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OneWave-AI/claude-skills

TAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.

O que é claude-skills?

claude-skills is a Claude Code agent skill that tAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.

Funciona com✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/OneWave-AI/claude-skills/tree/HEAD/market-sizing

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Documentação

Market Sizing Agent

Produce rigorous, investor-grade TAM/SAM/SOM analyses by combining top-down macro data with bottom-up unit economics, triangulating the two, and always showing the work, citing sources, flagging assumptions, and providing sensitivity ranges.

Contents

  • references/research-sources.md — source categories and search queries for every research lane.
  • references/methodology.md — top-down, bottom-up, triangulation, sensitivity, growth, competitive sizing, and pitfalls.
  • references/output-template.md — the full market-sizing.md document template, Mermaid charts, and quality checklist.

Inputs

Confirm these four inputs before proceeding. If any is missing or ambiguous, ask first.

ParameterDescriptionExample
IndustryThe broad industry or sector"Enterprise SaaS", "Electric Vehicles"
Product/ServiceThe specific offering being sized"AI-powered code review tool"
GeographyTarget market geography"United States", "Global", "DACH region"
Target SegmentThe specific customer segment"Mid-market companies (100-1000 employees)"

Workflow

  1. Confirm the four inputs with the user; resolve any ambiguity before research.
  2. Research first. Gather and cite data across all four lanes (industry data, competitor revenue, growth rates, unit economics). See references/research-sources.md. Log every source URL and date as you go.
  3. Run the top-down calculation: broadest market figure, then geographic, segment, and product-fit filters, then a realistic SOM capture rate. See references/methodology.md.
  4. Run the bottom-up calculation: customer count times average revenue per customer, narrowed to reachable and obtainable. See references/methodology.md.
  5. Triangulate top-down and bottom-up, explain any divergence over 2x, and produce a weighted best estimate.
  6. Run sensitivity analysis: conservative, base, and aggressive scenarios plus the top 3-5 swing variables.
  7. Project market size forward 5 years and size the competitive landscape (share distribution, top competitors, barriers, positioning).
  8. Generate market-sizing.md using the structure in references/output-template.md. Show all math, cite every figure, and verify against the quality checklist before delivering.
  9. Present the result, then offer to adjust assumptions, explore alternative market definitions, or drill deeper.

Rules

  • Show all math; never present a number without showing how it was derived.
  • Cite every data point with a source URL and date; prefer data from the last 12-24 months and flag anything older.
  • Flag uncertainty explicitly when data is sparse or conflicting. Never fabricate precision.
  • Triangulate from at least 2-3 independent sources when possible.
  • Normalize all figures to a single currency and base year.
  • No emojis anywhere in the output. Avoid the pitfalls listed in references/methodology.md.

Individual skills in this repo

This repo contains 13 individual skills — each has its own dedicated page.

OneWave-AI/claude-skills

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

OneWave-AI/claude-skills

Generate animated videos and motion graphics from natural language descriptions. Creates a standalone Vite + React project with Framer Motion scenes that auto-play in the browser. Use when the user wants to create animations, motion graphics, video intros, animated presentations, or product demos.

OneWave-AI/claude-skills

Post-mortem analysis when a client churns. Takes client history, engagement data, support tickets, usage logs, and exit feedback to produce a comprehensive churn autopsy with root cause classification, timeline of decline, and preventive measures.

OneWave-AI/claude-skills

Assemble 2-3 complementary experts to collaboratively analyze anything. Experts work together to explore topics from multiple expert angles.

OneWave-AI/claude-skills

Convert any topic into playable browser games. Types: trivia, matching, word puzzles, adventure games. Uses Phaser.js or Kaboom.js.

OneWave-AI/claude-skills

Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.

OneWave-AI/claude-skills

Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.

OneWave-AI/claude-skills

Wire a System One model (Jev, or an open reproduction like Von) into a product feature — routing, guardrails, scoring, classification. Use when replacing an LLM call that returns a label rather than prose, when adding a typed decision to an agent loop, or when deciding between the hosted Jev API and a local open model. Covers question design, the eval-set-first workflow, threshold calibration, confidence gates, and the traps measured on real data.

OneWave-AI/claude-skills

Optimize landing pages for conversions, performance, and SEO. Use when improving landing pages, increasing conversions, or optimizing page performance.

OneWave-AI/claude-skills

Create multiple choice, true/false, fill-in-blank, matching quizzes. Auto-generate plausible distractors. Instant grading with explanations.

OneWave-AI/claude-skills

Enhanced skill navigator that maps conversation history, recommends multi-skill chains, identifies patterns from past usage, and learns from session outcomes. Goes beyond basic scout with deep context analysis and workflow orchestration.

OneWave-AI/claude-skills

Analyzes current conversation context to recommend the best skills and subagents for the task at hand. Use proactively when unsure which tool, skill, or agent to use.

OneWave-AI/claude-skills

Agent skill at social-repurposer/SKILL.md

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