Fill in the per-paper TODO sections of research-wiki/papers/<slug.md pages that literature-ingest skills leave as bare scaffolds.

MITAuto-check: notesKnowledge Management

Install Wiki Enrich

skills CLI
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrich --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/wiki-enrich .claude/skills/wiki-enrich && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
wiki-enrich
GitHub stars
17k
Used in
1 other repo
Token cost
~4.1k tokens
SKILL.md length
1,724 words
Files
1
Skills in repo
26
Repo updated
First seen
Licence
MIT

At a glance

Fill in the per-paper TODO sections of research-wiki/papers/<slug.md pages that literature-ingest skills leave as bare scaffolds.

  • Works in 3 steps: Parse target + discover candidates → For each paper — read, fetch, fill → Final report
  • User says enrich wiki
  • SKILL.md covers Why this skill exists, Constants, Pre-flight and Workflow, plus 3 more sections
  • Calls python3, git and curl; reaches alphaxiv.org and export.arxiv.org

What it does

Wiki Enrich is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. Fill in the per-paper TODO sections of research-wiki/papers/<slug.md pages that literature-ingest skills leave as bare scaffolds. Use when user says 'enrich wiki', 'fill paper TODOs', 'wiki body 補完', '把 paper 摘要寫進 wiki', 'research-wiki 自動填', or after a batch ingest that left papers/ as TODO scaffolds.

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering LLM wikis and Project scaffolding. It works with arXiv. The repository describes itself as: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework… The licence is MIT.

When your agent uses it

  • User says enrich wiki
  • Fill paper TODOs
  • 把 paper 摘要寫進 wiki
  • Research-wiki 自動填

Example prompts

  • “enrich wiki”
  • “fill paper TODOs”
  • “wiki body 補完”
  • “/wiki-enrich”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash(*), Read, Write, Edit, Glob, Grep, WebFetch

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Parse target + discover candidates
  2. For each paper — read, fetch, fill
  3. Final report

What it can do on your machine

Read from SKILL.md and the folder at commit 26b95cf. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(*)
    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • WebFetch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3
    • git
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • alphaxiv.org
    • export.arxiv.org

    Also links to:

    • gist.github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Wiki Enrich loads about 4.1k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 1,724 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash(*), Read, Write, Edit, Glob, Grep, WebFetch

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wanshuiyin/Auto-claude-code-research-in-sleep at commit 26b95cf, republished under its MIT licence (© wanshuiyin). 1,724 words, ~4,107 tokens.

Download SKILL.mdSave it as .claude/skills/wiki-enrich/SKILL.md (or your agent's skills folder).
name
wiki-enrich
description
Fill in the per-paper TODO sections of research-wiki/papers/<slug>.md pages that literature-ingest skills leave as bare scaffolds. Use when user says 'enrich wiki', 'fill paper TODOs', 'wiki body 補完', '把 paper 摘要寫進 wiki', 'research-wiki 自動填', or after a batch ingest that left papers/ as TODO scaffolds.
allowed-tools
Bash(*), Read, Write, Edit, Glob, Grep, WebFetch
argument-hint
[target: slug|missing|all] [--source alphaxiv|deepxiv|arxiv|auto] [--force] [--max N]

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. Connections — NEVER edit (auto-generated from graph/edges.jsonl).
    11. Relevance to This Project (marker: _TODO._) — use RESEARCH_BRIEF.md, 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).

💡 Examples:

  • /wiki-enrich — enrich every paper with ≥1 TODO section (most common usage)
  • /wiki-enrich vllm — enrich a single paper by slug
  • /wiki-enrich all --force — rewrite every paper from scratch (use when you've adopted a new style)
  • /wiki-enrich --source alphaxiv --max 5 — only use alphaxiv, only do 5 papers
  • /wiki-enrich missing --max 50 — bigger batch (watch token budget)

Pre-flight

Resolve $WIKI_ROOT and $WIKI_SCRIPT (canonical chain — see shared-references/wiki-helper-resolution.md):

bash
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
[ -d research-wiki/ ] || { echo "ERROR: research-wiki/ not found. Run /research-wiki init first." >&2; 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." >&2; exit 1; }

If either fails, hard-fail — this skill manipulates wiki state and must not run blind.

Workflow

Phase 1: Parse target + discover candidates

Parse $ARGUMENTS for the first positional (target) and flags (--source, --force, --max).

Build the candidate paper list:

bash
case "$TARGET" in
  all)
    PAPERS=( research-wiki/papers/*.md )
    ;;
  missing|"")
    # only papers with at least one TODO marker line
    PAPERS=( $(grep -lE "^_TODO(\._?|: fill in after reading\._?)$" research-wiki/papers/*.md 2>/dev/null) )
    ;;
  *)
    P="research-wiki/papers/${TARGET}.md"
    [ -f "$P" ] || { echo "ERROR: paper not found: $P" >&2; exit 1; }
    PAPERS=( "$P" )
    ;;
esac
echo "Candidate papers: ${#PAPERS[@]} (cap ${MAX_PAPERS})"
PAPERS=( "${PAPERS[@]:0:${MAX_PAPERS}}" )

If the candidate list is empty, print "✓ Nothing to enrich." and exit 0. Do not error.

Phase 2: For each paper — read, fetch, fill

Iterate one paper at a time. For each $PAPER in $PAPERS:

Step 2.1 — Read the page and project context. Use the Read tool on the full paper file. Extract from the YAML frontmatter:

  • node_id (e.g. paper:vllm) — slug = part after paper:
  • arxiv from external_ids.arxiv — empty string if absent
  • title
  • existing ## Abstract (original) blockquote (if present) — fallback content source

Additionally, on the FIRST paper of the batch (cache for the rest), read project-context files needed for the Claims and Relevance to This Project sections:

  • research-wiki/graph/edges.jsonl — scan for claim: edges pointing to the current paper's node_id
  • RESEARCH_BRIEF.md (project root) — if present, source for project goals
  • CLAUDE.md (project root) — if present, fallback for project context
  • research-wiki/gap_map.md — if non-empty, source for gap framing

If none of the project-context files exist, the Relevance to This Project section will be filled with the literal "context not yet set" line (see Step 2.4 table).

Step 2.2 — Identify which sections are TODO.

Match each section header against its marker:

  • A header followed by exactly _TODO._ → fill
  • A header followed by _TODO: fill in after reading._ → fill (One-line thesis)
  • A header followed by any other content → skip (unless --force)
  • ## Connections → always skip (auto-generated)
  • ## Abstract (original) → always skip (immutable source data)

If no fillable sections remain, log "skip: <slug> (already enriched)" and continue.

Step 2.3 — Fetch source content.

The fetch chain runs in order until one returns usable content (>200 chars of text):

OrderSourceHow
1alphaxiv overview (auto default; --source alphaxiv to pin)WebFetch https://www.alphaxiv.org/overview/<arxiv_id>.md — LLM-optimized summary, often best for filling sections
2alphaxiv abs (fallback within alphaxiv)WebFetch https://www.alphaxiv.org/abs/<arxiv_id>.md
3deepxiv brief (--source deepxiv to pin)python3 "$DEEPXIV_FETCHER" paper-brief <arxiv_id> if helper resolves
4arXiv API abstract — fresh fetch (--source arxiv to pin)curl http://export.arxiv.org/api/query?id_list=<arxiv_id> — log label: arxiv-api-abstract
5Page abstract — fallback (last resort)Reuse the existing ## Abstract (original) blockquote already present in the page body from a prior ingest_paper run — log label: page-abstract-fallback
—No arxiv id + no page abstractSkip this paper, log "skip: <slug> (no arxiv id, no abstract)", continue

When trying alphaxiv: if WebFetch returns 404 / "Paper not found" / a redirect to the homepage, treat as miss and fall through.

When trying deepxiv: resolve $DEEPXIV_FETCHER per shared-references/integration-contract.md. If the helper or deepxiv CLI is missing, fall through silently.

Save the fetched content as $SOURCE_TEXT. Record which source succeeded for the log entry.

Step 2.4 — Generate per-section content.

You (Claude) are the LLM doing the grunt work. Given:

Show full SKILL.md (687 more words)Show less
  • $SOURCE_TEXT (the fetched overview / brief / abstract)
  • $TITLE
  • the list of fillable section headers

Write each TODO section's body following these rules:

SectionLengthStyleWhat to extract
One-line thesis1 sentence, ≤25 wordsDeclarativeThe paper's core contribution in one sentence — what they built / proved / improved
Problem / Gap1-2 sentencesDeclarativeWhat problem the field had, why prior work fell short
Method2-4 sentencesTechnical, name the techniqueCore mechanism — algorithm name + key idea + how it differs from baselines
Key Results1-3 bullets OR 2-3 sentencesQuantitativeHeadline numbers from the abstract / overview (X% improvement, Yx speedup, etc.). Keep units verbatim.
Assumptions1-3 bulletsDeclarativeWhat the paper takes for granted (workload type, hardware, model class, distribution shape)
Limitations / Failure Modes1-3 bulletsHonestWhat the paper explicitly admits OR what's structurally absent (e.g. "no multi-node evaluation", "assumes uniform request length")
Reusable Ingredients1-3 bulletsConcreteTechniques / datasets / insights from this paper that could be ported elsewhere. Highest value for /idea-creator — write carefully.
Open Questions1-2 bulletsQuestion formWhat the paper does NOT answer but raises
Claims1 lineStaticIf no claim: edges in graph/edges.jsonl reference this paper, write the literal italic line: _No claims tracked yet — populate via /proof-checker._. Else list claim node IDs.
Relevance to This Project1-2 sentencesProject-contextualUse RESEARCH_BRIEF.md / CLAUDE.md / gap_map.md to phrase the connection. If no project context, write the literal italic line: _Project context not yet set — populate RESEARCH_BRIEF.md or gap_map.md to enable this section._ and report.

Rules (Karpathy fidelity):

  • Faithful to source. If the paper doesn't say it, don't invent it. Prefer _Not stated in source._ over hallucination.
  • No filler. "This paper presents an approach to..." — don't write that. Start with the noun.
  • Keep technical terms in English. vLLM, KV cache, prefill, decode, TTFT, etc. stay verbatim.
  • Quantitative when possible. If the abstract has numbers, use them; don't paraphrase as "significant".
  • Bilingual support. If the project's CLAUDE.md declares a language preference (language: zh or language: bilingual), match it. Otherwise default to English (or follow shared-references/output-language.md).

Step 2.5 — Edit the file.

For each fillable section, use the Edit tool to replace the TODO marker with the generated body. Match the exact section header + marker pair to keep edits unique, e.g.:

## Problem / Gap
_TODO._

→

## Problem / Gap
<generated body>

Never touch the YAML frontmatter, ## Connections, or ## Abstract (original).

Step 2.6 — Append log entry.

bash
python3 "$WIKI_SCRIPT" log research-wiki/ "wiki-enrich: enriched paper:<slug> from <source> (filled N/M sections)"

Record which source provided content (alphaxiv-overview, alphaxiv-abs, deepxiv-brief, arxiv-api-abstract, or page-abstract-fallback) so the audit trail is honest about provenance.

Phase 3: Final report

After processing all candidates, print:

✓ wiki-enrich complete

Processed:  N
Enriched:   X (sections filled: total)
Skipped:    Y  (reasons: already enriched / no arxiv id / fetch failed)
Failed:     Z  (with paper + reason)

Source breakdown:
  alphaxiv-overview: A
  alphaxiv-abs:      B
  deepxiv-brief:     C
  arxiv-api-abstract:     D
  page-abstract-fallback: E

Re-ideation suggestion: <if ≥5 papers were enriched, recommend `/idea-creator "topic"` so the freshly-filled `Reusable Ingredients` and `Limitations` feed brainstorming. `query_pack.md` is already rebuilt below — the user does NOT need to call `/research-wiki query` manually.>

Also rebuild query_pack.md once at the end (single python3 "$WIKI_SCRIPT" rebuild_query_pack research-wiki/ call) so /idea-creator sees the new bodies on its next run.

Output Protocols

Follow the shared protocols:

  • No MANIFEST.md entry. This skill edits existing scaffolded pages in place rather than generating new artifacts. The audit trail lives in research-wiki/log.md (Step 2.6), with provenance per paper. Adding a wiki-enrich stage to shared-references/output-manifest.md is out of scope for this PR.
  • Output Language Protocol — respect the project's language setting.

Key Rules

  • Idempotent by default. Re-running without --force only touches still-TODO sections. Safe to invoke as a cron.
  • Never touch frontmatter, ## Connections, or ## Abstract (original). Frontmatter is metadata, Connections is graph-generated, Abstract is immutable source data.
  • Hard-fail on missing wiki / missing helper. Do not silently create research-wiki/ — if it's missing, the user is in the wrong cwd or hasn't run /research-wiki init.
  • Track provenance. Every log entry records which source actually filled the body. If a future audit shows alphaxiv hallucinated for a paper, you can find every page touched by that source.
  • Don't auto-trigger /idea-creator. This skill builds the substrate; the user decides when to brainstorm next. Only suggest re-ideation in the final report.
  • Gracefully degrade. If WebFetch is rate-limited, fall through to next source. If all sources miss, skip the paper and continue — don't abort the whole batch.
  • Karpathy fidelity above completeness. It is better to leave a section as _Not stated in source._ than to hallucinate. The wiki's value is that it doesn't lie.

Composing with Other Skills

/research-lit "topic"               ← ingests papers as scaffolds (Step 6)
/wiki-enrich                        ← THIS — fills paper bodies (you are here)
/research-wiki lint                 ← health-check (orphans, contradictions, dead ideas)
/idea-creator "direction"           ← reads query_pack, ideates on top of enriched wiki
/research-wiki query "topic"        ← rebuild query_pack after big wiki changes

After a fresh /research-pipeline run leaves Stage 1 Phase 1 done but Phase 2 not started (the failure mode that prompted this skill), the recovery path is:

/wiki-enrich              # fill the paper TODOs ingest_paper left behind
/idea-creator "..."        # now ideate with a wiki that actually has content

© wanshuiyin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/wiki-enrich of wanshuiyin/Auto-claude-code-research-in-sleep.

Open the folder on GitHubat commit 26b95cf

Used in 1 other repository

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wanshuiyin/Auto-claude-code-research-in-sleep, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Wiki Enrich next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Wiki Enrich compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Wiki Enrich this skillwanshuiyin/Auto-claude-code-research-in-sleep17k1 repos~4.1kAutomated safety check: NotesMIT
LLM Wikilewislulu/llm-wiki-skill655—~3.7kAutomated safety check: PassNone
Wiki Research Looprohitg00/pro-workflow2.9k—~1.5kAutomated safety check: PassNone
Karpathy LLM WikiAstro-Han/karpathy-llm-wiki2.5k—~3.6kAutomated safety check: PassMIT
Wiki Builderrohitg00/pro-workflow2.9k—~1kAutomated safety check: PassNone
Codex History IngestAr9av/obsidian-wiki3.5k—~2.2kAutomated safety check: NotesMIT

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Works with

Questions about Wiki Enrich

What does Wiki Enrich do?

Fill in the per-paper TODO sections of research-wiki/papers/<slug.md pages that literature-ingest skills leave as bare scaffolds. Wiki Enrich is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep.md pages that literature-ingest skills leave as bare scaffolds.

When should I use Wiki Enrich?

Wiki Enrich fits situations like: user says enrich wiki; fill paper TODOs; 把 paper 摘要寫進 wiki; research-wiki 自動填.

How do I install Wiki Enrich in Claude Code?

Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a claude-code`. Or copy the skill folder (skills/wiki-enrich in wanshuiyin/Auto-claude-code-research-in-sleep) into .claude/skills/wiki-enrich in your project. Claude Code loads it when a task matches its description.

How do I install Wiki Enrich in Codex?

Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a codex`. Or copy the skill folder (skills/wiki-enrich in wanshuiyin/Auto-claude-code-research-in-sleep) into .agents/skills/wiki-enrich in your project. Codex loads it when a task matches its description.

Can I use Wiki Enrich in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wiki-enrich, .gemini/skills/wiki-enrich, .github/skills/wiki-enrich and .opencode/skills/wiki-enrich in your project.

What does Wiki Enrich need to run?

Going by SKILL.md and its folder, Wiki Enrich needs the command-line tools its instructions call (python3, git and curl). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, Glob, Grep, WebFetch.

Does Wiki Enrich access the network?

SKILL.md names 3 domains. In commands or code: alphaxiv.org and export.arxiv.org; the agent is likely to contact these when it follows the instructions. As links in the text: gist.github.com. This is read from the text; nothing was executed.

Is Wiki Enrich safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Wiki Enrich use?

Wiki Enrich is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Wiki Enrich use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Wiki Enrich?

Skills that share tags, products or a category with Wiki Enrich: LLM Wiki (lewislulu/llm-wiki-skill, 655 stars), Wiki Research Loop (rohitg00/pro-workflow, 2.9k stars), Karpathy LLM Wiki (Astro-Han/karpathy-llm-wiki, 2.5k stars) and Wiki Builder (rohitg00/pro-workflow, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Wiki Enrich?

wanshuiyin (a GitHub user) maintains it in wanshuiyin/Auto-claude-code-research-in-sleep, which has 17,205 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 7, 2026.

Source: wanshuiyin/Auto-claude-code-research-in-sleep on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.