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- benjam3n/reasoningtool
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- 2026년 3월 17일 15:35
- 감지된 SKILL.md 언어
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/benjam3n/reasoningtool --skill jm-journey-matching명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Route any input through a branching question tree to narrow down the optimal response strategy before writing. Two stages — PERCEIVE (classify input) then ACT (select response). Covers all prompt types.
Generate exhaustive guesses about user input using ALL search methods with coverage tracking. Guessing is SEARCH through possibility space. Tracks space created vs space covered to ensure comprehensive exploration.
Systematically evaluate and select from a set of guesses, options, or possibilities. Combines ARAW analysis with prioritization to determine which guesses are strong, weak, actionable, or eliminable.
| name | jm - Journey Matching |
| description | Given a current situation (goal, problem, state), find journeys |
| output | {"format":"prose"} |
Input: $ARGUMENTS
Before executing, identify which interpretation matches the user's input:
Interpretation 1 — Find journeys that match a current situation: The user has a goal, problem, or life situation and wants to find journey archetypes from the library that match, so they can learn what typically comes next and what to watch for. Interpretation 2 — Get guidance for a specific stage of a journey: The user already knows roughly what journey they're on but wants to understand where they are in it, what challenges to expect at this stage, and what transitions are ahead. Interpretation 3 — Compare alternative paths forward: The user is at a decision point where multiple journey patterns could apply and wants to see how different paths diverge, what each requires, and which fits their constraints.
If ambiguous, ask: "I can help with finding journeys that match your situation, getting stage-specific guidance for a journey you're on, or comparing alternative paths forward — which fits?" If clear from context, proceed with the matching interpretation.
Given a current situation (goal, problem, state), find journeys from the library that might apply. This enables learning from past patterns without reinventing every path.
Describe the current situation in semi-generalizable terms.
Avoid specifics: NOT: "I'm a 35-year-old software engineer at BigCorp wanting to start a fintech startup" BUT: "Person in established career seeking entrepreneurial transition in technical domain"
Capture:
Output: situation_characterization
Estimate the journey archetype:
Complexity:
Difficulty:
Predictability:
Frequency:
Output: archetype_estimate
What domain(s) does this situation belong to?
Domains:
What themes are present?
Themes:
Output: domain_and_themes
Search for matching journeys:
Primary match criteria:
Search strategy:
For each candidate:
Output: candidate_journeys
For each candidate journey, assess fit:
Structural fit:
Resonance fit:
Transfer feasibility:
Score each journey: Strong fit / Moderate fit / Weak fit / No fit
Output: fit_assessments
For each fitting journey:
Where in the journey are you now?
This tells you:
Output: position_in_journeys
For top-matching journeys, generate guidance:
What this journey suggests:
Variations to consider:
Confidence level:
Output: journey_guidance
Combine insights from all matching journeys:
Convergent patterns:
Divergent options:
Synthesized guidance:
Output: synthesized_recommendations
situation_summary:
archetype:
complexity: '[simple|complicated|complex|chaotic]'
difficulty: '[easy|challenging|hard|seemingly_impossible]'
current_state: '[Semi-generalized description]'
desired_state: '[If known]'
domains:
- domain1
- domain2
themes:
- theme1
- theme2
synthesis:
alternatives:
- path:
- Alt step 1
- '...'
when_to_use: '[Condition]'
convergent_patterns:
- '[Pattern that multiple journeys agree on]'
divergent_options:
- '[Option 1: from journey A]'
- '[Option 2: from journey B]'
recommended_path:
confidence: '[High|Medium|Low]'
reasoning: '[Why this path]'
steps:
- Step 1
- Step 2
- '...'
top_matching_journeys:
- guidance:
key_transition: '[Upcoming crucial moment]'
next_steps:
- Step 1
- Step 2
watch_for:
- Challenge 1
- Challenge 2
journey:
fit: '[Strong|Moderate|Weak]'
source: '[Original source]'
title: '[Journey name]'
why_matches: '[Explanation]'
your_position:
current_step: N
description: '[Where you are in this journey]'