SKILLEMALL.ai
GitHub ↗ RU Sign in

AC saas-churn-analysis

SaaS churn and retention analysis: cohort-based churn rates, retention curves, revenue churn vs logo churn, at-risk customer identification, expansion vs contraction MRR, churn recovery playbooks, and net revenue retention (NRR) benchmarking. Produces investor-ready retention charts and actionable recovery plans. Use when: analyzing why customers are churning, building cohort retention tables, calculating NRR/GRR, identifying at-risk accounts before they cancel, or presenting retention data to investors/board. NOT for: executing churn recovery outreach (use CRM/email tools), real-time subscription billing changes (use billing platform APIs), general SaaS KPI dashboards (use saas-metrics-dashboard), or revenue forecasting without churn context (use startup-financial-model).

ClawHub Agent Skills author: samledger67-dotcom v1.0.0 MIT-0 2 files body ≈ 4 774 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerData and analyticstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4774 tokens
    • 100Steps. 40 steps
    • 100Consistency. Name and required fields are in place
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 783: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 40 items
    • +3Output format is stated explicitly
    • +4Has examples (24 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This is a text-only SaaS churn analysis guide that fits its stated purpose and does not request credentials, persistence, or account-changing authority.
    LLM: benign (high) · VirusTotal: · 29 May 2026