본문으로 건너뛰기
cccskills
무료GitHub에 공개

champion-identifier

Analyze LinkedIn profiles in target accounts to identify potential internal champions. Evaluates role, career path, mutual connections, interests, and suggests personalization approach. Use when you need to find who will champion your solution internally.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구LinkedIn
설치 방법 보기

포함된 파일(8)

  • SKILL.md2.3 KB
  • references/champion-development-plan.md1.7 KB
  • references/meeting-prep.md1.5 KB
  • references/multi-threading.md1.5 KB
  • references/output-template.md4.7 KB
  • references/outreach-templates.md1.7 KB
  • references/playbook.md2.2 KB
  • references/scoring-framework.md1.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Champion Identifier

Find the internal champion most likely to advocate for the solution at a target account, using structured LinkedIn and account analysis.

Contents

  • references/scoring-framework.md - champion profile, six scoring dimensions, total-score bands, warning signs
  • references/output-template.md - the full Markdown report structure to produce
  • references/outreach-templates.md - warm-intro, forwardable, direct, and opening-line messages
  • references/meeting-prep.md - discovery and qualification questions, red flags
  • references/multi-threading.md - sequence, coverage map, DMU table, org-chart format
  • references/champion-development-plan.md - five-phase plan with success metrics
  • references/playbook.md - tips, best practices, trigger phrases, worked example

Workflow

  1. Gather inputs: the company, the solution being sold, and any known contacts or mutual connections. Ask for whatever is missing.
  2. Research the company and the department the solution touches: stage, recent news, likely pain points, decision-making style, and hiring signals.
  3. Identify 5-10 candidate individuals on LinkedIn within the relevant department and leadership chain.
  4. Score each candidate across the six dimensions (0-10 each, 0-60 total) per references/scoring-framework.md, citing concrete evidence for every score.
  5. Rank candidates best to worst, and flag anyone who is a blocker or coach rather than a champion using the warning signs.
  6. For the top candidates, choose an outreach path (warm intro vs. direct) and draft the message from references/outreach-templates.md, plus personalization hooks and meeting prep from references/meeting-prep.md.
  7. Build the multi-threading plan and account map (DMU table, org chart) per references/multi-threading.md.
  8. Assemble the full report following references/output-template.md, filling every field with researched specifics and no placeholders.
  9. Recommend next steps using the phased plan in references/champion-development-plan.md.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OneWave-AI/claude-skills3662026년 10월 20일 업데이트

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OneWave-AI/claude-skills3662026년 10월 20일 업데이트

Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OneWave-AI/claude-skills3662026년 10월 20일 업데이트

Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OneWave-AI/claude-skills3662026년 10월 20일 업데이트

Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OneWave-AI/claude-skills3662026년 10월 20일 업데이트

Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OneWave-AI/claude-skills3662026년 10월 20일 업데이트

OneWave-AI의 스킬 모두 보기

이 스킬의 문제 신고