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AF cohort-curve-model

Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Use when someone has real cohort retention numbers (month 0, 1, 2…) and asks what lifetime value, lifetime periods, or long-run retention they imply, or whether retention is flattening or leaking. Produces a fitted power curve (parameters, R², retention floor), a 24-36 period projection, and a real .xlsx with live formulas where editing ARPU recalculates LTV — via the bundled zero-dependency script.

ClawHub Agent Skills author: mohitagw15856 v1.0.0 MIT-0 2 files body ≈ 806 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Fit a retention curve to observed cohort data and project LTV — computed, not estimated.

As a process F 59/100 · Will not run — References files that are not bundled: scripts/cohort_model.py

GeneratorExcelWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
F
59/100
Will not run
References files that are not bundled: scripts/cohort_model.py
Tools and files w 18
0
When it triggers w 12
20
Failures and branches w 10
55
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/cohort_model.py
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 59/100

Will not run. References files that are not bundled: scripts/cohort_model.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/cohort_model.py
  • 20When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 806 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +3Description length 491: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 17 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This skill is a narrow retention-modeling helper with no evidence of hidden data access, persistence, or unsafe behavior.
LLM: benign (high) · VirusTotal: · 16 Jul 2026