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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

Install / Use

npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-wiki

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

98/100

Supported Platforms

OpenAI Codex

Our assessment of research-wiki

research-wiki scores 98/100 on our quality scale, 35th of 794 AI & Machine Learning skills we index (top 5%).

Its SKILL.md is 20 KB long, well organised into 54 sections with 13 code examples: a thorough specification that gives an agent plenty to work with.

With 16,644 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 9 days ago, so research-wiki 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.

research-wiki compared with similar skills

All 4 of these similar skills score higher than research-wiki; compare them before choosing.

SkillScoreStarsUpdatedFormat
research-wiki (this skill)by wanshuiyin9816.6k9d agoSKILL.md
claude-memby thedotmack10094.8ktodayCLAUDE.md
Agent-Reachby Panniantong10085.8k12d agoCLAUDE.md
Understand-Anythingby Egonex-AI10084.4k16d agoCLAUDE.md
headroomby headroomlabs-ai10074.0k1d agoCLAUDE.md

Frequently asked questions

How do I install research-wiki?
Run npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-wiki. The install tabs above show the steps for each supported agent.
Which AI agents does research-wiki work with?
It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
Is research-wiki safe to use?
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 research-wiki still maintained?
The repository was last updated 9 days ago, so research-wiki is actively maintained.

name: research-wiki description: "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." argument-hint: "[subcommand: init|ingest|sync|query|update|lint|stats]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, mcp__codex__codex, mcp__codex__codex-reply

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

| Entity | Directory | Node ID format | What it represents | |--------|-----------|---------------|--------------------| | Paper | papers/ | paper:<slug> | A published or preprint research paper | | Idea | ideas/ | idea:<id> | A research idea (proposed, tested, or failed) | | Experiment | experiments/ | exp:<id> | A concrete experiment run with results | | Claim | claims/ | claim:<id> | A theorem/headline with an honest PROOF status — born via /proof-checker (see Hook 4) |

Typed Relationships (graph/edges.jsonl)

| Edge type | From → To | Meaning | |-----------|-----------|---------| | extends | paper → paper | Builds on prior work | | contradicts | paper → paper | Disagrees with results/claims | | addresses_gap | paper|idea → gap | Targets a known field gap | | inspired_by | idea → paper | Idea sourced from this paper | | tested_by | idea|claim → exp | Tested in this experiment | | supports | exp → claim|idea | Experiment confirms claim | | invalidates | exp → claim|idea | Experiment disproves claim | | supersedes | paper → paper | Newer 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.

Capture hygiene (anti-self-poisoning)

Before persisting an idea / claim / experiment note, screen it for operational noise that would harden into a self-cited falsehood (see shared-references/capture-antipatterns.md). Resolve the helper via the canonical chain (integration-contract §2): .aris/tools/capture_filter.py → tools/capture_filter.py → $ARIS_REPO/tools/capture_filter.py (warn-and-skip if unresolved). Run python3 <capture_filter> - on the note text; if it flags env-failure / transient-error / negative-tool-claim, do NOT store it as a durable node — rewrite it to the fix / missing config / workaround, or drop it. Never store "codex/gemini/the reviewer can't do X" — that gets loaded into every future session and cited against the agent long after the real cause is gone. (The wiki's "failed ideas → anti-repeat memory" is the GOOD inverse: a class-level research finding, not operational noise.)

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 /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

Helper resolution (run before any subcommand below)

All wiki operations except plain directory bootstrap go through a single canonical helper, tools/research_wiki.py. Skills that touch the wiki must resolve $WIKI_SCRIPT via the chain below — never hard-code python3 tools/research_wiki.py …. Hard-coding silently fails when the project does not have tools/ on disk (the post-install_aris.sh default), which is exactly the failure mode that left a real user's research-wiki/ empty for a week.

cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
ARIS_REPO="${ARIS_REPO:-$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null)}"
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
  ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
WIKI_SCRIPT=".aris/tools/research_wiki.py"
[ -f "$WIKI_SCRIPT" ] || WIKI_SCRIPT="tools/research_wiki.py"
[ -f "$WIKI_SCRIPT" ] || { [ -n "${ARIS_REPO:-}" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"; }
[ -f "$WIKI_SCRIPT" ] || {
  echo "ERROR: research_wiki.py not found at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
  echo "       Fix one of:" >&2
  echo "         1. rerun 'bash tools/install_aris.sh' from the ARIS repo (creates .aris/tools symlink, refreshes ~/.aris/repo)" >&2
  echo "         2. rerun 'bash tools/smart_update.sh' (refreshes ~/.aris/repo)" >&2
  echo "         3. export ARIS_REPO=<path-to-ARIS-repo>" >&2
  echo "         4. cp <ARIS-repo>/tools/research_wiki.py tools/" >&2
  exit 1
}

/research-wiki itself is the wiki tool — if the helper is missing the skill hard-fails. Caller skills that update the wiki as a side effect (/idea-creator, /result-to-claim, /research-lit, /arxiv, /alphaxiv, /deepxiv, /semantic-scholar, /exa-search) use the same chain but warn-and-skip instead of hard-failing — their primary output (idea list, claim verdict, paper summary) must still be delivered to the user.

/research-wiki init

Initialize the wiki for the current project. After resolving $WIKI_SCRIPT per the chain above:

python3 "$WIKI_SCRIPT" init research-wiki/

The helper creates research-wiki/{papers,ideas,experiments,claims,graph}/ plus index.md, log.md, gap_map.md, query_pack.md, and graph/edges.jsonl, then appends "Wiki initialized" to log.md.

(Earlier versions of this skill described a prose-only init that omitted query_pack.md — that drifted from the helper and made /idea-creator's Phase 0 query-pack check fall through to a rebuild_query_pack invocation that, under the old hard-coded path, silently failed. Delegating init to the helper is the single source of truth for the wiki schema.)

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

Add a paper to the wiki. This subcommand is thin wrapping around python3 "$WIKI_SCRIPT" ingest_paper …, which is the single implementation of paper ingest in ARIS (per shared-references/integration-contract.md — one helper, no copies). The helper does all of:

  1. Fetch metadata — queries the arXiv Atom API when --arxiv-id is given
  2. Generate slug — <first_author_last_name><year>_<keyword>
  3. Check dedup — skip an existing page unless --update-on-exist
  4. Create page — papers/<slug>.md with the schema below
  5. Rebuild index.md and query_pack.md
  6. Append log.md

Edge extraction (step 5/8 in the old manual flow) is not in ingest_paper; do it as a follow-up with add_edge per relationship identified:

# arXiv-known paper
python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
    --arxiv-id 2501.12345 --thesis "One-line claim from abstract."

# Venue paper with no arXiv mirror
python3 "$WIKI_SCRIPT" ingest_paper research-wiki/ \
    --title "Attention Is All You Need" \
    --authors "Ashish Vaswani, Noam Shazeer, …" --year 2017 --venue "NeurIPS"

# Manual edge after ingest
python3 "$WIKI_SCRIPT" add_edge research-wiki/ \
    --from "paper:vaswani2017_attention_all_you" \
    --to "paper:chen2025_factorized_gap" \
    --type "extends" --evidence "Section 3.2: adapts the encoder block …"

Other skills (/research-lit, /arxiv, /alphaxiv, /deepxiv, /semantic-scholar, /exa-search) call the same helper directly in their own last step — they don't re-route through /research-wiki ingest as a subcommand, so they don't need an LLM roundtrip.

/research-wiki sync — arxiv-ids <id1>,<id2>,...

Batch backfill: ingest one or more arXiv IDs that were read earlier without being ingested (e.g., because research-wiki/ was set up after the reading happened, or a hook didn't fire).

# Explicit list
python3 "$WIKI_SCRIPT" sync research-wiki/ \
    --arxiv-ids 2310.06770,1706.03762

# From a file (one id per line, # comments ok)
python3 "$WIKI_SCRIPT" sync research-wiki/ --from-file ids.txt

Dedup is handled per-id; already-ingested papers are skipped silently. This is the recommended manual repair step (see integration contract §5 Backfill). sync does not scan session traces — callers declare the ids explicitly.

Paper page schema (exactly what ingest_paper emits — do not handwrite alternative fields; lint will flag drift):

---
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"]
added: 2026-04-07T10:12:00Z
---

# <full title>

## 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]

Additionally, when the paper was ingested via --arxiv-id and the arXiv API returned an abstract, the helper appends an ## Abstract (original) section after Relevance to This Project containing the raw abstract text as a blockquote. Manual ingests (no --arxiv-id) do not include this section.

/research-wiki query "<topic>"

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

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

| Section | Budget | Content | |---------|--------|---------| | Project direction | full sections | Structured extraction from RESEARCH_BRIEF.md by ## heading (Problem / Constraints / Direction / Background / Non-Goals / Domain Knowledge / Existing Results), in priority order. No per-field char cap — the 8000-char assembly loop is the only safety net. Falls back to a flat 600-char slice if the brief uses no known headings. | | Top 5 gaps | 1200 chars | From gap_map.md, ranked by: unresolved + linked ideas + failed experiments | | Paper clusters | 1600 chars | 3-5 clusters by tag overlap, 2-3 sentences each | | Failed ideas | 1400 chars | Always included — highest anti-repetition value | | Top papers | 1800 chars | 8-12 pages ranked by: linked gaps, linked ideas, centrality, relevance flag | | Active chains | 900 chars | limitation → opportunity relationship chains | | Open unknowns | 500 chars | Unresolved 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.

`/research-wiki update <node_id> —

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars16.6k
CategoryAI
Updated9d ago
Forks1.4k

Languages

Python

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