1Password에 저장한 비밀번호와 API 키를 명령과 설정 템플릿에 사용합니다. CLI 설치와 인증부터 비밀 값 조회 및 환경 변수 전달까지 안내합니다.
- 1Password CLI 설치와 설정
- 저장된 API 키로 명령 실행
- 설정 템플릿에 비밀 값 삽입
기사·논문·메모를 출처와 상호 링크가 있는 Markdown 지식 모음으로 정리합니다. 답변과 비교를 저장하고, 끊어진 링크와 정보 모순도 점검합니다.
Karpathy's LLM Wiki: build/query interlinked markdown KB.
실행 점수 미검증
설치 방법 보기기사, 논문, 회의 메모로 상호 링크가 있는 Markdown 지식 모음을 만들고 갱신합니다. 원본 자료를 보존하고 인물·조직·개념별 페이지에 출처와 함께 정리합니다. 축적한 내용으로 질문에 답하고 보관할 가치가 있는 비교·분석을 페이지로 추가합니다. 끊어진 링크, 목차 누락, 오래된 정보, 모순, 근거가 약한 서술도 점검합니다.
한 주제를 계속 조사하며 새 자료를 기존 지식에 연결하려는 사람에게 적합합니다. 사용자는 자료 선택과 분석 방향을 맡고, 에이전트는 요약·연결·정리와 일관성 유지를 담당합니다.
Markdown 파일 폴더로 저장하며 데이터베이스나 전용 도구는 필요 없습니다. 경로는 WIKI_PATH로 지정하고 기본값은 ~/wiki입니다. Obsidian과 VS Code에서도 열 수 있습니다. 원본은 원칙적으로 변경하지 않고 정리된 페이지를 수정합니다. 기존 페이지 10개 이상을 갱신하는 자료 반영은 사전에 범위를 확인합니다. 선택 기능인 obsidian-headless 동기화에는 Node.js 22 이상과 Obsidian Sync 구독이 필요합니다.
이 소개문은 공개된 SKILL.md를 바탕으로 AI(OpenAI Codex)가 작성했습니다. 정확한 사양은 아래 원문을 확인하세요.
AI가 SKILL.md의 내용에서 주의가 필요한 작업과 도입 전제를 확인했습니다. 점수는 실제로 실행한 결과(실행 검증)만으로 매깁니다.
점수에는 넣지 않았습니다. 2026년 10월 30일 기준으로 AI(OpenAI Codex)가 판정했습니다. 참고용이므로 설치 전에 원문도 확인해 주세요.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Build and maintain a persistent, compounding knowledge base as interlinked markdown files. Based on Andrej Karpathy's LLM Wiki pattern.
Unlike traditional RAG (which rediscovers knowledge from scratch per query), the wiki compiles knowledge once and keeps it current. Cross-references are already there. Contradictions have already been flagged. Synthesis reflects everything ingested.
Division of labor: The human curates sources and directs analysis. The agent summarizes, cross-references, files, and maintains consistency.
Use this skill when the user:
Location: Set via WIKI_PATH environment variable (e.g. in ${HERMES_HOME:-~/.hermes}/.env).
If unset, defaults to ~/wiki.
WIKI="${WIKI_PATH:-$HOME/wiki}"
The wiki is just a directory of markdown files — open it in Obsidian, VS Code, or any editor. No database, no special tooling required.
wiki/
├── SCHEMA.md # Conventions, structure rules, domain config
├── index.md # Sectioned content catalog with one-line summaries
├── log.md # Chronological action log (append-only, rotated yearly)
├── raw/ # Layer 1: Immutable source material
│ ├── articles/ # Web articles, clippings
│ ├── papers/ # PDFs, arxiv papers
│ ├── transcripts/ # Meeting notes, interviews
│ └── assets/ # Images, diagrams referenced by sources
├── entities/ # Layer 2: Entity pages (people, orgs, products, models)
├── concepts/ # Layer 2: Concept/topic pages
├── comparisons/ # Layer 2: Side-by-side analyses
└── queries/ # Layer 2: Filed query results worth keeping
Layer 1 — Raw Sources: Immutable. The agent reads but never modifies these.
Layer 2 — The Wiki: Agent-owned markdown files. Created, updated, and
cross-referenced by the agent.
Layer 3 — The Schema: SCHEMA.md defines structure, conventions, and tag taxonomy.
When the user has an existing wiki, always orient yourself before doing anything:
① Read SCHEMA.md — understand the domain, conventions, and tag taxonomy.
② Read index.md — learn what pages exist and their summaries.
③ Scan recent log.md — read the last 20-30 entries to understand recent activity.
WIKI="${WIKI_PATH:-$HOME/wiki}"
# Orientation reads at session start
read_file "$WIKI/SCHEMA.md"
read_file "$WIKI/index.md"
read_file "$WIKI/log.md" offset=<last 30 lines>
Only after orientation should you ingest, query, or lint. This prevents:
For large wikis (100+ pages), also run a quick search_files for the topic
at hand before creating anything new.
When the user asks to create or start a wiki:
$WIKI_PATH env var, or ask the user; default ~/wiki)SCHEMA.md customized to the domain (see template below)index.md with sectioned headerlog.md with creation entryAdapt to the user's domain. The schema constrains agent behavior and ensures consistency:
# Wiki Schema
## Domain
[What this wiki covers — e.g., "AI/ML research", "personal health", "startup intelligence"]
## Conventions
- File names: lowercase, hyphens, no spaces (e.g., `transformer-architecture.md`)
- Every wiki page starts with YAML frontmatter (see below)
- Use `[[wikilinks]]` to link between pages (minimum 2 outbound links per page)
- When updating a page, always bump the `updated` date
- Every new page must be added to `index.md` under the correct section
- Every action must be appended to `log.md`
- **Provenance markers:** On pages that synthesize 3+ sources, append `^[raw/articles/source-file.md]`
at the end of paragraphs whose claims come from a specific source. This lets a reader trace each
claim back without re-reading the whole raw file. Optional on single-source pages where the
`sources:` frontmatter is enough.
## Frontmatter
```yaml
---
title: Page Title
created: YYYY-MM-DD
updated: YYYY-MM-DD
type: entity | concept | comparison | query | summary
tags: [from taxonomy below]
sources: [raw/articles/source-name.md]
# Optional quality signals:
confidence: high | medium | low # how well-supported the claims are
contested: true # set when the page has unresolved contradictions
contradictions: [other-page-slug] # pages this one conflicts with
---
confidence and contested are optional but recommended for opinion-heavy or fast-moving
topics. Lint surfaces contested: true and confidence: low pages for review so weak claims
don't silently harden into accepted wiki fact.
Raw sources ALSO get a small frontmatter block so re-ingests can detect drift:
---
source_url: https://example.com/article # original URL, if applicable
ingested: YYYY-MM-DD
sha256: <hex digest of the raw content below the frontmatter>
---
The sha256: lets a future re-ingest of the same URL skip processing when content is unchanged,
and flag drift when it has changed. Compute over the body only (everything after the closing
---), not the frontmatter itself.
[Define 10-20 top-level tags for the domain. Add new tags here BEFORE using them.]
Example for AI/ML:
Rule: every tag on a page must appear in this taxonomy. If a new tag is needed, add it here first, then use it. This prevents tag sprawl.
_archive/, remove from indexOne page per notable entity. Include:
One page per concept or topic. Include:
Side-by-side analyses. Include:
When new information conflicts with existing content:
contradictions: [page-name]
### index.md Template
The index is sectioned by type. Each entry is one line: wikilink + summary.
```markdown
# Wiki Index
> Content catalog. Every wiki page listed under its type with a one-line summary.
> Read this first to find relevant pages for any query.
> Last updated: YYYY-MM-DD | Total pages: N
## Entities
<!-- Alphabetical within section -->
## Concepts
## Comparisons
## Queries
Scaling rule: When any section exceeds 50 entries, split it into sub-sections
by first letter or sub-domain. When the index exceeds 200 entries total, create
a _meta/topic-map.md that groups pages by theme for faster navigation.
# Wiki Log
> Chronological record of all wiki actions. Append-only.
> Format: `## [YYYY-MM-DD] action | subject`
> Actions: ingest, update, query, lint, create, archive, delete
> When this file exceeds 500 entries, rotate: rename to log-YYYY.md, start fresh.
## [YYYY-MM-DD] create | Wiki initialized
- Domain: [domain]
- Structure created with SCHEMA.md, index.md, log.md
When the user provides a source (URL, file, paste), integrate it into the wiki:
① Capture the raw source:
web_extract to get markdown, save to raw/articles/web_extract (handles PDFs), save to raw/papers/raw/ subdirectoryraw/articles/karpathy-llm-wiki-2026.mdsource_url, ingested, sha256 of the body).
On re-ingest of the same URL: recompute the sha256, compare to the stored value —
skip if identical, flag drift and update if different. This is cheap enough to
do on every re-ingest and catches silent source changes.② Discuss takeaways with the user — what's interesting, what matters for the domain. (Skip this in automated/cron contexts — proceed directly.)
③ Check what already exists — search index.md and use search_files to find
existing pages for mentioned entities/concepts. This is the difference between
a growing wiki and a pile of duplicates.
④ Write or update wiki pages:
updated date.
When new info contradicts existing content, follow the Update Policy.[[wikilinks]]. Check that existing pages link back.^[raw/articles/source.md]
markers to paragraphs whose claims trace to a specific source.confidence: medium or low in frontmatter. Don't mark high unless the
claim is well-supported across multiple sources.⑤ Update navigation:
index.md under the correct section, alphabeticallylog.md: ## [YYYY-MM-DD] ingest | Source Title⑥ Report what changed — list every file created or updated to the user.
A single source can trigger updates across 5-15 wiki pages. This is normal and desired — it's the compounding effect.
When the user asks a question about the wiki's domain:
① Read index.md to identify relevant pages.
② For wikis with 100+ pages, also search_files across all .md files
for key terms — the index alone may miss relevant content.
③ Read the relevant pages using read_file.
④ Synthesize an answer from the compiled knowledge. Cite the wiki pages
you drew from: "Based on [[page-a]] and [[page-b]]..."
⑤ File valuable answers back — if the answer is a substantial comparison,
deep dive, or novel synthesis, create a page in queries/ or comparisons/.
Don't file trivial lookups — only answers that would be painful to re-derive.
⑥ Update log.md with the query and whether it was filed.
When the user asks to lint, health-check, or audit the wiki:
① Orphan pages: Find pages with no inbound [[wikilinks]] from other pages.
# Use execute_code for this — programmatic scan across all wiki pages
import os, re
from collections import defaultdict
wiki = "<WIKI_PATH>"
# Scan all .md files in entities/, concepts/, comparisons/, queries/
# Extract all [[wikilinks]] — build inbound link map
# Pages with zero inbound links are orphans
② Broken wikilinks: Find [[links]] that point to pages that don't exist.
③ Index completeness: Every wiki page should appear in index.md. Compare
the filesystem against index entries.
④ Frontmatter validation: Every wiki page must have all required fields (title, created, updated, type, tags, sources). Tags must be in the taxonomy.
⑤ Stale content: Pages whose updated date is >90 days older than the most
recent source that mentions the same entities.
⑥ Contradictions: Pages on the same topic with conflicting claims. Look for
pages that share tags/entities but state different facts. Surface all pages
with contested: true or contradictions: frontmatter for user review.
⑦ Quality signals: List pages with confidence: low and any page that cites
only a single source but has no confidence field set — these are candidates
for either finding corroboration or demoting to confidence: medium.
⑧ Source drift: For each file in raw/ with a sha256: frontmatter, recompute
the hash and flag mismatches. Mismatches indicate the raw file was edited
(shouldn't happen — raw/ is immutable) or ingested from a URL that has since
changed. Not a hard error, but worth reporting.
⑨ Page size: Flag pages over 200 lines — candidates for splitting.
⑩ Tag audit: List all tags in use, flag any not in the SCHEMA.md taxonomy.
⑪ Log rotation: If log.md exceeds 500 entries, rotate it.
⑫ Report findings with specific file paths and suggested actions, grouped by severity (broken links > orphans > source drift > contested pages > stale content > style issues).
⑬ Append to log.md: ## [YYYY-MM-DD] lint | N issues found
# Find pages by content
search_files "transformer" path="$WIKI" file_glob="*.md"
# Find pages by filename
search_files "*.md" target="files" path="$WIKI"
# Find pages by tag
search_files "tags:.*alignment" path="$WIKI" file_glob="*.md"
# Recent activity
read_file "$WIKI/log.md" offset=<last 20 lines>
When ingesting multiple sources at once, batch the updates:
When content is fully superseded or the domain scope changes:
_archive/ directory if it doesn't exist_archive/ with its original path (e.g., _archive/entities/old-page.md)index.mdThe wiki directory works as an Obsidian vault out of the box:
[[wikilinks]] render as clickable linksraw/assets/ folder holds images referenced via ![[image.png]]For best results:
raw/assets/TABLE tags FROM "entities" WHERE contains(tags, "company")If using the Obsidian skill alongside this one, set OBSIDIAN_VAULT_PATH to the
same directory as the wiki path.
On machines without a display, use obsidian-headless instead of the desktop app.
It syncs vaults via Obsidian Sync without a GUI — perfect for agents running on
servers that write to the wiki while Obsidian desktop reads it on another device.
Setup:
# Requires Node.js 22+
npm install -g obsidian-headless
# Login (requires Obsidian account with Sync subscription)
ob login --email <email> --password '<password>'
# Create a remote vault for the wiki
ob sync-create-remote --name "LLM Wiki"
# Connect the wiki directory to the vault
cd ~/wiki
ob sync-setup --vault "<vault-id>"
# Initial sync
ob sync
# Continuous sync (foreground — use systemd for background)
ob sync --continuous
Continuous background sync via systemd:
# ~/.config/systemd/user/obsidian-wiki-sync.service
[Unit]
Description=Obsidian LLM Wiki Sync
After=network-online.target
Wants=network-online.target
[Service]
ExecStart=/path/to/ob sync --continuous
WorkingDirectory=%h/wiki
Restart=on-failure
RestartSec=10
[Install]
WantedBy=default.target
systemctl --user daemon-reload
systemctl --user enable --now obsidian-wiki-sync
# Enable linger so sync survives logout:
sudo loginctl enable-linger $USER
This lets the agent write to ~/wiki on a server while you browse the same
vault in Obsidian on your laptop/phone — changes appear within seconds.
raw/ — sources are immutable. Corrections go in wiki pages.log-YYYY.md and start fresh.
The agent should check log size during lint.llm-wiki-compiler is a Node.js CLI that compiles sources into a concept wiki with the same Karpathy inspiration. It's Obsidian-compatible, so users who want a scheduled/CLI-driven compile pipeline can point it at the same vault this skill maintains. Trade-offs: it owns page generation (replaces the agent's judgment on page creation) and is tuned for small corpora. Use this skill when you want agent-in-the-loop curation; use llmwiki when you want batch compile of a source directory.
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1Password에 저장한 비밀번호와 API 키를 명령과 설정 템플릿에 사용합니다. CLI 설치와 인증부터 비밀 값 조회 및 환경 변수 전달까지 안내합니다.
손익계산서·재무상태표·현금흐름표를 수식으로 연결한 Excel 모델을 만듭니다. 실적과 예측 가정을 입력하고 시나리오를 비교하며 세 재무제표의 일관성을 단계별로 확인합니다.
기존 PyTorch 학습 코드를 적은 수정으로 여러 GPU나 컴퓨터에 맞게 구성합니다. 혼합 정밀도, 메모리 절약 설정과 학습 실행 방법을 안내합니다.
Hermes에서 Actual Computer로 자신의 기기에 AI 모델을 실행하도록 연결을 설정합니다. 중계 서비스와 로컬 연결, 모델 선택, 응답 확인 및 오류 점검을 다룹니다.
기술에 익숙하지 않고 쉽게 불만을 느끼는 사용자로 앱을 시험합니다. 혼란스러운 조작과 이탈 원인을 찾고, 개선 과제와 개인적 불만을 나눠 수정안을 정리합니다.
두 에이전트가 각각 수정한 브랜치의 Git 충돌을 양쪽 변경 내용과 의도에 따라 중립적으로 해결합니다. 충돌별 판단 이유를 기록하고 빌드나 테스트로 통합 결과를 확인합니다.