ingest

ingest is a command for Claude Code from kimsanguine/llm-brain. It costs 19 tokens per session (916 once invoked), scanned A, original, MIT.

A command for adding information to a file-based knowledge wiki from web pages, files, or text notes. It classifies the material, updates or creates wiki pages, and adds links between related pages.

In plain words
What is it for?
Use it to import a URL, local file, or written note, set its importance level, check unprocessed items, and compile source files into linked wiki pages.
Why use it?
It gives different kinds of source material one repeatable path into the wiki instead of requiring manual copying and organization.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

Not installable on its own: it runs a file from its repository that does not travel with it. Clone the repository, or install whatever ships that file. The line is uv run python scripts/ingest.py --url <URL> [--resonance <level>].

Part of the llm-brain plugin — 7 commands shipped together

Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add kimsanguine/llm-brain
Claude Code
/plugin install llm-brain

Made for: Claude Code.

Or install llm-brain, the plugin that ships this one along with the rest of its 7 commands.

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 ingest

README.md
[![agentmods](https://agentmods.dev/badge/commands/kimsanguine/llm-brain/ingest.svg)](https://agentmods.dev/commands/kimsanguine/llm-brain/ingest)
Your own site
<a href="https://agentmods.dev/commands/kimsanguine/llm-brain/ingest"><img src="https://agentmods.dev/badge/commands/kimsanguine/llm-brain/ingest.svg" alt="Measured on agentmods" height="20"></a>
Per session 19 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 916 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00019 $0.00916
Opus 5 $0.00010 $0.00458
Sonnet 5 $0.00004 $0.00183
Haiku 4.5 $0.00002 $0.00092

Measured 6d ago against content hash 8b1693502b36, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

ingest scanned grade A with 0 findings 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 6d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/ingest.md · 113 lines

How it starts

The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

llm-brain의 ingest 커맨드입니다. 아래 절차를 순서대로 실행하세요.

인자 파싱

$ARGUMENTS를 파싱해 모드를 결정합니다:

  • https://... 또는 http://... 로 시작 → URL 모드
  • --file <경로> 포함 → 파일 모드
  • --note "<텍스트>" 포함 → 노트 모드
  • --resonance high|medium|low 옵션이 있으면 해당 레벨 사용
  • --priority-only → resonance: high 미처리 파일만 처리
  • 인자 없음 → 미처리 파일 목록만 확인

Step 1: 스크립트 실행

인자에 따라 아래 중 해당하는 명령 실행:

# URL 수집
cd "$(git rev-parse --show-toplevel)"  # llm-brain 레포 루트
uv run python scripts/ingest.py --url <URL> [--resonance <level>]

# 파일 추가
uv run python scripts/ingest.py --file <경로> [--resonance <level>]

# 텍스트 노트
uv run python scripts/ingest.py --note "<텍스트>" [--resonance <level>]

# 미처리 목록 확인 (인자 없음)
uv run python scripts/ingest.py [--priority-only]

exit code 0 = 처리할 파일 없음, exit code 1 = 미처리 파일 있음.

Step 2: wiki 컴파일

스크립트 출력에서 미처리 파일 목록을 확인합니다. 미처리 파일이 있으면 schema/ingest.md 규칙에 따라 각 파일을 wiki 페이지로 컴파일합니다:

  1. 각 raw 파일 내용 읽기
  2. schema/domains.yaml 기준 도메인 분류
  3. index.md에서 관련 기존 페이지 확인
    • 기존 페이지 있음 → 갱신 (sources 추가, 내용 병합)
    • 없음 → 신규 생성 (wiki frontmatter 포함)
  4. wikilink 교차 연결
  5. index.md 갱신

Step 3: 완료 표시

wiki 컴파일 완료 후:

cd "$(git rev-parse --show-toplevel)"  # llm-brain 레포 루트
uv run python scripts/ingest.py --mark-done

Step 4: 그래프 delta 처리

wiki 페이지가 0개이거나 Step 2에서 변경 사항이 없으면 이 단계를 건너뜁니다.

4-0. 스냅샷 (export_graph.py 실행 전 반드시 먼저):

import sys
sys.path.insert(0, "scripts")
from ingest import snapshot_graph
snapshot_graph()  # wiki/graph.json → wiki/.graph_prev.json 복사

4-1. export_graph.py 실행 (graph.json 갱신):

cd "$(git rev-parse --show-toplevel)"  # llm-brain 레포 루트
uv run python scripts/export_graph.py

4-2. delta 계산 및 출력:

import sys
sys.path.insert(0, "scripts")
from ingest import run_delta_pipeline, print_delta
delta = run_delta_pipeline()
if delta:
    print_delta(delta)
else:
    print("[ingest] delta — 변경 없음")

Step 5: Canvas 생성

delta가 있으면 wiki/canvas/ingest-delta.canvas를 생성합니다:

import sys
sys.path.insert(0, "scripts")
from ingest import generate_ingest_delta_canvas
generated = generate_ingest_delta_canvas()
if not generated:
    print("[ingest] canvas 생성 생략 (delta 없음)")

Read the full file on GitHub · 113 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. 6d ago First seen · 113 lines · 19 tokens per session scan A 8b1693502b36

Subscribe to this mod's changes

ingest is a command published in the GitHub repository kimsanguine/llm-brain (10 stars, last pushed 22d ago), licensed MIT. It adds 19 tokens to every session and 916 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other commands, from other repositories

onboard

Interview-based first-run setup for this LLM Wiki kit. Asks the essential questions (vault location/name, Mode A/B, mothership path, Core Context identity + reuse axes), then fills every placeholder and writes Core Context so the wiki knows you from day one. Activate when the user says "온보딩해줘", "처음 시작할게", "처음 시작"…

johnfkoo951/cmds-llm-wiki · 139 tokens

refresh-context

Re-snapshot Core Context.md from (optional) mothership system files + key personal essays. Run when mothership files change, when new essays signal philosophy shift, or when snapshot is older than 30 days.

johnfkoo951/cmds-llm-wiki · 43 tokens

ingest

Ingest a source (URL/file/text) into Raw Sources + compile 1015 Wiki pages, with mandatory user-purpose gate and mothership cross-linking.

johnfkoo951/cmds-llm-wiki · 33 tokens

audit

Audit the whole Wiki vault against 3 knowledge-integrity criteria — eligibility coverage, MOC-cluster consistency, confidence calibration. Produces a vault health report and queues high-priority pages for /verify.

johnfkoo951/cmds-llm-wiki · 41 tokens

verify

Verify a single Wiki page against 3 knowledge-integrity criteria — eligibility, consistency, confirmability. Writes verificationStatus back to the page; flags conflicts as disputed rather than deleting them.

johnfkoo951/cmds-llm-wiki · 37 tokens

lint

Run comprehensive wiki health check — orphans, broken links, contradictions, stale pages, index sync, MOC coverage, v2/v4/v5 frontmatter coverage, Core Context freshness, and cross-vault link integrity (mainVaultRelated/mainVaultCmds).

johnfkoo951/cmds-llm-wiki · 53 tokens