Decision Trees
v1.0.1Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk/reward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.
⭐ 13· 3.9k·29 current·29 all-time
by@evgyur
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included README, SKILL.md, and a small Python EV calculator (scripts/decision_tree.py). The included code and instructions are directly relevant to computing and displaying expected values for decision trees.
Instruction Scope
SKILL.md stays on‑topic: it explains how to define options/outcomes, estimate probabilities/values, visualize the tree, calculate EV, and provide recommendations. It does not instruct the agent to read unrelated system files, access environment variables, or transmit data to external endpoints.
Install Mechanism
There is no install spec (instruction‑only skill) and the repository simply includes a small Python script. Nothing is downloaded or extracted during install. The README mentions optional manual download from GitHub releases, which is expected and not inherently risky.
Credentials
The skill declares no required environment variables, credentials, or config paths. The Python script reads an explicit JSON file provided by the user and can save a JSON file if the user chooses — these behaviors are proportional to the stated purpose.
Persistence & Privilege
always is false and the skill does not request permanent presence or modify other skills or system settings. Its only file writes are user‑initiated saves of decision JSON, which is expected for a calculator tool.
Assessment
This skill is internally consistent and appears to do what it claims: a local decision‑tree EV calculator and some documentation. Before installing or running, consider: (1) the publisher/source is listed as unknown — prefer skills from authors you trust or check the repository history; (2) the included script may read a JSON you point it at and can write a JSON file you name — avoid feeding it sensitive credentials or pointing it at system config files; (3) the tool performs no network activity, but an agent with autonomous invocation could run the script locally — if you allow autonomous runs, be aware it could create files in locations you permit. If you want extra assurance, open and inspect scripts/decision_tree.py yourself (it's short and readable) before running.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.