LLM Wiki
lewislulu/llm-wiki-skill
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers…
Fill in the per-paper TODO sections of research-wiki/papers/<slug.md pages that literature-ingest skills leave as bare scaffolds.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrich --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "wiki-enrich" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrich into .claude/skills/wiki-enrich/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wiki-enrich", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrichType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrich --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/wiki-enrich .agents/skills/wiki-enrich && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "wiki-enrich" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrich into .agents/skills/wiki-enrich/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wiki-enrich", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrich --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/wiki-enrich .cursor/skills/wiki-enrich && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "wiki-enrich" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrich into .cursor/skills/wiki-enrich/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wiki-enrich", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git --path skills/wiki-enrich--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrich --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/wiki-enrich .gemini/skills/wiki-enrich && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "wiki-enrich" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrich into .gemini/skills/wiki-enrich/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wiki-enrich", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrichInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/wiki-enrich .github/skills/wiki-enrich && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "wiki-enrich" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrich into .github/skills/wiki-enrich/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wiki-enrich", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill wiki-enrich -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep wiki-enrich --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/wiki-enrich .opencode/skills/wiki-enrich && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "wiki-enrich" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/wiki-enrich into .opencode/skills/wiki-enrich/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wiki-enrich", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
wiki-enrichFill 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. 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.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 26b95cf. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
Bash(*)ReadWriteEditGlobGrepWebFetchFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
python3gitcurlFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
alphaxiv.orgexport.arxiv.orgAlso links to:
gist.github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Bash(*), Read, Write, Edit, Glob, Grep, WebFetchAutomated 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.
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.
.claude/skills/wiki-enrich/SKILL.md (or your agent's skills folder).Target: $ARGUMENTS
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.
research-wiki/ — Resolved relative to git root. Skill hard-fails if not a directory.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).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 N.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).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.One-line thesis (marker: _TODO: fill in after reading._)Problem / Gap (marker: _TODO._)Method (marker: _TODO._)Key Results (marker: _TODO._)Assumptions (marker: _TODO._)Limitations / Failure Modes (marker: _TODO._)Reusable Ingredients (marker: _TODO._)Open Questions (marker: _TODO._)Claims (marker: _TODO._) — fill with _No claims tracked yet._ if no claim: edges point to this paper; otherwise list them.Connections — NEVER edit (auto-generated from graph/edges.jsonl).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.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)
Resolve $WIKI_ROOT and $WIKI_SCRIPT (canonical chain — see shared-references/wiki-helper-resolution.md):
cd "$(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.
Parse $ARGUMENTS for the first positional (target) and flags (--source, --force, --max).
Build the candidate paper list:
case "$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.
Iterate one paper at a time. For each $PAPER in $PAPERS:
Step 2.1 — Read the page and project context. Use the Read tool on the full paper file. Extract from the YAML frontmatter:
node_id (e.g. paper:vllm) — slug = part after paper:arxiv from external_ids.arxiv — empty string if absenttitle## Abstract (original) blockquote (if present) — fallback content sourceAdditionally, on the FIRST paper of the batch (cache for the rest), read project-context files needed for the Claims and Relevance to This Project sections:
research-wiki/graph/edges.jsonl — scan for claim: edges pointing to the current paper's node_idRESEARCH_BRIEF.md (project root) — if present, source for project goalsCLAUDE.md (project root) — if present, fallback for project contextresearch-wiki/gap_map.md — if non-empty, source for gap framingIf none of the project-context files exist, the Relevance to This Project section will be filled with the literal "context not yet set" line (see Step 2.4 table).
Step 2.2 — Identify which sections are TODO.
Match each section header against its marker:
_TODO._ → fill_TODO: fill in after reading._ → fill (One-line thesis)--force)## Connections → always skip (auto-generated)## Abstract (original) → always skip (immutable source data)If no fillable sections remain, log "skip: <slug> (already enriched)" and continue.
Step 2.3 — Fetch source content.
The fetch chain runs in order until one returns usable content (>200 chars of text):
| Order | Source | How |
|---|---|---|
| 1 | alphaxiv overview (auto default; --source alphaxiv to pin) | WebFetch https://www.alphaxiv.org/overview/<arxiv_id>.md — LLM-optimized summary, often best for filling sections |
| 2 | alphaxiv abs (fallback within alphaxiv) | WebFetch https://www.alphaxiv.org/abs/<arxiv_id>.md |
| 3 | deepxiv brief (--source deepxiv to pin) | python3 "$DEEPXIV_FETCHER" paper-brief <arxiv_id> if helper resolves |
| 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 |
| 5 | Page 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 abstract | Skip 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:
$SOURCE_TEXT (the fetched overview / brief / abstract)$TITLEWrite each TODO section's body following these rules:
| Section | Length | Style | What to extract |
|---|---|---|---|
| One-line thesis | 1 sentence, ≤25 words | Declarative | The paper's core contribution in one sentence — what they built / proved / improved |
| Problem / Gap | 1-2 sentences | Declarative | What problem the field had, why prior work fell short |
| Method | 2-4 sentences | Technical, name the technique | Core mechanism — algorithm name + key idea + how it differs from baselines |
| Key Results | 1-3 bullets OR 2-3 sentences | Quantitative | Headline numbers from the abstract / overview (X% improvement, Yx speedup, etc.). Keep units verbatim. |
| Assumptions | 1-3 bullets | Declarative | What the paper takes for granted (workload type, hardware, model class, distribution shape) |
| Limitations / Failure Modes | 1-3 bullets | Honest | What the paper explicitly admits OR what's structurally absent (e.g. "no multi-node evaluation", "assumes uniform request length") |
| Reusable Ingredients | 1-3 bullets | Concrete | Techniques / datasets / insights from this paper that could be ported elsewhere. Highest value for /idea-creator — write carefully. |
| Open Questions | 1-2 bullets | Question form | What the paper does NOT answer but raises |
| Claims | 1 line | Static | If 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 Project | 1-2 sentences | Project-contextual | Use 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):
_Not stated in source._ over hallucination.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.
python3 "$WIKI_SCRIPT" log research-wiki/ "wiki-enrich: enriched paper:<slug> from <source> (filled N/M sections)"Record which source provided content (alphaxiv-overview, alphaxiv-abs, deepxiv-brief, arxiv-api-abstract, or page-abstract-fallback) so the audit trail is honest about provenance.
After processing all candidates, print:
✓ wiki-enrich complete
Processed: N
Enriched: X (sections filled: total)
Skipped: Y (reasons: already enriched / no arxiv id / fetch failed)
Failed: Z (with paper + reason)
Source breakdown:
alphaxiv-overview: A
alphaxiv-abs: B
deepxiv-brief: C
arxiv-api-abstract: D
page-abstract-fallback: E
Re-ideation suggestion: <if ≥5 papers were enriched, recommend `/idea-creator "topic"` so the freshly-filled `Reusable Ingredients` and `Limitations` feed brainstorming. `query_pack.md` is already rebuilt below — the user does NOT need to call `/research-wiki query` manually.>Also rebuild query_pack.md once at the end (single python3 "$WIKI_SCRIPT" rebuild_query_pack research-wiki/ call) so /idea-creator sees the new bodies on its next run.
Follow the shared protocols:
- No
MANIFEST.mdentry. This skill edits existing scaffolded pages in place rather than generating new artifacts. The audit trail lives inresearch-wiki/log.md(Step 2.6), with provenance per paper. Adding awiki-enrichstage toshared-references/output-manifest.mdis out of scope for this PR.- Output Language Protocol — respect the project's language setting.
--force only touches still-TODO sections. Safe to invoke as a cron.## Connections, or ## Abstract (original). Frontmatter is metadata, Connections is graph-generated, Abstract is immutable source data.research-wiki/ — if it's missing, the user is in the wrong cwd or hasn't run /research-wiki init./idea-creator. This skill builds the substrate; the user decides when to brainstorm next. Only suggest re-ideation in the final report.WebFetch is rate-limited, fall through to next source. If all sources miss, skip the paper and continue — don't abort the whole batch._Not stated in source._ than to hallucinate. The wiki's value is that it doesn't lie./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 changesAfter 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
Just SKILL.md in skills/wiki-enrich of wanshuiyin/Auto-claude-code-research-in-sleep.
Open the folder on GitHubat commit 26b95cf
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Wiki Enrich this skillwanshuiyin/Auto-claude-code-research-in-sleep | 17k | 1 repos | ~4.1k | Automated safety check: Notes | MIT | |
| LLM Wikilewislulu/llm-wiki-skill | 655 | — | ~3.7k | Automated safety check: Pass | None | |
| Wiki Research Looprohitg00/pro-workflow | 2.9k | — | ~1.5k | Automated safety check: Pass | None | |
| Karpathy LLM WikiAstro-Han/karpathy-llm-wiki | 2.5k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Wiki Builderrohitg00/pro-workflow | 2.9k | — | ~1k | Automated safety check: Pass | None | |
| Codex History IngestAr9av/obsidian-wiki | 3.5k | — | ~2.2k | Automated safety check: Notes | MIT |
lewislulu/llm-wiki-skill
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers…
rohitg00/pro-workflow
Auto-grow a pro-workflow wiki by running a budget-capped BFS research loop over pluggable source fetchers (web, arXiv, GitHub).
Astro-Han/karpathy-llm-wiki
A skill your agent uses when building or maintaining a personal LLM-powered knowledge base.
rohitg00/pro-workflow
Start, structure, and grow a persistent research wiki indexed in pro-workflow's SQLite knowledge base.
Ar9av/obsidian-wiki
Ingest Codex CLI conversation/session history into Obsidian as distilled knowledge.
nduckmink/arkon
Propose or directly apply edits to Arkon wiki pages, including proposing brand new pages.
wanshuiyin/Auto-claude-code-research-in-sleep
Builds an academic conference poster as a single HTML and CSS file with measurement-based gates, real paper figures and a print-ready PDF rendered through headless Chromium.
wanshuiyin/Auto-claude-code-research-in-sleep
Runs a mathematical proof project as a stateful pipeline of run directories: a local attempt first, then a manual GPT Pro handoff package, with an optional DeepSeek audit.
wanshuiyin/Auto-claude-code-research-in-sleep
Render an ARIS Markdown / JSON artifact (IDEAREPORT, AUTOREVIEW, KILLARGUMENT, PAPERPLAN, research-wiki state, etc.) into a single-file HTML view designed for human reading.
wanshuiyin/Auto-claude-code-research-in-sleep
Audit experiment integrity before claiming results. An agent skill from wanshuiyin/Auto-claude-code-research-in-sleep.
wanshuiyin/Auto-claude-code-research-in-sleep
Run the Anti-Autoresearch integrity-forensics DETERMINISTIC slice (numeric core + rules-only reporter) against a paper via a SHA-pinned thin launcher, then convert the verdict into a typed policy…
wanshuiyin/Auto-claude-code-research-in-sleep
Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic — formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1 必会 / L2 进阶 / L3 顶级 lab).
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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.
Wiki Enrich fits situations like: user says enrich wiki; fill paper TODOs; 把 paper 摘要寫進 wiki; research-wiki 自動填.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.