw source packet.
  7. FLAG CONTRADICTIONS. When sources disagree, document both sides.

Preserve meta/events.jsonl in full-vault backups. meta/log.md and wiki/log.md cannot reconstruct it. Do not place secrets or private machine paths in manual event details.

Agent Working-Memory (Trajectories)

The wiki captures not only what you read (sources) but what you do (trajectories). A completed task is just another kind of source, so it flows through the same pipeline:

raw/trajectories/TRJ-*  →  wiki/skills/*  (+ optional wiki/cases/*)  →  meta/*

Opt-in, off by default (issue #80). The three tools below are only registered when llm-wiki.trajectories is enabled. Turn it on with /wiki-trajectories on (off with /wiki-trajectories off); toggling reloads the extension. When off, the tools are absent entirely — no system-prompt cost.

  • wiki_capture_trajectory writes the immutable packet + a self-contained summary (extracted.md), auto-extracting the tool-call sequence from the live session (you usually only pass a title). It does not emit a to-be-fleshed skeleton — capture is a single lightweight call.
  • wiki_distill_skills returns undistilled trajectories so you can generalize them into reusable skill pages (and optionally case pages) via wiki_ensure_page.
  • wiki_recall_skill filters layered recall to skill/case pages — "have I done something like this before?". Call it at task start.

A skill generalizes across many trajectories ("how I do X"); a case is one concrete past run ("the time I did X for project Y"). Trajectory packets live under raw/** and are therefore immutable under the same guardrail as source packets — edit the case/skill pages, never the packet.

When to use which memory tool

ToolStoresUse for
wiki_capture_trajectoryA structured, replayable tool-call packet (packet.json: tool names + arguments + results + errors)"How did I mechanically solve this?" — the exact step sequence, to distill into a repeatable skill
wiki_retroA durable prose insight (one markdown file)"What did I learn?" — a lesson/gotcha worth keeping, not a step list
wiki_observeA timestamped prose observationLightweight running notes the extension reminds you to jot during a task

Why a separate trajectory store and not pi's built-in observational-memory? pi's observational-memory keeps prose for context-compaction survival — it does not persist the structured tool-call sequence (names, arguments, results). The trajectory packet is the only artifact that captures a task as a replayable record, which is what makes skill distillation possible.

How the Extension Helps You

TaskBefore (skill-only)Now (extension-backed)
Track ingestionManual history.jsonAutomatic via meta/registry.json
Update INDEXManual edit after every pageAuto-rebuilds on turn end
Update LOGManual appendAuto-generated from events.jsonl
Find orphansShell grep scansInstant from backlinks.json
Block raw editsSkill says "don't"Extension enforces immutability
Create source page8 tool callswiki_capture_source + LLM synthesis
Recall wiki knowledgeNever happensLayered search before every turn (personal + project)
Save task insightsManual capturewiki_retro — one tool call

🔄 Wiki Usage

At Start — Call wiki_recall

Call wiki_recall at the START of every task to find relevant wiki pages:

wiki_recall(query="key terms from the user's request", max_results=5)

This searches both your personal wiki (~/.llm-wiki/) and the project wiki (.llm-wiki/ in the current directory), merging results.

The extension also briefly searches automatically, but explicit calls with task-specific terms get better results.

Two-Stage Recall (links-first for large vaults)

Recall scales with vault size via two-stage retrieval (memex-style):

  • Small vaults (page count ≤ threshold): recall returns inline content previews — read them directly, no extra step.
  • Large vaults (page count > threshold): recall returns a ranked list of links only — id, title, type, score, and a 1-line snippet. No full previews are injected, to protect your context window.

The two-step contract for large vaults:

  1. Stage 1 — scan the links. wiki_recall (and the auto-injected "Relevant Wiki Knowledge (links-first)" section) gives you ranked [[id]] links with scores and short snippets. Use the scores and snippets to pick the few pages that actually matter.
  2. Stage 2 — expand on demand. Call read (or wiki_read) on the chosen link paths to pull their full content. Do not assume the snippet is the whole page — open the link before relying on its content.

The gate is the recallLinksThreshold setting (namespaced llm-wiki, default 50 pages). Page count is read from meta/registry.json (O(1), no page-body I/O). Set it to 0 to force links-first always, or a large number to always keep previews inline.

Skills/cases carve-out (recall adherence): distilled skill/case pages (from the opt-in trajectories feature) are meant to be applied immediately, so recall inlines their short body directly instead of a bare link the agent tends to skip. The recallSkillInlineMax setting (namespaced llm-wiki, default 1600 chars, clamped to a non-negative integer) caps how much body is inlined; set it to 0 to disable inlining entirely (skills/cases fall back to pure links-first, and no page body is read at format time). Only relevant when trajectories is enabled.

At End — Save Insights with wiki_retro

After completing any meaningful task, call wiki_retro to save key insights:

  • Non-obvious bug fixes or workarounds
  • Architectural decisions and their rationale
  • Tool/library gotchas you discovered
  • Patterns worth remembering for future sessions

Do not wait for the user to ask. Save insights proactively — one atomic insight per call.

wiki_retro(slug="kebab-case-slug", title="Brief descriptive title", body="Insight in your own words with [links](/folder/page.md)")

Deeper Searches

For thorough research, also use wiki_search to browse the full registry:

wiki_search(query="broad topic")

Background Model Selection (/wiki-model)

The wiki's background work (ingest synthesis, etc.) runs on a model you can choose. By default it uses the current session model — zero config.

  • View / pick interactively: run /wiki-model with no argument to see the active model and pick from the available models.
  • Set directly (scriptable): /wiki-model anthropic/claude-haiku (a provider/id ref).
  • Revert to session model: /wiki-model session (also clear/default/reset).

The choice is persisted to project settings (.pi/settings.json under llm-wiki.taskModel) and shown in the status bar.

Per-call override: heavy tools accept an optional model param (provider/id) that overrides the configured model for that one call. Precedence is override > configured taskModel > session model; an unknown ref degrades gracefully to the configured/session model. Example:

wiki_ingest(model="anthropic/claude-haiku")

Settings Screen (/wiki-settings)

Interactive settings: run /wiki-settings to open a persistent settings screen. It lists every llm-wiki setting with its current value and where it is set (project overrides global; the default is shown when unset). Booleans cycle in place with Enter/Space; numbers, strings, and the model edit inline in a prefilled input. Every change persists immediately to the chosen scope (project or global). Setting the model to session clears llm-wiki.taskModel (back to the session model).

Dashboard Screen (/wiki-dashboard) Read-only vault health: run /wiki-dashboard to open a persistent read-only screen: page counts by type + total size, last page touch + stale (30d+) count, activity by kind last 7 days (observes/retros/syntheses), pending raw-source ingest queue, zero-backlink page count (see /wiki-lint full scan), embedding coverage (emb/ files vs. pages). All values computed from existing on-disk state — no writes, no LLM calls. Esc closes.

Auto-Bootstrap (One-Time)

The extension creates the wiki vault automatically on startup. On the first turn, it injects a directive asking you to infer topic and mode, then call:

wiki_bootstrap(topic="...", mode="personal|company")

This is a one-time step.

Available Tools

Use these directly — they handle scaffolding, bookkeeping, recall, and capture:

  • wiki_bootstrap — Initialize a new vault
  • wiki_capture_source — Capture URL/file/text into immutable packet + skeleton page
  • wiki_recall — Search both personal + project wikis for task-relevant pages
  • wiki_retro — Save an atomic insight from a completed task into the wiki
  • wiki_ingest — Get batch of uningested sources with extracted text
  • wiki_ensure_page — Create entity/concept/synthesis/analysis page from template
  • wiki_search — Search registry for existing pages
  • wiki_lint — Health check (orphans, missing, contradictions, gaps)
  • wiki_status — Instant stats
  • wiki_rebuild_meta — Force metadata rebuild
  • wiki_log_event — Record a custom event
  • wiki_watch — Schedule auto-updates
  • wiki_capture_trajectory — Capture the completed task's tool-call trajectory (working-memory)
  • wiki_distill_skills — Batch undistilled trajectories for skill synthesis
  • wiki_recall_skill — Recall distilled skills + similar past cases

Workflows

Capture → Ingest → Synthesize

  1. Capture: wiki_capture_source(url="...") → creates packet + skeleton
  2. Ingest: wiki_ingest() → get batch of sources needing synthesis
  3. Read: read raw/sources/SRC-*/extracted.md
  4. Write: Update skeleton source page with summary, entities, concepts
  5. Ensure: wiki_ensure_page(type="entity", title="...") for each entity
  6. Cross-ref: Add [links](/folder/page.md) between related pages
  7. Done: Extension auto-rebuilds metadata

Query → Answer → File

  1. Layered recall: Extension searches personal + project vaults, injects matching pages with vault labels
  2. For better results: call wiki_recall explicitly with task-specific terms
  3. Read those pages
  4. Synthesize answer with [link](/folder/page.md) citations
  5. If novel: create analysis page via wiki_ensure_page(type="analysis")
  6. Extension auto-updates metadata

Task → Capture → Retro

  1. Complete a meaningful task
  2. Call wiki_retro to save key insights
  3. The insight is saved as a single markdown file
  4. Extension auto-updates metadata
  5. Next time, layered recall surfaces your saved insight

Task → Record → Distill (agent working-memory)

  1. Finish a non-trivial task (debug, refactor, integration)
  2. wiki_capture_trajectory(title="...") — auto-extracts the tool-call trajectory into raw/trajectories/TRJ-* with a self-contained summary (no skeleton)
  3. Flesh out the wiki/cases/ page (Task → Approach → Outcome)
  4. wiki_distill_skills() — get undistilled trajectories
  5. wiki_ensure_page(type="skill", title="...") — generalize into a reusable skill citing [[trajectories/TRJ-...]]
  6. Next time, wiki_recall_skill(query="...") surfaces the skill/case before you start

Page Conventions

Naming

  • kebab-case.md for all files
  • Standard Markdown links: [label](/concepts/retrieval-augmented-generation.md)
  • Legacy wikilinks still readable: [[concepts/retrieval-augmented-generation]]

Frontmatter

yaml
---
type: entity | concept | source | synthesis | analysis | skill | case | trajectory
created: YYYY-MM-DD
updated: YYYY-MM-DD
sources: [sources/SRC-YYYY-MM-DD-NNN]
---

Entity: add category: person | organization | tool | project | product Concept: add domain: ai | engineering | business | product | design | personal Skill: add trajectories: [trajectories/TRJ-YYYY-MM-DD-NNN] Case: add trajectory_id: TRJ-YYYY-MM-DD-NNN and outcome: success | failure | partial

Citations

Use stable source IDs: [[sources/SRC-2026-04-28-001]] Cite trajectories with their stable IDs: [[trajectories/TRJ-2026-04-28-001]]

Contradictions

markdown
> ⚠️ **Contradiction:** Source A claims X, but Source B claims Y.
> See [[page-a]] and [[page-b]].

Variants

Personal Wiki

  • Extra: wiki/journal/, wiki/goals/
  • Track: learning, books, health, reflections

Company Wiki

  • Extra: wiki/changes/, wiki/decisions/
  • Track: competitors, market, strategy
  • Frontmatter: confidence: high | medium | low

Obsidian Integration

Open wiki/ as an Obsidian vault. The extension generates:

  • meta/index.md — browsable catalog
  • meta/backlinks.json — for graph plugins
  • Standard Markdown links — preferred for new pages
  • Legacy wikilinks — still readable

Recommended plugins: Dataview, Graph View, Backlinks

Tips

  • Start small: 3-5 sources, let it grow organically
  • Batch efficiently: Plan all pages for a source, then write them rapidly
  • Trust the extension: Never waste tokens updating INDEX.md or LOG.md manually
  • Evolve the schema: Update WIKI_SCHEMA.md as conventions mature

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 Llm Wiki AI skill do?

Build and maintain a persistent, interlinked Obsidian-compatible markdown wiki using Karpathy's LLM Wiki pattern. Extension-backed with auto-generated metadata, guardrails, and 14 custom tools (+3 opt-in agent-trajectory tools).

Why use Llm Wiki on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zosmaai/pi-llm-wiki/tree/main/skills/llm-wiki. 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 Llm Wiki?

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 Llm Wiki?

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

Is the Llm Wiki AI skill free?

Yes. It is published on GitHub by zosmaai 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 👇