From belt
Run a simulated expert panel on a strategic question — brief 4-8 named experts in parallel, synthesize into consensus table
How this skill is triggered — by the user, by Claude, or both
Slash command
/belt:expert-panelThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Run a simulated expert panel review on a strategic question. Each expert is briefed independently via a subagent, responds from their published framework, and the results are synthesized into a consensus table with actionable insights.
Run a simulated expert panel review on a strategic question. Each expert is briefed independently via a subagent, responds from their published framework, and the results are synthesized into a consensus table with actionable insights.
Frame the question — Write one clear question that experts can react to. Include:
Select 4-8 experts — Choose named experts whose published frameworks are directly relevant. Each expert must bring a distinct lens. Good panel composition covers at least 3 of: positioning, growth/PMF, pricing, moat/competition, DX, brand, network effects, offer design.
Strong expert choices and their lenses:
Brief each expert as a parallel subagent — Launch all agents simultaneously. Each prompt must be self-contained:
model: "sonnet" for cost efficiency — expert opinions don't need opusSynthesize into a consensus table — When all agents return:
| Expert | Verdict (2-4 words) | Key insight (one sentence) |
|---|
Then identify:
Present to the user — Show each expert's response as a short summary (not the full subagent output), then the consensus table, then tensions and actions.
If the user wants to go deeper — Run a second round with the same or different experts. Feed Round 1 conclusions as additional context. This catches experts building on each other's insights.
npx claudepluginhub belt-sh/skills --plugin beltCreates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.