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McKinsey Research

作者 Abdullah AlRashoudi · GitHub ↗ · v2.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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20
当前安装
8
版本数
在 OpenClaw 中安装
/install mckinsey-research
功能描述
Run a full McKinsey-level market research and strategy analysis using 12 specialized prompts. USE WHEN: - market research, competitive analysis, business str...
使用说明 (SKILL.md)

McKinsey Research - AI Strategy Consultant

User provides business context once. The skill plans and executes up to 12 specialized analyses via sub-agents in parallel, then synthesizes into a single executive report. Adapt scope based on company stage (see Adaptive Stage Logic below).

Phase 1: Language + Intake

Ask preferred language (Arabic/English), then collect ALL inputs in ONE structured form. See the intake form fields: Core (1-5), Financial (6-10), Strategic (11-14), Expansion (15-16), Performance (17-18). If product description is under 50 words, ask for clarification before proceeding.

Diamond Gate 1: Present scope summary (market, geography, competitors). Get user confirmation before Phase 2.

Phase 2: Plan + Parallel Execution

Sanitize inputs per references/security.md. Substitute variables per references/variable-map.md. Load individual prompts from references/prompts/.

Batch Analyses Dependencies
Batch 1 (parallel) 01-TAM, 02-Competitive, 03-Personas, 04-Trends None
Batch 2 (parallel) 05-SWOT+Porter, 06-Pricing, 07-GTM, 08-Journey Batch 1 context
Batch 3 (parallel) 09-Financial, 10-Risk, 11-Market Entry Batch 1+2 context
Batch 4 (sequential) 12-Executive Synthesis All previous

Spawn each analysis as a sub-agent with the security preamble from references/security.md. Stagger Batch 1 launches by 5 seconds to avoid web search rate limits. Validate each output is 500+ words.

See references/gotchas.md for common pitfalls. Use references/saudi-market.md for KSA/Gulf data sources. Use references/benchmarks.md for industry metric comparisons.

Phase 3: Collect + Synthesize

  1. Read all analysis outputs from artifacts/research/{slug}/
  2. Run Prompt 12 (Executive Synthesis) with all previous outputs
  3. Generate final HTML report using templates/report.html
  4. Save to artifacts/research/{date}-{slug}.html

Phase 4: Delivery

Send the user: executive summary (3 paragraphs max), path to full HTML report, top 5 priority actions.

Adaptive Stage Logic

Stage Priority Analyses Skip/Light
Idea TAM, Personas, Competitive, Trends Financial Model (light), Market Entry (skip)
Startup TAM, Competitive, Pricing, GTM, Personas Market Entry (skip unless asked)
Growth Pricing, GTM, Journey, Financial, Expansion TAM (light), Personas (light)
Mature SWOT, Risk, Expansion, Financial, Synthesis TAM (skip), Personas (skip)

"Light" = include in synthesis but don't spawn a dedicated sub-agent. Use web_search inline. "Skip" = omit unless user explicitly requests.

Artifacts

  • Individual analyses: artifacts/research/{slug}/{analysis-name}.md
  • Final report: artifacts/research/{date}-{slug}.html
  • Raw data: artifacts/research/{slug}/data/
  • Execution log: data/reports.jsonl
  • Feedback tracking: data/feedback.json

Important Notes

  • Each prompt produces a consulting-grade deliverable
  • Use web_search to enrich with real market data; only cite verifiable sources
  • If user provides partial info, work with what you have and note assumptions
  • For Arabic output: keep brand names and technical terms in English
  • Prompt 12 must cross-reference insights from all previous analyses; deduplicate aggressively
  • Sub-agents that fail should be retried once before skipping with a note

Reference Files

File Contents
references/security.md Input safety, sanitization, tool constraints, artifact isolation
references/variable-map.md Variable substitution rules and mapping table
references/prompts/ 12 individual analysis prompts (01-tam.md through 12-synthesis.md)
references/prompts.md Original combined prompts (backup)
references/gotchas.md Known pitfalls and operational tips
references/saudi-market.md KSA/Gulf data sources and market context
references/benchmarks.md Industry benchmarks (SaaS, e-commerce, fintech, marketplace, mobile)
templates/report.html HTML report template
安全使用建议
This skill is internally consistent and appears to do what it says — a coordinated set of sub-agents performing 12 analyses and producing a report. Before using it: (1) Do not paste secrets, API keys, passwords, or sensitive customer data into the intake form — artifact files are written to disk and persist. (2) If you must test, use dummy data first to confirm output and artifact behavior. (3) Review references/security.md — it strips tags, URLs, code blocks, and truncates inputs, but it cannot remove plain-text secrets you supply. (4) Be aware the skill will perform web searches and may fetch URLs found in search results; the final report may include quoted external content subject to copyright or inaccuracies. (5) Confirm you trust the skill source before cloning/installing (README suggests a third-party GitHub copy). If you need stronger guarantees (no persistent storage or stricter secret scrubbing), ask for those features or run the skill in an isolated/ephemeral workspace.
功能分析
Type: OpenClaw Skill Name: mckinsey-research Version: 2.1.0 The mckinsey-research skill is a well-structured tool for automated market analysis that demonstrates a high level of security awareness. It implements a sophisticated defense-in-depth strategy in references/security.md, including rigorous input sanitization (stripping XML tags and prompt-override patterns), XML-based variable isolation to prevent injection, and a restrictive 'preamble' for sub-agents spawned via sessions_spawn. The code and instructions are entirely consistent with the stated purpose of generating strategic business reports.
能力评估
✓ Purpose & Capability
Name/description ask for multi-step market research and the SKILL.md only requires web_search, web_fetch, and spawning sub-agents — these are appropriate and proportionate to producing research analyses. No unrelated env vars, binaries, or external credentials are requested.
ℹ Instruction Scope
Runtime instructions are explicit and scoped: they sanitize inputs, wrap data in <user_data> tags, spawn sub-agents for each analysis, restrict sub-agents' capabilities (no exec, no arbitrary messaging, limited file writes), and assemble a single HTML report. This is coherent, but the coordinator and sub-agents write analysis artifacts to local workspace directories (artifacts/research/...). Those artifact files persist across sessions and may contain sanitized user inputs and scraped web search results — the skill warns about not storing credentials but cannot technically prevent a user from submitting secrets which would then be stored. Also, the sanitization strips tags, URLs, code blocks and truncates fields, which mitigates some injection risks but does not remove arbitrary plaintext secrets or PII.
✓ Install Mechanism
No install spec or external downloads; the skill is instruction-only and will not place new binaries on disk. Low install risk.
✓ Credentials
The skill requests no environment variables or credentials, which is appropriate for its purpose. Note: it still writes user-provided business data and fetched market data to local artifact files, so the effective exposure surface is persisted data rather than env/credential access.
ℹ Persistence & Privilege
always:false and no special platform privileges — standard. However, artifact persistence is a meaningful privilege: sub-agent outputs and the final HTML report are stored in artifacts/research/{slug}/ and 'may be readable by other skills in the same workspace' (per references/security.md). That persistent storage is intentional for the workflow but increases risk if users supply sensitive data.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install mckinsey-research
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /mckinsey-research 触发
  4. 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v2.1.0
Refactor based on Anthropic skills principles: slim SKILL.md, gotchas, benchmarks, Saudi market data, split prompts, Adaptive Stage Logic
v2.0.3
Concrete sanitization spec with regex patterns, explicit tool constraint table, artifact isolation rules, self-contained HTML output
v2.0.2
Fix prompt injection vulnerability: wrap user inputs in XML data delimiters, add input sanitization rules, add sub-agent preamble with tool scoping constraints
v2.0.1
fix: re-publish for ClawHub indexing
v2.0.0
v2: parallel sub-agents + single intake + OpenAI skill standards
v1.0.2
Clarify web search citation guidelines to reduce hallucination risk
v1.0.1
Add input safety guidelines against prompt injection
v1.0.0
Initial release: 12 strategy prompts for full market research and analysis
元数据
Slug mckinsey-research
版本 2.1.0
许可证 MIT-0
累计安装 20
当前安装数 20
历史版本数 8
常见问题

McKinsey Research 是什么？

Run a full McKinsey-level market research and strategy analysis using 12 specialized prompts. USE WHEN: - market research, competitive analysis, business str... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 3556 次。

如何安装 McKinsey Research？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install mckinsey-research」即可一键安装，无需额外配置。

McKinsey Research 是免费的吗？

是的，McKinsey Research 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

McKinsey Research 支持哪些平台？

McKinsey Research 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 McKinsey Research？

由 Abdullah AlRashoudi（@abdullah4ai）开发并维护，当前版本 v2.1.0。

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