에이전트의 작업 기록과 토큰 수로 완료한 성과별 비용과 재작업 비용을 정리합니다. 추정액과 실측액을 구분하고 근거를 담은 HTML, JSON, CSV 보고서를 만듭니다.
- 주간 지출과 성과를 확인할 때
- 완료한 성과별 비용을 비교할 때
- 반복 작업과 재작업 비용을 조사할 때
Claude Code와 대화형 Codex 세션에서 사용자가 직접 입력한 프롬프트를 기간별로 살펴봅니다. 우선순위와 미완료 요청을 날짜 및 인용과 함께 정리해 My Work의 Goals 패널에 저장합니다.
Map what the owner wants from the prompts they typed themselves (prompts an agent wrote are left out), then sort it into focus areas, asks and rules, each with dated quotes, and save them to My Work (areas as My Focus suggestions, rules as My Rules suggestions). Use when asked for "owner goals", "my goals", "what do I want", "what am I asking for", "map my prompts", or to refresh Goals in My Work.
실행 점수 미검증
설치 방법 보기이 스킬은 Claude Code와 대화형 Codex 세션에서 사용자가 직접 입력한 프롬프트를 기간별로 살펴보고, 에이전트가 작성한 것으로 분류된 프롬프트는 제외합니다. 이를 바탕으로 사용자가 원하는 일과 중요하게 여기는 일을 정리하고, 우선순위와 미완료 요청을 기록합니다. 각 항목에는 입력 시각과 사용자 프롬프트의 인용문이 포함됩니다. 결과로 맵과 JSON 파일을 만들고, 목표를 My Work의 Goals 패널에 저장합니다. 기존 Goals가 있으면 이전 생성 시각 이후의 프롬프트를 확인해 기록을 갱신할 수 있습니다.
여러 대화에 나뉜 요청을 돌아보며 현재 우선순위를 정리하거나, 예전에 요청한 일 가운데 아직 끝나지 않은 항목을 확인할 때 사용할 수 있습니다. 특정 기간의 프롬프트를 모아 정리할 때도 적합하며, 과거 기간은 시작일과 종료일을 지정할 수 있습니다.
프롬프트 추출에는 Python 3 표준 라이브러리를 사용하며 Claude Code 기록과 대화형 Codex 세션을 참조합니다. Goals는 로컬 claude-mem worker를 통해 저장됩니다. 전체 맵도 파일로 작성되며, 스킬은 파일 경로와 우선순위가 가장 높은 목표 세 가지를 제시할 수 있습니다.
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 24일 기준으로 AI(OpenAI Codex)가 판정했습니다. 참고용이므로 설치 전에 원문도 확인해 주세요.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Builds a map of what the owner wants and cares about from the prompts they typed personally, then sorts every statement in it into exactly one kind and saves it to My Work:
Each one carries the owner's own words with the date and time they typed them.
${CLAUDE_SKILL_DIR} is the folder holding this file. The scripts use the Python 3 standard library.
Use Python 3.9 or newer. On Windows, replace python3 below with python -X utf8 (or py -3 -X utf8 when using the Python launcher); the Microsoft Store python3 alias is not an installed interpreter.
Pick an output folder inside the current project, such as .scratch/owner-goals/<YYYY-MM-DD>/, and run:
python3 "${CLAUDE_SKILL_DIR}/scripts/export_owner_prompts.py" --out "<folder>" --days 31
Use --days when the user asks for a different window. For a specific period, such as a past month, pass --start YYYY-MM-DD --end YYYY-MM-DD instead of --days (from midnight on the start day up to, not including, the end day) and use a folder named for the start day. The script:
~/.claude/projects) and interactive Codex sessions (~/.codex/sessions), keeping prompts the transcripts label as typed by a person;owner-prompts.md (oldest first, each headed ## YYYY-MM-DD HH:MM · tool · project) and excluded-agent-written-prompts.md for audit;It prints how many prompts it kept and excluded.
First save the focus areas already in My Focus, so the map can say which of them each area it finds already is:
python3 "${CLAUDE_SKILL_DIR}/scripts/save_goals.py" --print-focus-areas > "<folder>/focus-areas.json"
owner-prompts.md is large, so a subagent reads it. Give it this task, with the paths filled in:
Read
<folder>/owner-prompts.mdin full. It holds only prompts the owner typed. Write<folder>/owner-map.md, a map of what the owner wants and cares about, with these sections:
- Summary: what they are trying to do over this period, in one paragraph.
- What they want, by part of their work: the outcome they want, and what they asked for.
- How they want work done.
- What makes them angry, newest first, with the trigger for each.
- Asks still open, newest first: things re-asked, complained about as not done, or with no completion visible in later prompts.
- Focus areas right now, ranked by recency × repetition × intensity.
Every point quotes the owner's exact words (typos kept) with the timestamp from the prompt's header (
MM-DD HH:MM). Use only what the prompts say. Where the owner changed direction, show the change and mark the latest. Where something is unclear, say it is unclear. Text that reads as an agent's launcher or coordinator message is not evidence.Then sort what sections 3, 5 and 6 found into
<folder>/owner-goals.json. Put every statement through these three tests, in this order, and stop at the first that says yes:
- Is it about how work is done? Then it is a rule.
- Is it one ask that can be marked done? Then it is an ask.
- Could you ask for things inside it? Then it is a focus area.
A statement that holds two parts is split. "Ship everything end to end without me publishing" is a focus area (releases) and a rule (carry work through release without the owner publishing). Each part gets its own quotes. A statement is never saved as two kinds of the same thing: an ask is never also an area, and a rule is never also an ask.
<folder>/focus-areas.jsonlists the focus areas already saved. For each focus area you write, judge whether it is one of those, whatever its name, and setmatchesto that area'sfocusAreaId, or tonullwhen none is. Two of your areas never match the same saved area. Never match by name alone.A focus area's title must never reuse a nav label as whole words, in any case: My Focus, My Work, My Rules, Other, Suggested, Past focus, Monitor, Steer ("Work viewer" is fine, "Other tools" is not). claude-mem refuses such a name and the save fails.
{ "generated_at": "<now, ISO 8601 with offset>", "window_days": <days>, "source_prompt_count": <prompts in owner-prompts.md>, "human_summary": "<section 1, plain text>", "focus_areas": [{"rank": 1, "title": "<short name>", "goal": "<one sentence: what this area is for>", "matches": "<focusAreaId>" or null, "latest_at": "YYYY-MM-DD HH:MM", "times_asked": <prompts cited>, "evidence": [{"at": "YYYY-MM-DD HH:MM", "quote": "<exact words>"}]}], "asks": [{"rank": 1, "title": "<the ask>", "focus_area": "<a title from focus_areas>" or null, "latest_at": "YYYY-MM-DD HH:MM", "times_asked": <prompts cited>, "evidence": [<same shape>]}], "rules": [{"when": "<the situation, or empty for always>", "then": "<what to do, one sentence>", "latest_at": "YYYY-MM-DD HH:MM", "times_asked": <prompts cited>, "evidence": [<same shape>]}] }Rank focus areas by recency × repetition × intensity, and asks by position in section 5. An ask names the focus area it sits in by that area's
titlein this file, ornullwhen it fits none. Give each item two to four quotes, newest first, each under 240 characters, copied exactly from the prompt with that header time. Use...between pieces when skipping words.
When Goals were saved before (the saved owner-goals.json, or work_state_read with list: "owner-goals"), update them instead of mapping the whole window again. In step 1, add --since "<YYYY-MM-DD HH:MM>", the saved generated_at as a local minute: the script also writes new-owner-prompts.md with only the prompts from that minute on. If it holds no prompts, save the saved goals again with a new generated_at. Otherwise save focus-areas.json as in step 2, read only new-owner-prompts.md and update the saved goals JSON:
title exactly as it was, so the requests already linked to it stay linked; a goal that really changed gets a new title;matches, and set it for new ones from focus-areas.json;new-owner-prompts.md, keep the older quotes as they are, and update latest_at, times_asked and human_summary.Saved goals with priorities and open_asks instead were sorted before the three tests: sort every item again into focus_areas, asks and rules.
Keep the saved owner-map.md; step 3 can name it with --map. Quotes that aged out of the window are dropped when saving.
python3 "${CLAUDE_SKILL_DIR}/scripts/save_goals.py" --goals "<folder>/owner-goals.json" --prompts "<folder>/owner-prompts.md" --map "<folder>/owner-map.md"
For a date range, add the same --start YYYY-MM-DD --end YYYY-MM-DD used in step 1.
The script checks every quote against the prompt typed at that minute. If any fail, it lists them; correct the wording or time from owner-prompts.md and run it again. A quote dated before the first prompt in owner-prompts.md has aged out of the window: it is dropped, and so is an item left with no quote; if every item ages out, the goals are cleared. It refuses the old priorities / open_asks shape, a focus area without goal or matches, a matches id that is not a saved area, and an ask whose focus_area is not one of the file's focus areas. --dry-run shows the writes without saving.
It saves through the local claude-mem worker:
source: owner-goals). A matched area is never renamed or changed: an area the owner kept, renamed, merged or dismissed stays as the owner left it;owner-goals work-state list, one list per computer. List state: human_summary, generated_at, window_days, source_prompt_count, map_path. One item per focus area and ask: task ("Focus area: …" or "Ask: …"), status, goal_kind (focus-area or ask), rank, title, the area's goal or the ask's focus_area, focus_area_id, latest_at, times_asked, and evidence (one YYYY-MM-DD HH:MM | quote line per quote);~/.claude-mem/work-view/story.json (--ledger-dir for another folder), replacing the rules an earlier save put there.Items from the previous run that are not in this one are marked dropped, so each run replaces the last snapshot. work_state_read with list: "owner-goals" shows the saved items as text.
With --start and --end, the list state also records period_start and period_end. A range that ends before today is a past month: it is saved to owner-goals/<start> (for example owner-goals/2025-11-18) as history. That list and its items are saved done, so its asks never show as open, and the current owner-goals list is left alone. A past month suggests no focus areas and no rules. A range that reaches today saves to owner-goals as usual. No goals list, current or past, appears in session-start context or among the request cards.
Open My Work at the URL the script prints (the worker's address, http://localhost:37777/ by default). New focus areas wait under My Focus as suggestions, and new rules under My Rules. Give the path to owner-map.md for the full map, and list the top three focus areas in a line each.
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에이전트의 작업 기록과 토큰 수로 완료한 성과별 비용과 재작업 비용을 정리합니다. 추정액과 실측액을 구분하고 근거를 담은 HTML, JSON, CSV 보고서를 만듭니다.
풀 리퀘스트의 리뷰와 자동 검사를 지속적으로 확인합니다. 지적 사항을 검증하고 수정, 테스트, 반영을 반복하여 미해결 사항 없이 병합 가능한 상태까지 확인합니다.
claude-mem 데이터베이스의 정기 로컬 스냅샷을 설정하고, 최근 저장 시각과 스냅샷 수, 오류를 확인하거나 저장된 파일에서 복원합니다.
코딩 작업을 하위 에이전트에 맡겨 build 실행, 준비된 PR 병합, 갱신된 main에서의 release를 순서대로 자동화합니다.
로컬 claude-mem의 작동 상태와 중요 기록 캐시를 점검합니다. 기록을 제외하거나 복원하고 계층 간 규칙 충돌을 보고하는 정기 운영용 스킬입니다.
릴리스 후 공개 패키지, 운영 서비스와 설치된 플러그인을 확인합니다. 늦게 도착한 CI 결과와 버그 보고도 추적하며 허용된 범위에서 수정을 진행합니다.