research-wiki

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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.

9installs

NPX Install

npx skill4agent add wanshuiyin/auto-claude-code-research-in-sleep research-wiki

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
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 testable scientific claim with evidence status

Typed Relationships (
graph/edges.jsonl
)

Edge typeFrom → ToMeaning
extends
paper → paperBuilds on prior work
contradicts
paper → paperDisagrees with results/claims
addresses_gap
paper|idea → gapTargets a known field gap
inspired_by
idea → paperIdea sourced from this paper
tested_by
idea|claim → expTested in this experiment
supports
exp → claim|ideaExperiment confirms claim
invalidates
exp → claim|ideaExperiment disproves claim
supersedes
paper → 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 /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

/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"

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

Add a paper to the wiki. This subcommand is thin wrapping around the canonical helper
python3 tools/research_wiki.py 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:
bash
# arXiv-known paper
python3 tools/research_wiki.py ingest_paper research-wiki/ \
    --arxiv-id 2501.12345 --thesis "One-line claim from abstract."

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

# Manual edge after ingest
python3 tools/research_wiki.py 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).
bash
# Explicit list
python3 tools/research_wiki.py sync research-wiki/ \
    --arxiv-ids 2310.06770,1706.03762

# From a file (one id per line, # comments ok)
python3 tools/research_wiki.py 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):
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"]
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):
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.

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

Update a specific entity:
/research-wiki update paper:chen2025 — relevance: core
/research-wiki update idea:001 — outcome: negative
/research-wiki update claim:C1 — status: invalidated
After any update: rebuild
query_pack.md
, update
log.md
.

/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.

/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

All paper-reading skills follow the same integration contract (see
shared-references/integration-contract.md
):
  • single predicate —
    [ -d research-wiki/ ]
  • single canonical helper —
    python3 tools/research_wiki.py ingest_paper …
  • concrete artifact —
    papers/<slug>.md
    +
    log.md
    entry
  • backfill —
    sync --arxiv-ids …
  • diagnostic —
    tools/verify_wiki_coverage.sh

Hook 1: After
/research-lit
finds papers

# At end of research-lit, after synthesis:
if research-wiki/ exists:
    for paper in top_relevant_papers (limit 8-12):
        python3 tools/research_wiki.py ingest_paper research-wiki/ \
            --arxiv-id <id> [--thesis "..."] [--tags "..."]
        for each explicit relation to existing wiki paper:
            python3 tools/research_wiki.py add_edge research-wiki/ \
                --from "paper:<slug>" --to "<target>" \
                --type <extends|contradicts|addresses_gap|...> \
                --evidence "..."
    log "research-lit ingested N papers"
Each paper-reading skill ships its own Step "Update Research Wiki (if active)" that calls the same helper once per paper it touched. The business logic is not duplicated — only the loop over that skill's specific result set differs.

Hook 2:
/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):
    /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
/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
/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."