菜单
AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting. Use when asked to analyze sales calls, build revenue attribution models, or generate client reports.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when adversarially reviewing a document that makes formal or technical claims — math derivations, physics papers, statistical analyses, benchmark reports, whitepapers. Inventories every equation and quantitative claim, verifies each AS NAMED in the text (never a paraphrase or a neighboring statement), and classifies VERIFIED / REFUTED / UNVERIFIABLE / VACUOUS. Triggers on "check this paper", "verify these claims", "is this derivation right", "review this proof", "audit this benchmark", "does the math hold up". For source-code review see code-review-and-quality; for skill/agent library audits see skill-library-review; for content quality scoring see content-ops.
Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".
Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.
Conducts multi-axis code review across correctness, readability, architecture, security, and performance. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Triggers on "review my PR", "review this diff", "code review", "review this changeset", "is this ready to merge", "pre-merge review".
Estimate the full development cost of an existing codebase from lines of code, architectural complexity, and team-composition overhead. Use for 'how much would this cost to build', 'what did this codebase cost', development-cost or build-cost estimates, calendar-time estimates, and Claude/AI ROI on a delivered codebase. Estimates by measured LOC and complexity, not by ticket volume.
Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.
| name | revenue-intelligence |
| description | AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting. Use when asked to analyze sales calls, build revenue attribution models, or generate client reports. |
| when_to_use | Use when extracting structured insights from Gong sales call transcripts (objections, buying signals, competitive mentions), proving content ROI by mapping content pieces to pipeline and closed revenue, generating unified client reports from GA4 + HubSpot + Ahrefs + Gong, identifying content gaps in the buyer journey, or detecting anomalies across marketing metrics. Not when: drafting or optimizing cold outbound sequences — even if seeded from Gong call data — use outbound-engine. This skill produces follow-up *drafts* from a specific call's context; sequence building, delivery, and optimization are outbound-engine's. Not when: the content-gap analysis is SEO/keyword-driven (competitor coverage, search demand) — use seo-ops; this skill's gap analysis is buyer-journey/pipeline-driven. |
| compatibility | Requires Bash (Python 3 where scripts are invoked). Works in Claude Code and Codex via install.sh. |
AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting.
| Tool | Purpose |
|---|---|
scripts/gong_insight_pipeline.py | Extract objections, buying signals, and competitive mentions from call transcripts |
scripts/revenue_attribution.py | Map content to closed revenue with first-touch, linear, and time-decay models |
scripts/client_report_generator.py | Generate unified GA4 + HubSpot + Ahrefs + Gong client reports |
