Cac Modeler

What it does

When your agent uses it

• Business, Finance & HR work in your project

Example prompts

• “Use the cac-modeler skill to model LTV, CAC by channel, LTV:CAC ratios, and payback period for an indie developer”
• “/cac-modeler”

Workflow steps

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

1. 1Load all inputs: pricing.json, retention.json, distribution.json, competitors.json, user_profile.md, and all matching…
2. 2Extract market_insights calibration signals (trend velocity, platform activity, monetization evidence, competitor ad activity).
3. 3Determine founder's budget tier from user_profile.md.
4. 4Compute LTV using the formula, pricing data, and retention data. Note confidence level.
5. 5For each applicable channel (after relevance filter), estimate CAC using benchmarks adjusted by market_insights and distribution.json…
6. 6Compute LTV:CAC ratio per channel. Classify each.
7. 7Compute payback period for viable channels.
8. 8Rank channels by LTV:CAC ratio. Select recommended first channel — must be executable by this founder at their budget tier.
9. 9Determine viability verdict.
10. 10Write 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

Cac Modeler loads about 3.3k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 1,389 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~59

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

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). 1,389 words, ~3,315 tokens.

name

cac-modeler

description

Models LTV, CAC by channel, LTV:CAC ratios, and payback period for an indie developer. Uses market_insights to calibrate channel CPMs and competitive intensity. Includes indie budget tier definitions and viability thresholds.

<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/cac.json -->

LTV = ARPU_monthly × average_lifespan_months

CAC = cost_per_impression / (CTR × install_rate × activation_rate)

payback_months = CAC / ARPU_monthly

{
  "ltv": {
    "estimated_ltv": 0,
    "arpu_monthly": 0,
    "average_lifespan_months": 0,
    "ltv_confidence": "high | medium | low",
    "ltv_assumptions": []
  },
  "founder_budget_tier": "bootstrap | lean | moderate | serious",
  "cac_by_channel": {
    "aso_organic": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "content_seo": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "tiktok_organic": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "reddit_community": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "paid_social_meta": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "paid_social_tiktok": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "influencer": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "word_of_mouth": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "press_product_hunt": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0, "one_time_cohort_estimate": 0 }
  },
  "skipped_channels": [],
  "viable_channels": [],
  "marginal_channels": [],
  "non_viable_channels": [],
  "recommended_first_channel": "",
  "recommended_first_channel_rationale": "",
  "payback_period_months": 0,
  "market_insights_adjustments": [],
  "viability_verdict": "viable | marginal | not-viable",
  "viability_verdict_rationale": ""
}

© 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/cac-modeler of MaxKmet/idea-validation-agents.

Open the folder on GitHubat commit 3a4c800