SKILLEMALL.ai
GitHub ↗ RU Sign in

AC churn-prevention

Reduce voluntary and involuntary churn with cancel flows, save offers, dunning, win-back tactics, and retention strategy. Use when users are cancelling, failed payments are rising, or subscription retention needs improvement.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 4 files body ≈ 4 393 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Reduce voluntary and involuntary churn with cancel flows, save offers, dunning, win-back tactics, and retention strategy.

As a process C 64/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

ProcedureStripeData and analyticstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
64/100
Has gaps
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: churn-prevention (sickn33/agentic-awesome-skills)

How to improve

    For the model run — optional
    • 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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"

    Process rating: all ten parameters 64/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 12 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4393 tokens
    • 85Steps. 87 steps, 2 vague phrases
    • 100Consistency. Name and required fields are in place
    • low 11 top-level sections: this looks like several domains in one skill
    • medium 6 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 225: enough signal without eating the budget
    • +4Structure: 35 headings
    • +3Step-by-step instructions: 87 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.