km-workflow

6-phase workflow for content extraction, analysis, and export to Obsidian/Notion.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/treylom/knowledge-manager/km-workflow
Any agent
npx skills add treylom/knowledge-manager --skill km-workflow
Clone the repo
git clone --depth 1 https://github.com/treylom/knowledge-manager

Made for: Claude Code, Codex.

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,392 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 $0.00022 $0.03392
Opus 5 $0.00011 $0.01696
Sonnet 5 $0.00004 $0.00678
Haiku 4.5 $0.00002 $0.00339

Measured yesterday against content hash a11f8886bc2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

km-workflow 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 yesterday.

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.

.agent/skills/km-workflow/SKILL.md · 290 lines

How it starts

The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Knowledge Manager Workflow

Complete 6-phase workflow guide for content processing


Workflow Overview

Phase 0: Load Configuration
    ↓
Phase 1: Detect Input Source
    ↓
Phase 1.5: Collect User Preferences
    ↓
Phase 2: Extract Content
    ↓
Phase 3: Analyze Content
    ↓
Phase 4: Select Output Format
    ↓
Phase 5: Execute Export
    ↓
Phase 6: Verify and Report

🛑 MANDATORY WORKFLOW - 절대 건너뛰지 마세요!

Antigravity/Gemini CLI에서 반드시 실행:

STEP 1: 사용자 선호도 확인 (Phase 1.5) - 필수!

콘텐츠 처리 전 반드시 아래 질문을 사용자에게 물어야 합니다:

📊 상세 수준: 1.요약 / 2.보통 / 3.상세
🎯 중점 영역: A.개념 / B.실용 / C.기술 / D.인사이트 / E.전체
📝 노트 분할: ①단일 / ②주제별 / ③원자적 / ④3-tier
🔗 연결 수준: 최소 / 보통 / 최대

기본값(3.상세, E.전체, ④3-tier, 최대)을 사용하시겠습니까?

💡 3-tier란? 개요 노트 + 주제별 노트 + 원자적 노트로 계층 구조화

소셜 미디어(Threads/Instagram) URL인 경우 추가 질문:

🔄 답글 수집 범위:
  1) depth=1: 직접 답글만 (빠름)
  2) depth=2: 답글의 답글까지 (더 완전한 맥락)

⚠️ 이 단계를 건너뛰면 안 됩니다!

  • 사용자가 "빠르게", "기본으로" 등 퀵 프리셋 키워드를 사용한 경우만 생략 가능
  • 그 외 모든 경우: 반드시 질문 후 진행

STEP 2-0: 저장 루트·위치 결정 (Phase 3.4) - 필수! 🔴

저장 루트는 backend 설정이 아니라 사용자가 연 프로젝트가 정한다. ChatGPT Work 로컬 프로젝트에서는 host가 제공하는 primary 연결 폴더, Codex CLI에서는 대화를 시작한 작업 디렉터리, IDE에서는 선택한 workspace root를 target_root로 기록한다. ChatGPT 프로젝트(로컬 프로젝트 아님)는 컴퓨터 폴더에 직접 접근하지 못하므로 target_root를 추측하지 않는다.

우선순위: 사용자가 이번 요청에서 명시한 대상 폴더 > host가 제공한 primary project/workspace root > project-scoped 요청이 아닐 때만 km-config.json backend. 사용자가 명시적으로 다른 연결 폴더나 Notion을 선택한 경우는 그 지시를 따른다.

  1. 루트 확정: target_root = realpath(host_project_root)로 canonicalize한다. host가 primary root와 shell cwd를 구분하면 cwd를 프로젝트 루트로 추정하지 않는다. project-scoped 요청인데 root를 확인할 수 없으면 저장을 중단하고 대상 폴더를 요청한다.
  2. 구조 선독 (MUST-read): ${target_root}/VAULT-STRUCTURE.md가 있으면 경로를 정하기 전에 반드시 read하고 그 구조(예: 직군/사업/연도/문서종류)에 맞는 폴더를 결정한다. 있으면 ${target_root}/MOC-Map.md도 read하여 기존 허브·관련 문서 연결에 사용한다. 사용자가 경로를 지정했으면 그 지시가 우선이다. read 실패·부재 시 새 최상위 폴더를 추정 생성하지 말고, 명시 경로가 없으면 target root에 저장하고 그 fallback을 보고한다.
  3. 경로 봉쇄: relativePath를 정규화한 뒤 candidate_path = realpath(parent) + filename을 계산한다. candidate_pathtarget_root 밖이면 저장하지 않는다(.., 절대경로, symlink escape 포함).
  4. 도구는 루트 뒤에 선택: Obsidian CLI/MCP의 실제 연결 볼트를 canonicalize하여 target_root와 동일하다고 확인한 때만 사용한다. 불일치·확인 불가면 파일 쓰기 도구로 target root 안에 저장한다.
  5. Provenance: 최종 보고에 구조 문서 read 결과, target_root, 선택한 상대 경로, 저장 후 관측한 actual_saved_path를 남긴다.

Read the full file on GitHub · 290 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. yesterday First seen · 290 lines · 22 tokens per session scan A a11f8886bc2b

Subscribe to this mod's changes

km-workflow is a skill published in the GitHub repository treylom/knowledge-manager (227 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 3,392 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 2026-09-01.

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