Aris Research Wiki logo

Aris Research Wiki

OrganizationPopular
OpenLAIR
aris-research-wiki

Persistent research knowledge base that accumulates papers, ideas, experiments, claims, and their relationships across the entire research lifecycle. Inspired by Karpathy's LLM Wiki pattern. Use when user says "知识库", "research wiki", "add paper", "wiki query", "查知识库", or wants to build/query a persistent field map.

Overview

PublisherOpenLAIR
Repositorydr-claw
Skill namearis-research-wiki
Stars
1.2K
Forks
136
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Aris Research 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/OpenLAIR/dr-claw/tree/main/skills/aris-research-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/OpenLAIR/dr-claw.git /tmp/dr-claw
mkdir -p .claude/skills
cp -r /tmp/dr-claw/skills/aris-research-wiki .claude/skills/aris-research-wiki
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aris Research 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 Aris Research 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 Aris Research 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.

Research Wiki: Persistent Research Knowledge Base

Subcommand: $ARGUMENTS

Overview

The research wiki is a persistent, per-project knowledge base that accumulates structured knowledge across the entire ARIS research lifecycle. Unlike one-off literature surveys that are used and forgotten, the wiki compounds — every paper read, idea tested, experiment run, and review received makes the wiki smarter.

Inspired by Karpathy's LLM Wiki pattern: compile knowledge once, keep it current, don't re-derive on every query.

Core Concepts

Four Entity Types

EntityDirectoryNode ID formatWhat it represents
Paperpapers/paper:<slug>A published or preprint research paper
Ideaideas/idea:<id>A research idea (proposed, tested, or failed)
Experimentexperiments/exp:<id>A concrete experiment run with results
Claimclaims/claim:<id>A testable scientific claim with evidence status

Typed Relationships (graph/edges.jsonl)

Edge typeFrom → ToMeaning
extendspaper → paperBuilds on prior work
contradictspaper → paperDisagrees with results/claims
addresses_gappaper|idea → gapTargets a known field gap
inspired_byidea → paperIdea sourced from this paper
tested_byidea|claim → expTested in this experiment
supportsexp → claim|ideaExperiment confirms claim
invalidatesexp → claim|ideaExperiment disproves claim
supersedespaper → paperNewer work replaces older

Edges are stored in graph/edges.jsonl only. The ## Connections section on each page is auto-generated from the graph — never hand-edit it.

Wiki Directory Structure

research-wiki/
  index.md               # categorical index (auto-generated)
  log.md                 # append-only timeline
  gap_map.md             # field gaps with stable IDs (G1, G2, ...)
  query_pack.md          # compressed summary for /aris-idea-creator (auto-generated, max 8000 chars)
  papers/
    <slug>.md            # one page per paper
  ideas/
    <idea_id>.md         # one page per idea
  experiments/
    <exp_id>.md          # one page per experiment
  claims/
    <claim_id>.md        # one page per testable claim
  graph/
    edges.jsonl          # materialized current relationship graph

Subcommands

/aris-research-wiki init

Initialize the wiki for the current project:

  1. Create research-wiki/ directory structure
  2. Create empty index.md, log.md, gap_map.md
  3. Create empty graph/edges.jsonl
  4. Log: "Wiki initialized"

/aris-research-wiki ingest "<paper title>" — arxiv: <id>

Add a paper to the wiki:

  1. Fetch metadata — use arXiv/DBLP/Semantic Scholar to get full metadata
  2. Generate slug — <first_author_last_name><year>_<keyword> (e.g., chen2025_factorized_gap)
  3. Check dedup — if paper:<slug> already exists, update instead of creating
  4. Create page — papers/<slug>.md with full schema (see below)
  5. Extract relationships — scan the paper's related work / method for connections to existing wiki pages
  6. Add edges — append to graph/edges.jsonl
  7. Update index — regenerate index.md
  8. Update gap_map — if the paper reveals new gaps or addresses existing ones
  9. Rebuild query_pack — regenerate query_pack.md
  10. Log — append to log.md

Paper page schema:

markdown
---
type: paper
node_id: paper:<slug>
title: "<full title>"
authors: ["First A. Author", "Second B. Author"]
year: 2025
venue: arXiv
external_ids:
  arxiv: "2501.12345"
  doi: null
  s2: null
tags: [tag1, tag2]
relevance: core  # core | related | peripheral
origin_skill: research-lit
created_at: 2026-04-07T10:12:00Z
updated_at: 2026-04-07T10:12:00Z
---

# One-line thesis

[Single sentence capturing the paper's core contribution]

## Problem / Gap

## Method

## Key Results

## Assumptions

## Limitations / Failure Modes

## Reusable Ingredients

[Techniques, datasets, or insights that could be repurposed]

## Open Questions

## Claims

[Reference claim pages: claim:C1, claim:C2, etc.]

## Connections

[AUTO-GENERATED from graph/edges.jsonl — do not edit manually]

## Relevance to This Project

[Why this paper matters for our specific research direction]

/aris-research-wiki query "<topic>"

Generate query_pack.md — a compressed, context-window-friendly summary:

Fixed budget (max 8000 chars / ~2000 tokens):

SectionBudgetContent
Project direction300 charsFrom CLAUDE.md or RESEARCH_BRIEF.md
Top 5 gaps1200 charsFrom gap_map.md, ranked by: unresolved + linked ideas + failed experiments
Paper clusters1600 chars3-5 clusters by tag overlap, 2-3 sentences each
Failed ideas1400 charsAlways included — highest anti-repetition value
Top papers1800 chars8-12 pages ranked by: linked gaps, linked ideas, centrality, relevance flag
Active chains900 charslimitation → opportunity relationship chains
Open unknowns500 charsUnresolved questions across the wiki

Pruning priority (when over budget): low-ranked papers > cluster detail > chain detail. Never prune failed ideas or top gaps first.

Key rule: Read from short fields only (frontmatter, one-line thesis, gap summary, failure note). Do not summarize full page bodies every time.

/aris-research-wiki update <node_id> — <field>: <value>

Update a specific entity:

/aris-research-wiki update paper:chen2025 — relevance: core
/aris-research-wiki update idea:001 — outcome: negative
/aris-research-wiki update claim:C1 — status: invalidated

After any update: rebuild query_pack.md, update log.md.

/aris-research-wiki lint

Health check the wiki:

  1. Orphan pages — entities with zero edges
  2. Stale claims — claims with status: reported older than 14 days
  3. Contradictions — claims with both supports and invalidates edges
  4. Missing connections — papers sharing 2+ tags but no explicit relationship
  5. Dead ideas — stage: proposed ideas that were never tested
  6. Sparse pages — pages with 3+ empty sections

Output a LINT_REPORT.md with suggested fixes.

/aris-research-wiki stats

Quick overview:

📚 Research Wiki Stats
Papers: 28 (12 core, 10 related, 6 peripheral)
Ideas: 7 (2 active, 3 failed, 1 partial, 1 succeeded)
Experiments: 12
Claims: 15 (5 supported, 3 invalidated, 7 reported)
Edges: 64
Gaps: 8 (3 unresolved)
Last updated: 2026-04-07T10:12:00Z

Integration with Existing Workflows

Hook 1: After /aris-research-lit finds papers

# At end of research-lit, after synthesis:
if research-wiki/ exists:
    for paper in top_relevant_papers (limit 8-12):
        /aris-research-wiki ingest paper
        for each gap identified:
            add_edge(paper.node_id, gap_id, "addresses_gap")
        for each explicit relation to existing wiki paper:
            add_edge(paper.node_id, target.node_id, relation_type)
    rebuild query_pack
    log "research-lit ingested N papers"

Hook 2: /aris-idea-creator reads AND writes wiki

Before ideation:

if research-wiki/query_pack.md exists (and < 7 days old):
    prepend query_pack to landscape context
    treat failed ideas as banlist
    treat top gaps as search seeds
    still run fresh literature search for last 3-6 months

After ideation (THIS IS CRITICAL — without it, ideas/ stays empty):

for idea in all_generated_ideas (recommended + killed):
    /aris-research-wiki upsert_idea(idea)
    for paper_id in idea.based_on:
        add_edge(idea.node_id, paper_id, "inspired_by")
    for gap_id in idea.target_gaps:
        add_edge(idea.node_id, gap_id, "addresses_gap")
rebuild query_pack
log "idea-creator wrote N ideas to wiki"

Hook 3: After /aris-result-to-claim verdict

# Create experiment page
exp_id = upsert_experiment(experiment_data)

# Update each claim's status
for claim_id in resolved_claims:
    if verdict == "yes":
        set_claim_status(claim_id, "supported")
        add_edge(exp_id, claim_id, "supports")
    elif verdict == "partial":
        set_claim_status(claim_id, "partial")
        add_edge(exp_id, claim_id, "supports")  # partial
    else:
        set_claim_status(claim_id, "invalidated")
        add_edge(exp_id, claim_id, "invalidates")

# Update idea outcome
update_idea(active_idea_id, outcome=verdict)

# If failed, record WHY for future ideation
if verdict in ("no", "partial"):
    update_idea failure_notes with specific metrics and reasons

rebuild query_pack
log "result-to-claim: exp_id updated, verdict=..."

Re-ideation Trigger

After significant wiki updates, suggest re-running /aris-idea-creator:

  • ≥5 new papers ingested since last ideation
  • ≥3 new failed/partial ideas since last ideation
  • New contradiction discovered in the graph
  • New gap identified that no existing idea addresses

The system suggests but does not auto-trigger. User decides.

Key Rules

  • One source of truth for relationships: graph/edges.jsonl. Page Connections sections are auto-generated views.
  • Canonical node IDs everywhere: paper:<slug>, idea:<id>, exp:<id>, claim:<id>, gap:<id>. Never use raw titles or inconsistent shorthands.
  • Failed ideas are the most valuable memory. Never prune them from query_pack.
  • query_pack.md is hard-budgeted at 8000 chars. Deterministic generation, not open-ended summarization.
  • Append to log.md for every mutation. The log is the audit trail.
  • Reviewer independence applies. When the wiki is read by cross-model review skills, pass file paths only — do not summarize wiki content for the reviewer.

Acknowledgements

Inspired by Karpathy's LLM Wiki — "compile knowledge once, keep it current, don't re-derive on every query."

Frequently asked questions

What does the Aris Research Wiki AI skill do?

Persistent research knowledge base that accumulates papers, ideas, experiments, claims, and their relationships across the entire research lifecycle. Inspired by Karpathy's LLM Wiki pattern. Use when user says "知识库", "research wiki", "add paper", "wiki query", "查知识库", or wants to build/query a persistent field map.

Why use Aris Research Wiki on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-research-wiki. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aris Research 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 Aris Research Wiki?

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

Is the Aris Research Wiki AI skill free?

Yes. It is published on GitHub by OpenLAIR 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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