saas-metrics-coach

v1.0.0

SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS busines...

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byAlireza Rezvani@alirezarezvani
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
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Benign
high confidence
Purpose & Capability
Name/description match the provided assets: SKILL.md, benchmark/formula references, input template, and three Python scripts that compute SaaS metrics, quick ratio, and forward simulations. No unrelated binaries, environment variables, or external services are requested.
Instruction Scope
SKILL.md instructs the agent to collect specific SaaS inputs, run the included local scripts (or fallback to formulas.md), and benchmark results against references/benchmarks.md. It does not ask the agent to read unrelated system files, use unspecified env vars, or transmit data to external endpoints.
Install Mechanism
There is no install spec (instruction-only). The included code files are pure Python and use only the standard library; nothing is downloaded from external URLs or installed automatically. Running the scripts will execute local code only.
Credentials
The skill declares no required environment variables, credentials, or config paths. The scripts accept numeric inputs via CLI/interactive prompts and do not reference secrets or external keys.
Persistence & Privilege
Flags are default (always: false, model invocation allowed). The skill does not request permanent presence or claim to modify other skills or system-wide settings.
Assessment
This skill is internally coherent and appears to do what it claims: local calculation, benchmarking, and recommendations. Before installing or using it: (1) Review the Python scripts yourself or run them in an isolated environment if you have any doubt — they use only the Python standard library and contain no network calls. (2) Be cautious about pasting very sensitive or identifying business data into the agent; the skill needs numeric revenue/customer inputs but does not require secrets. (3) Note small implementation details (e.g., gross-margin handling via percent vs decimal defaults and some input validation relying on truthy checks) — verify outputs for edge cases. (4) If you will allow the agent to invoke the skill autonomously, ensure your agent policies / prompts do not cause it to include or exfiltrate unrelated context; the skill itself does not contact external endpoints. If you want higher assurance, run the scripts locally on a sample dataset first and confirm results match your expectations.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.

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