소스 정보
- 저장소
- ThomasMoreAI/legal-skills-open
- 최근 소스 활동
- 2026년 6월 1일 17:27
- 감지된 SKILL.md 언어
- 영어
- 스타
- 40
- 포크
- 4
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.
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/ThomasMoreAI/legal-skills-open --skill er명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Use when retrieving arbitration institutional rules (ICC, LCIA, SCC, SIAC, HKIAC, VIAC, МКАС/МАК при ТПП України, UNCITRAL) — fetching current version, verifying redaction applicable to the date of arbitration agreement, constructing URLs for official rule texts
Use when choosing the Polish legal regime for letters, requests, applications, complaints, petitions, public-information requests, KPA filings, PPSA complaints, RODO access requests, registry extracts, court-file access, tax/ZUS/cudzoziemcy/USC procedures, or professional lawyer letters. Prevents mixing UDIP, KPA, PPSA, RODO, registry, special-procedure, and advocate/radca letter regimes.
Use when preparing applications for recognition and enforcement of foreign arbitral awards in Poland, applications for setting aside arbitral awards under KPC art. 1205–1211, or opposing such applications — mapping Article V of the 1958 New York Convention to art. 1214–1215 of the Polish KPC, identifying grounds for refusal, structuring public policy arguments
SOC 직업 분류 기준
SKILL.md 표시 중
| name | er |
| title | AI4L - Main Skill for Evidence Review Creation and Auditing using @AGENTS |
| description | AI4L - Main Skill for Evidence Review Creation and Auditing using @AGENTS |
| author | forever-healthy |
| author_url | https://github.com/forever-healthy/AI4L/tree/main/.claude/skills/er |
| license | MIT |
| version | 0.1.0 |
| execution_mode | open |
| jurisdiction | general |
| practice | litigation |
| language | en |
Parse the user's input to determine which sub-command to execute
Set [args] to $ARGUMENTS
Note the [start_time] when beginning any command, and report the [time_taken] when done
All generated results go in [creation_dir] as .md files
Do not edit or modify any files outside [creation_dir]
Full lines formatted as line comments must be ignored when processing commands.
Create an evidence review (ER) using the @er-creator agent.
If no [args] are given {
usage: /er create {topic}Report: create: [topic]
@er-creator: [topic]
Wait until the agent finishes
Report: filename: [filename]
Audit an ER using the @er-auditor agent.
If no [args] are given {
Report: audit: [target_er]
@er-auditor: [target_er]
Wait until the agent finishes and returns the result
Report: target_er: [target_er]
Report: pass_rate: [pass_rate]
Audit and fix an ER using the @er-fixer agent.
If no [args] are given {
Report: fix: [target_er]
@er-fixer: [target_er]
Wait until the agent finishes and returns the result
Report: target_er: [target_er]
Report: pass_rate: [pass_rate]
Create a final QA file from all audits
If no [args] are given {
Report: combine: [target_er]
@er-combiner: [target_er]
Wait until the agent finishes and returns the result
Report QA file: [new_qa_filename]
Loops audit/fix cycles up to [max_audits] times until [needed_passes] show 100% pass rate.
If no [args] are given {
Report: iterate: [target_er]
Initialize {
Loop while [iteration] < [max_audits] and [consecutive_passes] < [needed_passes] {
@er-fixer: [target_er]
Wait until the agent finishes and returns the result
If the [pass_rate] is 100%, increment [consecutive_passes]; otherwise reset to 0
Increment [iteration]
Report: "Iteration [iteration]: Pass rate = [pass_rate]% ([consecutive_passes]/[needed_passes] consecutive passes needed)" }
@er-combiner: [target_er]
If [iteration] < [max_audits] {
Return: status: success
} else {
Return: status: failed
}
Return: target_er: [target_er]
Return: iterations: [iteration]
A create and multi-pass audit workflow.
Compares all ERs for a given intervention, typically from different AI models or versions, to determine which is strongest based on content quality and the latest QA audit results.
If no [args] are given {
Report: compare: [intervention]
Compare all ERs in [creation_dir] with a similar [intervention] in their frontmatter
Present a clear recommendation of which ER is strongest and why.