소스 정보
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- nearai/ironclaw
- 최근 소스 활동
- 2026년 4월 17일 16:37
- 감지된 SKILL.md 언어
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- 스타
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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/nearai/ironclaw --skill new-project명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Use when adding or changing a Reborn integration — a new extension, a channel surface, model-callable tools, or a shared auth provider — or when deciding whether something is "a channel", "an extension", or "a tool". Maps the unified extension model (NEA-25) to the exact manifest sections, crates, seams, and tests.
Use when creating or editing agent guidance in this repo — anything under .claude/ (skills, commands, rules), AGENTS.md or CLAUDE.md files, or the runtime skills/ directory — or when guidance is found citing files, branches, or checks that no longer exist.
Use when adding or reviewing tests for Reborn behavior — choosing a test tier, covering a bug fix, testing model/tool-choice behavior, touching tests/integration or tests/fixtures/llm_traces, or when a test needs Postgres, Docker, or a live LLM.
| name | new-project |
| version | 0.2.0 |
| description | Create and structure a new autonomous project — "/new-project <what project does>" |
| activation | {"keywords":["project","create project","new project","set up project","autonomous workspace","campaign","department","company project"],"patterns":["create a (new )?project","set up.*project","organize.*into.*project","new.project","/new.project"],"tags":["project-management","organization","goals"],"max_context_tokens":2000} |
Create an autonomous project workspace using memory_write and mission_create.
Given the user's description of what the project does, derive a short slug (lowercase, hyphens, e.g. ai-research). Then execute these steps sequentially (one tool call at a time — do NOT batch calls that depend on each other):
memory_write(target: "projects/{slug}/AGENTS.md", content: "# {Project Name}\n\n{What the agent should know about this project: domain, stakeholders, priorities, constraints, tools/APIs to use.}")
This file is loaded into the system prompt for every mission in this project. Make it specific and actionable.
memory_write(target: "projects/{slug}/context.md", content: "# {Project Name} — Context\n\n## Overview\n{What the project is and why it exists.}\n\n## Current State\n{What is known so far.}")
memory_write(target: "projects/{slug}/goals.md", content: "# Goals\n\n- Goal 1\n- Goal 2\n...")
Include measurable targets when possible. If the project would benefit from tracked metrics, add a metrics section:
## Metrics
| Metric | Unit | Target | How to measure |
|--------|------|--------|----------------|
| {name} | {unit} | {target} | {evaluation instruction — tell the agent HOW to check this: API call, file to read, command to run} |
Create recurring missions scoped to the project. Use the project name or slug as project_id (the engine resolves it to the correct project):
mission_create(name: "...", goal: "...", cadence: "daily", project_id: "{Project Name}")
Choose appropriate cadences: hourly, daily, weekly, monthly, or cron expressions like 0 9 * * 1-5.
projects/
{slug}/
AGENTS.md # Agent instructions (loaded into system prompt)
context.md # Background knowledge, current state
goals.md # Goal breakdown with optional metrics
research/ # Research and analysis outputs
reports/ # Generated reports
project_id when creating missions. Without it, missions land in the Default project.