Agent skill

Market Sizing

by LeoYeAI in LeoYeAI/openclaw-master-skills

Produce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business.

MITAuto-check passedProduct & Project Management

Install Market Sizing

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill market-sizing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills market-sizing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
market-sizing
GitHub stars
2.2k
Token cost
~4.7k tokens
SKILL.md length
2,233 words
Files
5
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Produce a rigorous, sourced TAM/SAM/SOM market sizing for any product or business.

  • Works in 6 steps: Data Gathering → Define the revenue unit → TAM (Total Addressable Market) → …
  • A user asks about market size
  • SKILL.md covers When to use this skill, Loading examples, Pre-flight: Classify the… and Step-by-step workflow…, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

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.

When your agent uses it

  • A user asks about market size
  • Total addressable market
  • Market opportunity — across any industry including SaaS
  • Consumer brands

Example prompts

  • “/market-sizing”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Data Gathering
  2. Define the revenue unit
  3. TAM (Total Addressable Market)
  4. SAM (Serviceable Addressable Market)
  5. SOM (Serviceable Obtainable Market)
  6. Reconcile and flag conflicts

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    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.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~4.7k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,233 words, ~4,664 tokens.

Download SKILL.mdSave it as .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.
name
market-sizing
description
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.
version
v2

TAM / SAM / SOM Market Sizing

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.


When to use this skill

Trigger on any prompt that contains:

  • "TAM", "SAM", "SOM", or "market size"
  • "How big is the market for X"
  • "Total addressable market", "serviceable market", "obtainable market"
  • "Market sizing for [product/company]"
  • Investor deck context requiring market opportunity quantification

Loading examples

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.

FileUse when product is...
examples/b2b-saas.mdSoftware / AI tool sold to businesses
examples/b2c-consumer-brand.mdConsumer packaged goods, DTC brands, subscriptions
examples/b2b-physical.mdPhysical goods or materials sold to businesses

If no exact match exists, load the closest file and adapt.


Pre-flight: Classify the product before doing anything else

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.

Axis 1 — Business model
CodeTypeRevenue unitPricing anchor
B2B-SaaSSoftware sold to businessesAnnual contract (ACV)10–20% of economic value created
B2B-PhysicalPhysical goods sold to businessesPer-unit price × annual volumeGross margin benchmarks by industry
B2C-BrandConsumer product (any category)Revenue per customer per yearAvg. purchase price × purchase frequency
B2C-SubscriptionConsumer subscriptionMonthly/annual subscription feeStated price or comp pricing
Marketplace/PlatformTakes % of GMVTake rate × GMVIndustry take rate benchmarks
Axis 2 — Market geography
  • US-only: Use US Census, NAICS codes, US industry associations
  • Global: Use global market reports, then apply regional share (US ~25–30% of global GDP; NA ~32%; APAC ~38%)
  • Single city/region: Use metro area population data + category penetration rates

Step-by-step workflow (universal)

Step 0 - Data Gathering

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.

STEP 1 — Define the revenue unit

Before any market data search, lock down exactly what the product sells, to whom, and at what price.

Required inputs:

  • Who is the buyer? (job title / consumer demographic)
  • What is the unit of sale? (per seat, per SKU, per kg, per subscription, per transaction)
  • What is the price? (stated, or estimated by comp analysis)
  • What is the purchase frequency? (one-time, monthly, annual, recurring)

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."

STEP 2 — TAM (Total Addressable Market)

TAM = the maximum theoretical revenue if every potential buyer purchased the product.

2A — Bottom-up method (PREFERRED for all product types)

For B2B products:

  1. Search for the number of firms in the target industry
    • US: Use NAICS code lookup — search "NAICS [code] number of firms US" or site:siccode.com NAICS [code]
    • Global: Use IBISWorld, Statista, or national business registries
  2. Estimate the % that are genuinely relevant (apply product-fit filter at this stage only if very obvious — e.g. residential-only firms for a commercial B2B product)
  3. TAM = Total relevant firms × ACV

For B2C products:

  1. Find the total consumer population in the target geography (US Census, APPA, Statista, trade associations)
  2. Find the % in the target demographic/behavioral segment
  3. TAM = Segment population × annual spend per person

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)
2B — Top-down method (SECONDARY cross-check)
  1. Find total category market size from analyst reports (Grand View, IMARC, Mordor, Precedence, IBISWorld)
  2. Apply funnel: Total market → relevant segment → product-type share → geography
  3. Search: "[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).

TAM output format:
  • Single dollar figure (round to nearest $100M for large markets, $10M for mid-size)
  • Conservative range (low–high)
  • The math shown explicitly: N firms × $X ACV = $Y or N consumers × $Z annual spend = $Y
STEP 3 — SAM (Serviceable Addressable Market)

SAM = 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:

FilterTypical reductionHow to estimate
GeographyVariesIf US-only product, filter out non-US. If city-level, apply metro population fraction.
Product-fit segment30–60% reductionRemove segments the product doesn't serve (e.g. residential-only for commercial SaaS; solo operators who can't justify the cost)
Tech/channel readiness10–30% reduction% of target customers with internet access, cloud tools, or relevant distribution channel access
Language/regulatoryVariesOnly relevant for global products
Willingness to pay tier20–40% reductionRemove 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"
SAM output format:
  • Single dollar figure with conservative range
  • Every filter listed with its % reduction and data source
  • Blended ACV (if applicable)
STEP 4 — SOM (Serviceable Obtainable Market)

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 typeYear 1Year 3Source basis
New B2B SaaS (no category)0.1–0.3% of SAM1.5–3% of SAMAndreessen 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 SAM0.5–2%Nielsen new brand launch data
New B2C DTC subscription0.1–0.5%1–4%DTC cohort benchmarks
New B2B physical product0.2–0.5%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:

  • SOM (Year 1) = SAM × Year 1 penetration rate
  • SOM (Year 3) = SAM × Year 3 penetration rate
  • Customer count = SOM ÷ Blended ACV (sanity check — does this number of customers feel achievable given go-to-market?)
SOM output format:
  • Year 1 and Year 3 ARR/revenue targets
  • Customer count at each stage
  • Named comparable company validations
  • Penetration rate used (with justification)
STEP 5 — Reconcile and flag conflicts

After running both methods:

  1. If bottom-up TAM > top-down TAM: Usually because the product creates new demand (not just shifting existing spend). State this explicitly.
  2. If bottom-up TAM < top-down TAM: Often because the product is a niche within a large category. State which definition is being used.
  3. Always present a range, not a single number, for TAM and SAM. Single numbers imply false precision.
  4. Flag the 3 most sensitive assumptions — the ones where a change of ±20% would materially shift the output. These are the assumptions to validate with primary research.

Source hierarchy (use in this order, stop when satisfied)

Tier 1 — Primary/official sources (highest credibility)
  • US Census Bureau / Economic Census — firm counts, employment, revenue by NAICS code
  • NAICS/SIC code databases (siccode.com, census.gov) — industry firm counts
  • Industry trade associations (APPA for pets, CFMA for construction, NMMA for marine, etc.)
  • SEC filings / public company 10-Ks — addressable market disclosures
  • Government labour/business statistics (BLS, SBA, ONS)
Tier 2 — Reputable market research
  • IBISWorld, Grand View Research, Mordor Intelligence, IMARC Group, Precedence Research, Statista
  • Use these for: category market size, CAGR, segment shares
  • Cross-reference at least 2 sources — market research firms often disagree by 20–40%
Show full SKILL.md (902 more words)Show less
Tier 3 — Comparable company evidence
  • Funding announcements (TechCrunch, Crunchbase, Construction Dive, etc.)
  • Public company revenue disclosures at IPO/Series B
  • Use for: SOM cross-validation, ACV benchmarks, growth rate validation
Tier 4 — Pricing/ACV benchmarks
  • Competitor pricing pages (direct)
  • G2, Capterra, Trustpilot reviews mentioning price
  • Stripe, Monetizely, OpenView pricing guides (for B2B SaaS)

ACV / pricing benchmarks by product type

Product typeTypical ACV/ASPNotes
SMB B2B SaaS (1–50 employees)$3,000–$15,000/yr10–20% 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/yrDepends heavily on industry
B2C DTC brand (avg. LTV proxy)2–3× first-order value

Common errors — check before finalising

ErrorHow to catch it
"1% of a $10B market" without proofAlways verify with bottom-up customer count
Top-down only, no bottom-upAlways run both methods
Using global TAM when product is US-onlyApply 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 benchmarkedAlways cite at least one comp company
ACV not grounded in value or comp pricingAlways show the value math or comp reference
Treating TAM as "market size from report" uncriticallyCheck the report's definition matches your product's scope
Single number without rangeAlways show low–high range

Industry-specific data source cheat sheet

IndustryBest firm-count sourceBest market-size sourceKey trade association
Construction / TradesNAICS via siccode.com or CensusIBISWorld, CFMA BenchmarkerCFMA, AGC, NECA
Pet / Consumer packaged goodsCensus NAICS 311APPA, Grand View, IMARCAPPA, PFI
Food & BeverageCensus NAICS 311/312Mordor, Grand View, MintelFMI, GMA
Beauty / Personal careCensus NAICS 325620Euromonitor, Grand ViewPBA, CEW
Fashion / ApparelCensus NAICS 315McKinsey Fashion Report, StatistaAAFA
SaaS / SoftwareCrunchbase, G2 categoriesGartner, IDC, ForresterVaries
AutomotiveNAICS 441/336Ward's Auto, IHS MarkitNADA, SEMA
B2B PackagingCensus NAICS 322/326Grand View, MordorPMMI, FPA

Output format

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:

  • Both methods in full, each covering TAM → SAM → SOM completely before moving to the next method. Each method include 3 sections (TAM, SAM, SOM), each with: headline figure + range, full calculation shown line by line, and key assumptions&sources listed
  • A standalone section for all used figures' sources & assumptions.
  • A summary: table with TAM / SAM / SOM figures (bottom-up and top-down side by side); a method comparison note explaining divergence if bottom-up and top-down differ by >2×

The following is an example markdown output with a structure you should follow:

"""

Market Sizing: BuildFlow AI

AI-powered invoicing automation for HVAC and Electrical subcontractors — United States


Method 1: Bottom-up

TAM
InputValueSource
HVAC firms (NAICS 238220)88,738siccode.com / US Census
Electrical firms (NAICS 238210)55,951siccode.com / US Census
% doing GC-facing commercial work55%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/yrValue-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)

SAM
FilterReductionRemainingSource
Remove residential-only operators−40%47,748 firmsSBA / IBISWorld industry mix
Remove solo / owner-only operators−35%31,037 firmsCensus nonemployer statistics
Remove cloud / tech-unready firms−25%23,278 firmsConstruction 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)

SOM
Year% of SAMARRCustomers
Year 10.27%~$1.5M~140
Year 20.85%~$4.8M~450
Year 31.5–3.0%$8.4M–$16.8M790–1,580

Benchmark: 1.5–3% of SAM by Year 3 — a.H./OpenView SaaS benchmarks for new B2B entrant in established category

CompSignal
PayraRaised $15M (Edison Partners); AP automation for construction subcontractors
SitelineRaised $18M Series A; subcontractor billing / SOV management, comparable ICP
Adaptive Construction SolutionsBootstrapped 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


Method 2: Top-down

TAM
Global construction accounting software (2025):  $2.64B  [Precedence Research]
× US share (76%):                                 $2.0B
× AP/AR segment (54.4%):                          $1.09B

→ TAM: $1.09B

SAM
Top-down TAM:                        $1.09B
× Specialty trade sub-share (~28%):  $305M
× HVAC + Electrical only (~50%):     $152M

→ SAM: $152M

SOM
SAM (top-down):              $152M
× 3% Year 3 penetration:     $4.6M

→ SOM: $0.4M ARR (Year 1) / $4.6M ARR (Year 3)


Sources

InputValue assumedSource
HVAC firms (NAICS 238220)88,738siccode.com / US Census
Electrical firms (NAICS 238210)55,951siccode.com / US Census
% GC-facing commercial work55%SBA subcontractor mix data
Avg. HVAC subcontractor revenue$1.5M/yrCFMA 2024 Benchmarker
Avg. Electrical subcontractor revenue$2.0M/yrCFMA 2024 Benchmarker
Subcontractor net profit margin2.2–3.5%CFMA 2024 Benchmarker
Profit lost to unbilled/errors~5% of revenueIndustry estimate
Global construction accounting software market$2.64B (2025)Precedence Research
AP/AR segment share54.4%Precedence Research
Cloud/tech readiness of small construction firms~75%Construction tech adoption surveys 2024
SaaS penetration benchmark (Year 3)1.5–3% of SAMa.H./OpenView SaaS benchmarks
Comp: Payra raise$15MEdison Partners announcement
Comp: Siteline raise$18M Series APublic announcement

Summary

Bottom-upTop-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. """

Quality checklist before delivering output

  • Both bottom-up AND top-down methods calculated
  • Every input number has a named source
  • TAM and SAM shown as ranges, not single numbers
  • Blended ACV used where multiple customer segments exist
  • SOM penetration rate benchmarked against 1–2 comp companies
  • At least 3 sensitivity risks flagged
  • Part 1 markdown follows the Output Example structure exactly
  • Prose narration follows all 6 workflow steps
  • Top-down vs bottom-up divergence explained if >2×

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in skills/tam-sam-som of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • examples/b2b-physical.md
  • examples/b2b-saas.md
  • examples/b2c-consumer-brand.md

Open the folder on GitHubat commit e5199b5

Compare with similar skills

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.

Market Sizing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Market Sizing this skillLeoYeAI/openclaw-master-skills2.2k—~4.7kAutomated safety check: PassMIT
TAM SAM SOM Calculatordeanpeters/Product-Manager-Skills7.2k1 repos~4.8kAutomated safety check: PassCustom licence
Market Sizing Analysisnicepkg/auto-company19511 repos~3.1kAutomated safety check: PassNone
Management ConsultantDogInfantry/claude-skill-management-consultant-B1136—~14kAutomated safety check: PassCustom licence
Competitor MapperMaxKmet/idea-validation-agents478—~3.3kAutomated safety check: PassMIT
Market Sizing Frameworksslgoodrich/agents139—~3.3kAutomated safety check: PassCustom licence

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Questions about Market Sizing

What does Market Sizing do?

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.

When should I use Market Sizing?

Market Sizing fits situations like: A user asks about market size; total addressable market; market opportunity — across any industry including SaaS; consumer brands.

How do I install Market Sizing in Claude Code?

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.

How do I install Market Sizing in Codex?

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.

Can I use Market Sizing in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Market Sizing need to run?

SKILL.md names no scripts, command-line tools or credentials: Market Sizing is instructions for the agent only.

Does Market Sizing access the network?

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.

Is Market Sizing safe to install?

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.

What licence does Market Sizing use?

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.

How many tokens does Market Sizing use?

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.

What are the alternatives to Market Sizing?

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.

Who maintains Market Sizing?

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.