consulting-analysis
Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts,
npx skills add majiayu000/claude-skill-registry --skill consulting-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Produces professional, consulting-grade research reports in Markdown, covering market, consumer, brand, financial, industry, competitive, investment, and macro analyses. Operates in two phases: (1) Analysis Framework Generation with chapter skeletons, data requirements, and visualization plans; (2) Report Generation after data collection to synthesize inputs into a final report. Output adheres to McKinsey/BCG voice standards and uses the specified output locale for language.
How it works
Phase 1 requires a research subject to generate a complete analysis framework, including chapter skeletons, data requirements per chapter, analysis logic, and visualization plans. Phase 2 collects data from downstream skills and synthesizes a final report with structured narratives and embedded charts. The skill selects professional frameworks (e.g., SWOT, PESTEL, Porter's Five Forces, VRIO) appropriate to the domain and explicitly maps each chapter to a framework, ensuring data feasibility and depth over breadth. The framework includes a detailed chapter skeleton, data requirements, and visualization plans, all formatted in Markdown. Citations are generated inline per GB/T 7714-2015 standards. Language follows the configured output_locale.
When to use it
- When user requests a market analysis, consumer insight report, financial analysis, industry research, or any consulting-grade analytical report.
- When the user provides a subject and needs a structured analysis framework before data collection.
- When the user provides data summaries, analysis frameworks, or chart files to be synthesized into a report.
- When a professional consulting-style research report is required.
What it can touch
The skill references the downstream data collection process and supports embedding pre-generated charts and constructing comparison tables as part of the framework and final report. It relies on phase 2 inputs from other skills to populate the final narrative and visuals.
Caveats
- The skill description emphasizes two-phase delivery and professional tone but does not guarantee specific outcomes without downstream data.
- The guidance requires adherence to established consulting voice and structured framework generation, with explicit mapping of chapters to chosen frameworks.
- All inputs and outputs should be in Markdown and aligned with the specified language locale.
# Professional Research Report Skill ## Overview This skill produces professional, consulting-grade research reports in Markdown format, covering domains such as **market analysis, consumer insights, brand strategy, financial analysis, industry research, competitive intelligence, investment research, and macroeconomic analysis**. It operates across two distinct phases: 1. **Phase 1 — Analysis Framework Generation**: Given a research subject, produce a rigorous analysis framework including chapter skeleton, per-chapter data requirements, analysis logic, and visualization plan. 2. **Phase 2 — Report Generation**: After data has been collected by other skills, synthesize all inputs into a final polished report. The output adheres to McKinsey/BCG consulting voice standards. The report language follows the `output_locale` setting (default: `zh_CN` for Chinese). ## Core Capabilities - **Design analysis frameworks** from scratch given only a research subject and scope - Transform raw data into structured, high-depth research reports - Follow the **"Visual Anchor → Data Contrast → Integrated Analysis"** flow per sub-chapter - Produce insights following the **"Data → User Psychology → Strat
- Overview
- Core Capabilities
- When to Use This Skill
- Purpose
- Phase 1 Inputs
- Phase 1 Workflow
- Step 1.1: Understand the Research Subject
- Step 1.2: Select Analysis Frameworks & Models
- Step 1.3: Design Chapter Skeleton
- Step 1.4: Define Data Query Requirements Per Chapter
- Step 1.5: Define Visualization & Content Structure Per Chapter
- Step 1.6: Output Complete Analysis Framework
- Phase 1 Quality Checklist
- Phase 2 Inputs
What does the consulting-analysis skill do?
Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This skill operates in two phases — (1) generating a structured analysis framework with chapter skeleton, data query requirements, and analysis logic, and (2) after data collection by other skills, producing the final consulting-grade report with structured narratives, embedded charts,
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill consulting-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.