Summary
This analyzes LinkedIn profiles at target accounts and scores people on whether they'd make good internal champions for your product. It looks at six dimensions like role relevance, influence level, and personal stake, then ranks everyone with a 0-60 score and explains why they'd help you win the deal. The output includes warm intro scripts, personalization hooks from their recent activity, and discovery questions. It's built for account-based sales where you need to find someone who'll actually advocate for you internally, not just be friendly and passive. The scoring framework is surprisingly detailed, distinguishing champions from coaches from blockers.
Install to Claude Code
npx -y skills add onewave-ai/claude-skills --skill champion-identifier --agent claude-codeInstalls into .claude/skills of the current project.
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Files
SKILL.mdView on GitHub
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
- Gather inputs: the company, the solution being sold, and any known contacts or mutual connections. Ask for whatever is missing.
- Research the company and the department the solution touches: stage, recent news, likely pain points, decision-making style, and hiring signals.
- Identify 5-10 candidate individuals on LinkedIn within the relevant department and leadership chain.
- Score each candidate across the six dimensions (0-10 each, 0-60 total) per references/scoring-framework.md, citing concrete evidence for every score.
- Rank candidates best to worst, and flag anyone who is a blocker or coach rather than a champion using the warning signs.
- 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.
- Build the multi-threading plan and account map (DMU table, org chart) per references/multi-threading.md.
- Assemble the full report following references/output-template.md, filling every field with researched specifics and no placeholders.
- Recommend next steps using the phased plan in references/champion-development-plan.md.
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Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →First SeenJun 3, 2026
