wiki-enrich

wiki-enrich is a skill for Claude Code, Codex from wanshuiyin/Auto-claude-code-research-in-sleep. It costs 87 tokens per session (4,435 once invoked), scanned A, original, MIT.

A workflow for filling unfinished sections in research-wiki paper pages with summaries, explanations, and connections. It is designed for wikis that store structured notes about academic papers.

In plain words
What is it for?
Use it to enrich paper notes with key ideas, methods, findings, and links to related research.
Why use it?
It turns bare paper templates into usable reference pages instead of leaving them as TODO lists.

Skill for Claude CodeCodex

Written for Claude Code and Codex: allowed-tools in frontmatter, but also reads ~/.codex or $CODEX_HOME. Also seen: mentions CLAUDE.md; positional $N argument; mentions AGENTS.md.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is - **[Output Language Protocol](../shared-references/output-language.md)** — respect the project's language setting..

Good fit Use it to enrich paper notes with key ideas, methods, findings, and links to related research.

Compare 6 skills from other repositories ↓
About the project

ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.

wanshuiyin/Auto-claude-code-research-in-sleep · 16,030 stars · on GitHub

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
agentmods
npx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for wiki-enrich

README.md
[![agentmods](https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich/github.svg)](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich)
Your own site
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for wiki-enrich

Your own site · 80×15
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,435 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00087 $0.04435
Opus 5 $0.00044 $0.02218
Sonnet 5 $0.00017 $0.00887
Haiku 4.5 $0.00009 $0.00443

Measured 9d ago against content hash 3c3748a4da25, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

wiki-enrich scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| 4 | **arXiv API abstract — fresh fetch** (`--source arxiv` to pin) | `curl http://export.arxiv.org/api/query?id_list=<arxiv_id>` — log label: `arxiv-api-abstract` |
skills/skills-codex/wiki-enrich/SKILL.md · 256 lines

How it starts

The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Wiki Enrich: Fill Paper TODO Sections (Karpathy LLM-Wiki)

Target: $ARGUMENTS

Why this skill exists

ingest_paper (called by /research-lit, /arxiv, /alphaxiv, /deepxiv, /semantic-scholar, /exa-search) only renders the per-paper scaffold — frontmatter + abstract + 10 fillable _TODO._ placeholder sections (plus two protected sections: ## Connections is graph-summary and ## Abstract (original) is auto-populated when --arxiv-id is given). No downstream skill in ARIS fills those 10 sections; the wiki sits as TODO until someone reads each paper.

This contradicts the Karpathy LLM-wiki design (https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f):

"You never (or rarely) write the wiki yourself — the LLM writes and maintains all of it. … The tedious part of maintaining a knowledge base is not the reading or the thinking — it's the bookkeeping. … LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass."

/wiki-enrich is the missing back half of ingest_paper: it reads each scaffolded paper page, fetches paper content from external sources via a graceful fallback chain (see Phase 2.3 for the full 5-source chain), and rewrites the 10 fillable TODO sections into 1-3 sentence prose summaries.

Constants

  • WIKI_ROOT = research-wiki/ — Resolved relative to git root. Skill hard-fails if not a directory.
  • TARGET_DEFAULT = missing — When no target is given, enrich only papers with ≥1 TODO section. Other targets: <slug> (one paper) or all (every paper, even ones already enriched — usually combined with --force to overwrite).
  • SOURCE_DEFAULT = auto — Fetch order: alphaxiv overview → alphaxiv abs → deepxiv brief → arXiv API abstract → page abstract fallback. First non-empty wins (full chain documented in Phase 2.3 table). Override with --source to pin one source.
  • MAX_PAPERS = 20 — Hard cap per invocation; LLMs touch many files but token budgets are real. Override with --max N.
  • FORCE = false — When false (default), skip sections that already have non-TODO content. When true, overwrite every fillable section, but never touch the two protected sections: ## Connections (auto-generated from edges.jsonl) and ## Abstract (original) (immutable arXiv-fetched source data).
  • SECTIONS_TO_FILL — 10 fillable sections + 2 protected. ingest_paper (research_wiki.py:436-473) scaffolds 11 section headers unconditionally and a 12th — ## Abstract (original) — only when arXiv returns an abstract for the given --arxiv-id (research_wiki.py:469-473). Of these, 10 carry a _TODO._ (or _TODO: fill in after reading._) marker and need filling. The other 2 — ## Connections (position 10 in the enumeration below) and ## Abstract (original) (position 12, conditional) — are protected by construction: Connections is auto-generated from graph/edges.jsonl, Abstract (original) is immutable source data from the arXiv API. This skill writes to the 10, never the 2.
    1. One-line thesis (marker: _TODO: fill in after reading._)
    2. Problem / Gap (marker: _TODO._)
    3. Method (marker: _TODO._)
    4. Key Results (marker: _TODO._)
    5. Assumptions (marker: _TODO._)
    6. Limitations / Failure Modes (marker: _TODO._)
    7. Reusable Ingredients (marker: _TODO._)
    8. Open Questions (marker: _TODO._)
    9. Claims (marker: _TODO._) — fill with _No claims tracked yet._ if no claim: edges point to this paper; otherwise list them.
    10. ConnectionsNEVER edit (auto-generated from graph/edges.jsonl).
    11. Relevance to This Project (marker: _TODO._) — use RESEARCH_BRIEF.md, AGENTS.md (or legacy CLAUDE.md), or gap_map.md for project context. If no project context exists, leave as TODO and report it.
    12. Abstract (original) — leave alone (already populated by ingest_paper when --arxiv-id was used).

Read the full file on GitHub · 256 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 256 lines · 87 tokens per session scan A 3c3748a4da25

Subscribe to this mod's changes

wiki-enrich is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (16,030 stars, last pushed yesterday), licensed MIT. It adds 87 tokens to every session and 4,435 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

remote-compute-ssh

Evaluate and use SSH Remote Compute before choosing where to run GPU, high-memory, parallel, batch, model-inference, bioinformatics, or other long-running scientific work; supports short remote commands and asynchronous jobs with automatic harvest and analysis.

aipoch/open-science · 53 tokens

figure-style

Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a…

aipoch/open-science · 91 tokens

evo2

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…

aipoch/open-science · 83 tokens

ligandmpnn

Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be…

aipoch/open-science · 100 tokens

memory

Cross-project research memory. Deep-dive past projects' notes, record corrections, and save cross-project insights across all Luxas research projects.

Muuuun/luxas · 30 tokens

ml-training-recipes

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…

Orchestra-Research/AI-Research-SKILLs · 88 tokens