TAM SAM SOM Calculator
deanpeters/Product-Manager-Skills
Calculates total, serviceable available and serviceable obtainable market size for a product idea with explicit assumptions, methods and caveats.
Produce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business.
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills market-sizing --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tam-sam-som .claude/skills/market-sizing && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "market-sizing" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-som into .claude/skills/market-sizing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "market-sizing", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-somType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills market-sizing --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tam-sam-som .agents/skills/market-sizing && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "market-sizing" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-som into .agents/skills/market-sizing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "market-sizing", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills market-sizing --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tam-sam-som .cursor/skills/market-sizing && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "market-sizing" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-som into .cursor/skills/market-sizing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "market-sizing", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/tam-sam-som--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills market-sizing --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tam-sam-som .gemini/skills/market-sizing && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "market-sizing" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-som into .gemini/skills/market-sizing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "market-sizing", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills market-sizingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tam-sam-som .github/skills/market-sizing && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "market-sizing" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-som into .github/skills/market-sizing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "market-sizing", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills market-sizing --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tam-sam-som .opencode/skills/market-sizing && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "market-sizing" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/tam-sam-som into .opencode/skills/market-sizing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "market-sizing", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
market-sizingProduce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business.
Market Sizing is an agent skill from LeoYeAI/openclaw-master-skills. Produce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business. Use this skill whenever a user asks about market size, total addressable market, SAM, SOM, or market opportunity — across any industry including SaaS, AI tools, consumer brands, F&B, fashion, beauty, packaging, automotive, and more.
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `_meta.json`, `examples/b2b-physical.md` and `examples/b2b-saas.md`).
It sits in Product & Project Management, covering Market sizing. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Market Sizing loads about 4.7k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 2,233 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,233 words, ~4,664 tokens.
.claude/skills/market-sizing/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.Produce both a bottom-up and a top-down analysis for every engagement. Both methods are required — they serve different purposes and must be reconciled. The goal is a defensible, sourced output — not a fast estimate.
Trigger on any prompt that contains:
Before calculating, check the examples/ directory for a file matching the product's category. Load the closest match to calibrate data sources, filter logic, and ACV anchors before you begin.
| File | Use when product is... |
|---|---|
examples/b2b-saas.md | Software / AI tool sold to businesses |
examples/b2c-consumer-brand.md | Consumer packaged goods, DTC brands, subscriptions |
examples/b2b-physical.md | Physical goods or materials sold to businesses |
If no exact match exists, load the closest file and adapt.
Before searching or calculating, classify the product along these two axes. The classification directly determines which data sources to use and which pricing method applies.
| Code | Type | Revenue unit | Pricing anchor |
|---|---|---|---|
| B2B-SaaS | Software sold to businesses | Annual contract (ACV) | 10–20% of economic value created |
| B2B-Physical | Physical goods sold to businesses | Per-unit price × annual volume | Gross margin benchmarks by industry |
| B2C-Brand | Consumer product (any category) | Revenue per customer per year | Avg. purchase price × purchase frequency |
| B2C-Subscription | Consumer subscription | Monthly/annual subscription fee | Stated price or comp pricing |
| Marketplace/Platform | Takes % of GMV | Take rate × GMV | Industry take rate benchmarks |
Since accuracy and reliability are key of this task, make sure you collect enough reliable data from reliable sources before the calculation. Limited estimation is fine with enough evidence support, but any kind of data making up or hallucination without evidence during the calculation and output phase is a fraud.
Before any market data search, lock down exactly what the product sells, to whom, and at what price.
Required inputs:
If price is unknown: Search "[product category] average price" OR "[closest competitor] pricing" and anchor to the median. For B2B-SaaS, also apply the 10–20% value rule: price = 10–20% of annual economic value the product creates for the customer.
Output of Step 1: A single sentence — "The revenue unit is [X] sold to [Y] at [Z]/year."
TAM = the maximum theoretical revenue if every potential buyer purchased the product.
For B2B products:
"NAICS [code] number of firms US" or site:siccode.com NAICS [code]For B2C products:
Search queries to use:
"[industry] number of [businesses/firms/brands] US 2024""NAICS [code] firm count employees revenue""[consumer category] number of [households/users/owners] US""[category] market size 2024" (for cross-reference only — not the primary method)"[category] software/product market size 2024" or "[category] industry revenue US 2024"Rule: Bottom-up is the primary method. Top-down is the cross-check. Both results should be reported. If they diverge significantly (>2×), explain why (e.g. top-down only counts current spenders; bottom-up counts total potential).
N firms × $X ACV = $Y or N consumers × $Z annual spend = $YSAM = the subset of TAM that the specific product can actually serve today, given its business model, geography, language, and product fit.
Universal filter checklist — apply all that are relevant:
| Filter | Typical reduction | How to estimate |
|---|---|---|
| Geography | Varies | If US-only product, filter out non-US. If city-level, apply metro population fraction. |
| Product-fit segment | 30–60% reduction | Remove segments the product doesn't serve (e.g. residential-only for commercial SaaS; solo operators who can't justify the cost) |
| Tech/channel readiness | 10–30% reduction | % of target customers with internet access, cloud tools, or relevant distribution channel access |
| Language/regulatory | Varies | Only relevant for global products |
| Willingness to pay tier | 20–40% reduction | Remove segments priced out of the product's tier |
Formula: SAM = TAM × Filter₁ × Filter₂ × ... × Filterₙ
Blended ACV for SAM: If there are multiple customer segments (SME vs. mid-market, or mass vs. premium), calculate a weighted average ACV:
Blended ACV = (Segment_A_ACV × weight_A) + (Segment_B_ACV × weight_B)
Search queries for filter data:
"[industry] percentage [commercial/residential/enterprise/SMB]""[industry] small business cloud software adoption rate""[consumer category] premium vs mass market share"SOM = the portion of SAM the product can realistically capture in a defined timeframe (typically 3 years / Year 1–3 ramp).
Penetration rate benchmarks by product type:
| Product type | Year 1 | Year 3 | Source basis |
|---|---|---|---|
| New B2B SaaS (no category) | 0.1–0.3% of SAM | 1.5–3% of SAM | Andreessen Horowitz, OpenView benchmarks |
| New B2B SaaS (established category) | 0.3–0.8% | 3–7% | Category incumbents' early growth data |
| New B2C consumer brand (retail) | 0.05–0.2% of SAM | 0.5–2% | Nielsen new brand launch data |
| New B2C DTC subscription | 0.1–0.5% | 1–4% | DTC cohort benchmarks |
| New B2B physical product | 0.2–0.5% | 2–5% | Trade distribution ramp benchmarks |
Cross-validation method: Find 2–3 comparable companies (same category, similar stage) and anchor SOM to their reported early ARR/revenue. Search:
"[similar company] [Series A / early revenue / ARR] raised [year]"
Formula:
After running both methods:
| Product type | Typical ACV/ASP | Notes |
|---|---|---|
| SMB B2B SaaS (1–50 employees) | $3,000–$15,000/yr | 10–20% of value created |
| Mid-market B2B SaaS (50–500 employees) | $15,000–$60,000/yr | |
| Enterprise B2B SaaS (500+ employees) | $60,000–$500,000+/yr | |
| Consumer subscription (premium) | $100–$600/yr | |
| Consumer packaged goods (premium) | $30–$150/order, 6–12×/yr | |
| B2B physical product (SMB) | $5,000–$50,000/yr | Depends heavily on industry |
| B2C DTC brand (avg. LTV proxy) | 2–3× first-order value |
| Error | How to catch it |
|---|---|
| "1% of a $10B market" without proof | Always verify with bottom-up customer count |
| Top-down only, no bottom-up | Always run both methods |
| Using global TAM when product is US-only | Apply geography filter explicitly |
| Including non-buyers in TAM (e.g. residential firms in a commercial B2B) | Apply product-fit filter before TAM finalisation |
| SOM penetration rate not benchmarked | Always cite at least one comp company |
| ACV not grounded in value or comp pricing | Always show the value math or comp reference |
| Treating TAM as "market size from report" uncritically | Check the report's definition matches your product's scope |
| Single number without range | Always show low–high range |
| Industry | Best firm-count source | Best market-size source | Key trade association |
|---|---|---|---|
| Construction / Trades | NAICS via siccode.com or Census | IBISWorld, CFMA Benchmarker | CFMA, AGC, NECA |
| Pet / Consumer packaged goods | Census NAICS 311 | APPA, Grand View, IMARC | APPA, PFI |
| Food & Beverage | Census NAICS 311/312 | Mordor, Grand View, Mintel | FMI, GMA |
| Beauty / Personal care | Census NAICS 325620 | Euromonitor, Grand View | PBA, CEW |
| Fashion / Apparel | Census NAICS 315 | McKinsey Fashion Report, Statista | AAFA |
| SaaS / Software | Crunchbase, G2 categories | Gartner, IDC, Forrester | Varies |
| Automotive | NAICS 441/336 | Ward's Auto, IHS Markit | NADA, SEMA |
| B2B Packaging | Census NAICS 322/326 | Grand View, Mordor | PMMI, FPA |
The output file is where you calculations are made. After collecting necessary data, make a step-by-step analysis & calculation on the file, finally yielding a summary and a comparison. Produce a structured markdown document containing:
The following is an example markdown output with a structure you should follow:
"""
AI-powered invoicing automation for HVAC and Electrical subcontractors — United States
| Input | Value | Source |
|---|---|---|
| HVAC firms (NAICS 238220) | 88,738 | siccode.com / US Census |
| Electrical firms (NAICS 238210) | 55,951 | siccode.com / US Census |
| % doing GC-facing commercial work | 55% | SBA subcontractor mix data |
| ACV — HVAC SME tier | $7,500/yr | $1.5M avg. revenue × 0.5%; CFMA 2024 |
| ACV — Electrical SME tier | $10,000/yr | $2.0M avg. revenue × 0.5%; CFMA 2024 |
| ACV — mid-market tier (>$5M rev) | $24,000/yr | Value-based; comp: Siteline pricing |
HVAC SME (48,806 × 82%): 40,021 × $7,500 = $300M
HVAC mid-market (48,806 × 18%): 8,785 × $24,000 = $211M
Electrical SME (30,773 × 82%): 25,234 × $10,000 = $252M
Electrical mid-market (30,773×18%): 5,539 × $24,000 = $133M
Value-based ceiling (5% leakage × 10–20% SaaS capture): ~$1.87B→ TAM: $1.87B (range: $1.5B–$2.2B)
| Filter | Reduction | Remaining | Source |
|---|---|---|---|
| Remove residential-only operators | −40% | 47,748 firms | SBA / IBISWorld industry mix |
| Remove solo / owner-only operators | −35% | 31,037 firms | Census nonemployer statistics |
| Remove cloud / tech-unready firms | −25% | 23,278 firms | Construction tech adoption surveys 2024 |
Blended ACV:
SME tier (70%): $7,500 × 0.70 = $5,250
Mid-market (30%): $18,000 × 0.30 = $5,400
Blended: = $10,650/yr
23,278 firms × $10,650 + mid-market uplift ≈ $560M→ SAM: $560M (range: $430M–$690M)
| Year | % of SAM | ARR | Customers |
|---|---|---|---|
| Year 1 | 0.27% | ~$1.5M | ~140 |
| Year 2 | 0.85% | ~$4.8M | ~450 |
| Year 3 | 1.5–3.0% | $8.4M–$16.8M | 790–1,580 |
Benchmark: 1.5–3% of SAM by Year 3 — a.H./OpenView SaaS benchmarks for new B2B entrant in established category
| Comp | Signal |
|---|---|
| Payra | Raised $15M (Edison Partners); AP automation for construction subcontractors |
| Siteline | Raised $18M Series A; subcontractor billing / SOV management, comparable ICP |
| Adaptive Construction Solutions | Bootstrapped to ~$5M ARR in 3 years; construction AR automation |
→ SOM: $1.5M ARR (Year 1) / $8.4M–$16.8M ARR (Year 3), ~790–1,580 customers
Global construction accounting software (2025): $2.64B [Precedence Research]
× US share (76%): $2.0B
× AP/AR segment (54.4%): $1.09B→ TAM: $1.09B
Top-down TAM: $1.09B
× Specialty trade sub-share (~28%): $305M
× HVAC + Electrical only (~50%): $152M→ SAM: $152M
SAM (top-down): $152M
× 3% Year 3 penetration: $4.6M→ SOM: $0.4M ARR (Year 1) / $4.6M ARR (Year 3)
| Input | Value assumed | Source |
|---|---|---|
| HVAC firms (NAICS 238220) | 88,738 | siccode.com / US Census |
| Electrical firms (NAICS 238210) | 55,951 | siccode.com / US Census |
| % GC-facing commercial work | 55% | SBA subcontractor mix data |
| Avg. HVAC subcontractor revenue | $1.5M/yr | CFMA 2024 Benchmarker |
| Avg. Electrical subcontractor revenue | $2.0M/yr | CFMA 2024 Benchmarker |
| Subcontractor net profit margin | 2.2–3.5% | CFMA 2024 Benchmarker |
| Profit lost to unbilled/errors | ~5% of revenue | Industry estimate |
| Global construction accounting software market | $2.64B (2025) | Precedence Research |
| AP/AR segment share | 54.4% | Precedence Research |
| Cloud/tech readiness of small construction firms | ~75% | Construction tech adoption surveys 2024 |
| SaaS penetration benchmark (Year 3) | 1.5–3% of SAM | a.H./OpenView SaaS benchmarks |
| Comp: Payra raise | $15M | Edison Partners announcement |
| Comp: Siteline raise | $18M Series A | Public announcement |
| Bottom-up | Top-down | |
|---|---|---|
| TAM | $1.87B | $1.09B |
| SAM | $560M | $152M |
| SOM — Year 1 / Year 3 | $1.5M / $8.4M–$16.8M ARR | $0.4M / $4.6M ARR |
Bottom-up preferred — top-down counts only existing software spend and misses the large paper/spreadsheet segment. Use top-down as the floor / downside scenario. """
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files in skills/tam-sam-som of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Market Sizing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Market Sizing this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.7k | Automated safety check: Pass | MIT | |
| TAM SAM SOM Calculatordeanpeters/Product-Manager-Skills | 7.2k | 1 repos | ~4.8k | Automated safety check: Pass | Custom licence | |
| Market Sizing Analysisnicepkg/auto-company | 195 | 11 repos | ~3.1k | Automated safety check: Pass | None | |
| Management ConsultantDogInfantry/claude-skill-management-consultant-B1 | 136 | — | ~14k | Automated safety check: Pass | Custom licence | |
| Competitor MapperMaxKmet/idea-validation-agents | 478 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Market Sizing Frameworksslgoodrich/agents | 139 | — | ~3.3k | Automated safety check: Pass | Custom licence |
deanpeters/Product-Manager-Skills
Calculates total, serviceable available and serviceable obtainable market size for a product idea with explicit assumptions, methods and caveats.
nicepkg/auto-company
This skill should be used when the user asks to "calculate TAM", "determine SAM", "estimate SOM", "size the market", "calculate market opportunity", "what's the total addressable market", or…
DogInfantry/claude-skill-management-consultant-B1
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MaxKmet/idea-validation-agents
Maps the full competitive landscape — direct, indirect, substitute, and emerging competitors — with positioning gap analysis, review mining, and marketinsights-calibrated saturation scoring.
slgoodrich/agents
TAM/SAM/SOM calculations and market sizing methodologies. An agent skill from slgoodrich/agents.
AlphaMao1/AlphaMao_Skills
市场规模测算工具 (TAM/SAM/SOM)。适用于用户提到「市场规模」「market size」「TAM」「SAM」「SOM」或需要估算目标市场大小时使用。
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Categories
Produce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business. Market Sizing is an agent skill from LeoYeAI/openclaw-master-skills. Produce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business.
Market Sizing fits situations like: A user asks about market size; total addressable market; market opportunity — across any industry including SaaS; consumer brands.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a claude-code`. Or copy the skill folder (skills/tam-sam-som in LeoYeAI/openclaw-master-skills) into .claude/skills/market-sizing in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a codex`. Or copy the skill folder (skills/tam-sam-som in LeoYeAI/openclaw-master-skills) into .agents/skills/market-sizing in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/market-sizing, .gemini/skills/market-sizing, .github/skills/market-sizing and .opencode/skills/market-sizing in your project.
SKILL.md names no scripts, command-line tools or credentials: Market Sizing is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Market Sizing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Market Sizing: TAM SAM SOM Calculator (deanpeters/Product-Manager-Skills, 7.2k stars), Market Sizing Analysis (nicepkg/auto-company, 195 stars), Management Consultant (DogInfantry/claude-skill-management-consultant-B1, 136 stars) and Competitor Mapper (MaxKmet/idea-validation-agents, 478 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.