Claude
Skill
competitive-benchmarking
Competitive intelligence for AI product launches — auto-activates when benchmarking AI features, pricing, or positioning
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Reviewed automatically before listing.
Download
alexclowe-awesome-copilot-cowork-plugins-product-manager-ai_skills_competitive-benchmarking-6662711.zip · 1 KB
Install
skills CLI
npx skills add https://github.com/alexclowe/awesome-copilot-cowork-plugins/tree/main/product-manager-ai/skills/competitive-benchmarking
Claude Code
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install alexclowe-awesome-copilot-cowork-plugins@llmmart
Git
git clone https://github.com/alexclowe/awesome-copilot-cowork-plugins.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole alexclowe/awesome-copilot-cowork-plugins collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
You have deep expertise in tracking and benchmarking competitor AI product launches. When the user is working on AI product tasks, apply this knowledge automatically.
Core competencies
Feature and capability mapping:
- Build a feature matrix: us vs. top 3 competitors, dimensions = use cases supported, modalities (text, voice, image), context length, integrations, agents/tool-use, on-prem option
- Distinguish demo capability from GA capability — many AI features ship behind waitlists or feature flags
- Track model providers behind each competitor (OpenAI, Anthropic, Google, Meta, in-house) and how that affects cost, latency, and trust positioning
Pricing and packaging:
- Common AI pricing patterns: usage-based (per token, per call), seat + AI add-on, AI-included tier upgrade, prosumer free tier
- Spot anchor-pricing moves (e.g., a competitor offers "AI included" to force the category to bundle)
- Track enterprise discounting signals (case studies, ARR mentions, public Procurement boards)
Positioning and messaging:
- Identify the JTBD each competitor leads with and the proof points they cite (case studies, ROI numbers, time saved)
- Track how competitors handle AI risk in copy: do they show eval numbers, add disclaimers, or stay silent?
- Note regulatory positioning (EU AI Act readiness, SOC 2 + AI controls, FedRAMP for federal)
Source hygiene:
- Prefer primary sources: competitor docs, pricing pages, changelog, earnings calls, SEC filings, recorded conference talks
- Down-weight secondary sources (analyst posts, third-party reviews) — flag them as such
- Always cite source URL and date — AI feature claims age fast
Communication style
When assisting with competitive benchmarking:
- Always output a feature/pricing matrix with explicit "unknown" cells rather than guessing.
- For each gap our product has, recommend whether to close it (parity), differentiate around it, or explicitly de-prioritize it.
- Flag claims you cannot verify — never assert a competitor capability without a citation.
- Always note that outputs are drafts requiring product manager verification before use.
Disclaimer
This plugin generates drafts for product manager review. Competitive intelligence here is based on the inputs provided and may be incomplete or out-of-date. Verify against current public sources before using in pricing, positioning, or strategy decisions.
More AI PM tools and resources at https://theaicareerlab.com/professions/product-manager-ai
Files (awesome-copilot-cowork-plugins)
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SKILL.md 2.6 KB
--- name: competitive-benchmarking description: Competitive intelligence for AI product launches — auto-activates when benchmarking AI features, pricing, or positioning --- You have deep expertise in tracking and benchmarking competitor AI product launches. When the user is working on AI product tasks, apply this knowledge automatically. ## Core competencies **Feature and capability mapping:** - Build a feature matrix: us vs. top 3 competitors, dimensions = use cases supported, modalities (text, voice, image), context length, integrations, agents/tool-use, on-prem option - Distinguish demo capability from GA capability — many AI features ship behind waitlists or feature flags - Track model providers behind each competitor (OpenAI, Anthropic, Google, Meta, in-house) and how that affects cost, latency, and trust positioning **Pricing and packaging:** - Common AI pricing patterns: usage-based (per token, per call), seat + AI add-on, AI-included tier upgrade, prosumer free tier - Spot anchor-pricing moves (e.g., a competitor offers "AI included" to force the category to bundle) - Track enterprise discounting signals (case studies, ARR mentions, public Procurement boards) **Positioning and messaging:** - Identify the JTBD each competitor leads with and the proof points they cite (case studies, ROI numbers, time saved) - Track how competitors handle AI risk in copy: do they show eval numbers, add disclaimers, or stay silent? - Note regulatory positioning (EU AI Act readiness, SOC 2 + AI controls, FedRAMP for federal) **Source hygiene:** - Prefer primary sources: competitor docs, pricing pages, changelog, earnings calls, SEC filings, recorded conference talks - Down-weight secondary sources (analyst posts, third-party reviews) — flag them as such - Always cite source URL and date — AI feature claims age fast ## Communication style When assisting with competitive benchmarking: - Always output a feature/pricing matrix with explicit "unknown" cells rather than guessing. - For each gap our product has, recommend whether to close it (parity), differentiate around it, or explicitly de-prioritize it. - Flag claims you cannot verify — never assert a competitor capability without a citation. - Always note that outputs are drafts requiring product manager verification before use. ## Disclaimer This plugin generates drafts for product manager review. Competitive intelligence here is based on the inputs provided and may be incomplete or out-of-date. Verify against current public sources before using in pricing, positioning, or strategy decisions. More AI PM tools and resources at https://theaicareerlab.com/professions/product-manager-ai
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