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Agent skill

Tam Sam Som Builder

by MaxKmet in MaxKmet/idea-validation-agents

Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to marketinsights trend data, competitor revenue proxies, and community size signals.

MITAuto-check passedProduct & Project Management

Install Tam Sam Som Builder

skills CLI
$ npx skills add MaxKmet/idea-validation-agents --skill tam-sam-som-builder -a claude-code

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

GitHub CLI
$ gh skill install MaxKmet/idea-validation-agents tam-sam-som-builder --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/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tam-sam-som-builder .claude/skills/tam-sam-som-builder && 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
tam-sam-som-builder
GitHub stars
478
Token cost
~3.3k tokens
SKILL.md length
1,461 words
Files
1
Skills in repo
15
Repo updated
First seen
Licence
MIT

At a glance

Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to marketinsights trend data, competitor revenue proxies, and community size signals.

  • Works in 12 steps: Load all available inputs:… → Extract calibration data from… → Run Approach A (search volume) if… → …
  • Tasks that involve Market sizing
  • SKILL.md covers Purpose, Input, Methodology and SAM Filtering, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tam Sam Som Builder is an agent skill from MaxKmet/idea-validation-agents. Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to marketinsights trend data, competitor revenue proxies, and community size signals. Includes indie capture rate benchmarks and growth-rate adjustments by trend velocity.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management, covering Market sizing. It works with Reddit and TikTok. The repository describes itself as: AI agents that act as your personal venture analyst - from startup idea brainstorming to full validation and go-to-market strategy. Built for developers who'd rather validate in… The licence is MIT.

When your agent uses it

  • Tasks that involve Market sizing

Example prompts

  • “Use the tam-sam-som-builder skill to estimate TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to…”
  • “/tam-sam-som-builder”

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. Load all available inputs: keywords.json, competitors.json, pricing.json, and all matching memory/market_insights/-*--.md files.
  2. Extract calibration data from market_insights (trend velocity, top signals, monetization evidence, overall verdict).
  3. Run Approach A (search volume) if keywords.json is available.
  4. Run Approach B (community size proxy) if market_insights contain community signals.
  5. Run Approach C (competitor revenue proxy) if competitors.json has user/pricing data.
  6. Triangulate: compare estimates, determine confidence, select final TAM.
  7. Apply SAM filters (geography, platform, demographics, niche).
  8. Estimate SOM using category-appropriate capture rate benchmark.
  9. Apply growth multiplier from trend velocity.
  10. Run reality checks. Adjust if any are triggered.
  11. Determine market size verdict from SOM year 1 thresholds.
  12. Write output.

What it can do on your machine

Read from SKILL.md and the folder at commit 3a4c800. 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 (its code samples are json).

    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

Tam Sam Som Builder loads about 3.3k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 1,461 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

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 MaxKmet/idea-validation-agents at commit 3a4c800, republished under its MIT licence (© MaxKmet). 1,461 words, ~3,330 tokens.

Download SKILL.mdSave it as .claude/skills/tam-sam-som-builder/SKILL.md (or your agent's skills folder).
name
tam-sam-som-builder
description
Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to market_insights trend data, competitor revenue proxies, and community size signals. Includes indie capture rate benchmarks and growth-rate adjustments by trend velocity.
<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/market_size.json -->

Skill: tam-sam-som-builder

Purpose

Provide a realistic market size estimate that an indie developer can actually use for decisions. Most TAM estimates are useless because they use top-down analyst numbers designed to impress VCs, not to inform a solo builder deciding where to spend the next 6 months. This skill uses triangulated bottom-up methodology — multiple independent estimation approaches cross-checked against each other — anchored to real signals from market_insights/ trend data.

Input

  • Idea slug (or market research slug for pre-idea deep dives)
  • memory/ideas/<slug>/keywords.json (search volume estimates — if available)
  • memory/ideas/<slug>/competitors.json (competitor scale and pricing signals)
  • memory/ideas/<slug>/pricing.json (target price — if available)
  • memory/market_insights/<niche>-*-<YYYY>-<MM>.md (trend analysis files — use all available platform files for this niche)
Using Market Insights

Trend analysis files from memory/market_insights/ provide critical calibration data. Extract the following from each available file's YAML frontmatter and narrative:

FieldHow it informs TAM/SAM/SOM
trend_velocityAdjusts growth rate projections (see Step 5)
top_signalsValidates that real demand exists; calibrates bottom-up search volume estimates
monetization_evidenceConfirms WTP — if no monetization evidence exists across any platform, discount TAM by 30–50%
overall_verdict (hot/warm/cool/cold)Sanity-check on whether the market is worth sizing at all
Platform narrative (Reddit pain points, TikTok engagement, App Store reviews)Source for community size proxy estimation (see Approach B)

If overall_verdict across platforms is "cold", flag the entire estimate as speculative and note that market demand is unvalidated.

Methodology

Approach A — Search Volume (primary when keywords.json available)
TAM = monthly_search_volume × 12 × intent_conversion_rate × annual_price

Where:

  • monthly_search_volume = total from keywords.json across all relevant keywords
  • intent_conversion_rate = % of searchers who have genuine purchase intent (see benchmarks below)
  • annual_price = from pricing.json target WTP, annualized

Intent conversion benchmarks by search type:

Search intentConversion rateExample query
Direct solution search ("app to track X")8–15%"habit tracker app", "budget planner iOS"
Problem-aware search ("how to X")3–8%"how to save money", "how to build habits"
Category browsing ("best X apps")5–12%"best workout apps 2026", "top meditation apps"
Tangential interest ("X tips")1–3%"productivity tips", "healthy eating advice"
Approach B — Community Size Proxy (primary when market_insights available)

When keyword data is weak but trend analysis reveals active communities, estimate from community engagement:

TAM = active_community_members × platform_multiplier × annual_price

Where:

  • active_community_members = sum of engaged users across platforms (subreddit subscribers, TikTok hashtag creators, App Store review volume)
  • platform_multiplier = ratio of total interested population to active community members (see below)

Platform multipliers (how many silent interested people per active community member):

Signal sourceMultiplierRationale
Reddit subscribers in niche subreddit20–50×~2–5% of interested people join a subreddit
TikTok hashtag creators (not views)100–500×Tiny fraction of interested people create content
App Store reviews for top competitor50–100×~1–2% of users leave reviews
Newsletter subscribers in niche10–30×Email subscribers are a warmer proxy
Approach C — Competitor Revenue Proxy (supplementary)

Estimate from competitor data in competitors.json:

TAM = sum of estimated annual revenue across all mapped competitors × market_coverage_factor

Where:

  • Estimate competitor revenue from: estimated_users × competitor_price × 12 × estimated_conversion_rate
  • market_coverage_factor = 1.3–2.0× (competitors don't capture the full market). Use 1.3× for saturated markets, 2.0× for markets with few competitors.
Triangulation

Run all available approaches and compare:

  • If estimates agree within 2× → high confidence. Use the geometric mean.
  • If estimates disagree by 2–5× → medium confidence. Use the most conservative estimate and note the range.
  • If estimates disagree by > 5× → low confidence. Flag assumptions that cause the divergence.

Always report which approaches were used and their individual estimates in the output.

SAM Filtering

SAM narrows TAM to the segment actually reachable by the app. Apply these filters in order:

Geographic & Platform Filters
FilterHow to applyData source
PlatformiOS-only → multiply TAM by iOS market share in target geographySee benchmarks below
GeographyEnglish-only → US + UK + CA + AU + NZ + IE. Specific country → that country onlyApp concept language
Age rangeIf app targets a demographic (teens, 50+), apply population %Idea description
Income bracketIf app requires disposable income for subscription, filter by incomePricing model

iOS market share by region (for iOS-only apps):

RegioniOS share (approximate)
United States55–58%
United Kingdom50–53%
Canada53–56%
Australia55–58%
Western Europe (avg)30–35%
Global25–28%
Southeast Asia10–15%
India4–6%
Latin America12–18%

For Android-only or cross-platform, apply the inverse or use 100%.

Segment Filters

Beyond geography and platform, apply any filters that narrow the market to people who would actually consider this specific app:

  • Niche focus (e.g., "fitness" → "climbing-specific fitness")
  • Prerequisite behavior (e.g., "already tracks workouts" → subset of fitness app users)
  • Tech-savviness requirement (if the app requires unusual setup, filter out casual users)

SAM should typically be 10–40% of TAM. If SAM > 50% of TAM, the filters are too loose. If SAM < 5% of TAM, the niche may be too narrow for viable economics.

SOM Estimation

SOM is what an indie developer can realistically capture. This is where most estimates go wrong — indie builders don't have the resources to capture meaningful market share in crowded categories.

SOM Capture Rate Benchmarks by Category
App categoryYear 1 capture rateYear 3 capture rateNotes
Utility / tool (calculator, converter, scanner)0.1–0.5% of SAM0.5–2.0%Discoverable via ASO, many competitors
Niche productivity (specific workflow tool)0.5–2.0%2.0–5.0%Smaller SAM but higher capture in the niche
Health & fitness (niche)0.3–1.5%1.0–4.0%Loyal users if retention is strong
Health & fitness (broad)0.05–0.2%0.2–0.8%Dominated by incumbents
Social / community0.01–0.1%0.1–0.5%Network effects favor incumbents; cold start is brutal
Content / media0.1–0.5%0.5–2.0%Depends heavily on content quality and curation
Finance / budgeting0.1–0.5%0.5–2.0%High trust barrier, but sticky once adopted
Creative tools (photo, video, design)0.2–1.0%1.0–3.0%Shareable output drives organic growth
Education / learning0.2–1.0%1.0–3.0%Retention is the main challenge
Lifestyle / habit0.3–1.5%1.0–4.0%Success varies wildly by habit loop quality

Use the lower end of the range when:

  • market_saturation from competitors.json is "high"
  • Founder is beginner tier (from user_profile.md)
  • No distribution advantage identified

Use the upper end when:

  • Founder has an existing audience or distribution edge
  • Strong ASO opportunity or viral loop exists
  • Market is growing fast (trend_velocity = "rising-fast")
Show full SKILL.md (501 more words)Show less
SOM Calculation
SOM_year_1 = SAM × capture_rate_year_1
SOM_year_3 = SAM × capture_rate_year_3 × growth_multiplier

Growth Rate Adjustment (from market_insights)

Trend velocity from market_insights directly affects the year-3 projection:

Trend velocityGrowth multiplier (applied to year-3 SOM)Rationale
rising-fast1.5–2.0×Market is expanding — your share of a growing pie grows faster
rising1.2–1.5×Moderate tailwind
stable1.0×No adjustment — capture rate is the only growth driver
declining0.5–0.8×Shrinking market — your absolute numbers may drop even if capture rate improves

If multiple platform files have different velocities, use the median velocity.

Reality Check Layer

Before finalizing, run these sanity checks:

CheckThresholdAction if triggered
TAM inflationTAM > $10B for a niche indie appAlmost certainly using top-down numbers. Redo with bottom-up only.
SAM too broadSAM > 50% of TAMFilters are too loose. Add platform/geography/niche constraints.
SOM fantasySOM year 1 > $500K for a solo developerReality-check the capture rate. Most indie apps earn $0–$50K in year 1.
No monetization evidencemonetization_evidence from market_insights is empty across all platformsDiscount TAM by 30–50%. People may want this but not pay for it.
Cold marketAll market_insights files show overall_verdict = "cold" or "cool"Flag as speculative. Note that market demand is unvalidated.

Market Size Verdict Thresholds

Based on SOM year 1 (the number that actually matters for an indie developer deciding whether to build):

SOM year 1VerdictMeaning for an indie dev
> $200KlargeSignificant indie opportunity. Even partial execution could be life-changing.
$50K–$200KmediumViable as a primary project. Can sustain a solo developer if retention is good.
$10K–$50KnicheSide-project scale. Viable if build cost is low and the founder has another income source.
< $10Kmicro-nicheHobby scale. Only worth building if the founder has a personal reason to build it or can expand the niche.

Process

  1. Load all available inputs: keywords.json, competitors.json, pricing.json, and all matching memory/market_insights/<niche>-*-<YYYY>-<MM>.md files.
  2. Extract calibration data from market_insights (trend velocity, top signals, monetization evidence, overall verdict).
  3. Run Approach A (search volume) if keywords.json is available.
  4. Run Approach B (community size proxy) if market_insights contain community signals.
  5. Run Approach C (competitor revenue proxy) if competitors.json has user/pricing data.
  6. Triangulate: compare estimates, determine confidence, select final TAM.
  7. Apply SAM filters (geography, platform, demographics, niche).
  8. Estimate SOM using category-appropriate capture rate benchmark.
  9. Apply growth multiplier from trend velocity.
  10. Run reality checks. Adjust if any are triggered.
  11. Determine market size verdict from SOM year 1 thresholds.
  12. Write output.

Output

Write to memory/ideas/<slug>/market_size.json:

json
{
  "methodology": "bottom-up | community-proxy | competitor-proxy | triangulated",
  "estimation_approaches": [
    {
      "approach": "search-volume | community-proxy | competitor-proxy",
      "tam_estimate": 0,
      "key_assumptions": []
    }
  ],
  "triangulation_confidence": "high | medium | low",
  "tam": {
    "value": 0,
    "currency": "USD",
    "period": "annual",
    "assumptions": []
  },
  "sam": {
    "value": 0,
    "filter_criteria": [],
    "sam_to_tam_ratio": 0
  },
  "som": {
    "year_1": 0,
    "year_3": 0,
    "capture_rate_year_1_pct": 0,
    "capture_rate_year_3_pct": 0,
    "growth_multiplier": 1.0,
    "growth_multiplier_source": ""
  },
  "market_insights_used": [],
  "trend_velocity_observed": "rising-fast | rising | stable | declining",
  "monetization_evidence_found": true,
  "reality_checks_triggered": [],
  "market_size_verdict": "large | medium | niche | micro-niche"
}

Notes

  • When used in the market-deep-dive workflow, pricing.json may not exist yet. In that case, use the median competitive price from competitors.json or a category benchmark ($3–$7/mo for typical B2C subscription apps).
  • When used in idea-validation (if the orchestrator includes it), the output feeds into idea-scoring's Monetization dimension. The market_size_verdict and SOM values are used alongside pricing and CAC data to assess overall monetization viability.
  • Market_insights files have a stale_after date. If all available files are past their stale date, flag the estimates as potentially outdated and recommend re-running trend-analysis before making a build decision.

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

Files

Just SKILL.md in skills/tam-sam-som-builder of MaxKmet/idea-validation-agents.

Open the folder on GitHubat commit 3a4c800

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Works with

Categories

Questions about Tam Sam Som Builder

What does Tam Sam Som Builder do?

Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to marketinsights trend data, competitor revenue proxies, and community size signals. Tam Sam Som Builder is an agent skill from MaxKmet/idea-validation-agents. Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to marketinsights trend data, competitor revenue proxies, and community size signals.

When should I use Tam Sam Som Builder?

Tam Sam Som Builder fits situations like: tasks that involve Market sizing.

How do I install Tam Sam Som Builder in Claude Code?

Run `npx skills add MaxKmet/idea-validation-agents --skill tam-sam-som-builder -a claude-code`. Or copy the skill folder (skills/tam-sam-som-builder in MaxKmet/idea-validation-agents) into .claude/skills/tam-sam-som-builder in your project. Claude Code loads it when a task matches its description.

How do I install Tam Sam Som Builder in Codex?

Run `npx skills add MaxKmet/idea-validation-agents --skill tam-sam-som-builder -a codex`. Or copy the skill folder (skills/tam-sam-som-builder in MaxKmet/idea-validation-agents) into .agents/skills/tam-sam-som-builder in your project. Codex loads it when a task matches its description.

Can I use Tam Sam Som Builder 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 MaxKmet/idea-validation-agents --skill tam-sam-som-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tam-sam-som-builder, .gemini/skills/tam-sam-som-builder, .github/skills/tam-sam-som-builder and .opencode/skills/tam-sam-som-builder in your project.

What does Tam Sam Som Builder need to run?

SKILL.md names no scripts, command-line tools or credentials: Tam Sam Som Builder is instructions for the agent only.

Does Tam Sam Som Builder 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 Tam Sam Som Builder 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 Tam Sam Som Builder use?

Tam Sam Som Builder 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 Tam Sam Som Builder use?

About 3.3k tokens (SKILL.md is roughly 13k 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 Tam Sam Som Builder?

Skills that share tags, products or a category with Tam Sam Som Builder: Last30days (mvanhorn/last30days-skill, 64k stars), TAM SAM SOM Calculator (deanpeters/Product-Manager-Skills, 7.2k stars), Last30days Cn (Jesseovo/last30days-skill-cn, 1.9k stars) and Market Sizing Analysis (nicepkg/auto-company, 195 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tam Sam Som Builder?

MaxKmet (a GitHub user) maintains it in MaxKmet/idea-validation-agents, which has 478 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on June 16, 2026.

Source: MaxKmet/idea-validation-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.