decision-trees
No security findings. Scanned 1 time, most recently 2026-02-05.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Decision 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.
- Package
- 3 files, 18 KB
- SKILL.md
- 1325 words, 12+ sections
- Code
- Python (1)
SKILL.md covers: Decision Trees — Structured Decision-Making · When to Use · Method · Formula · Classic Example (from Wikipedia) · Estimates: · Tree: · Business Example · Estimates: · Tree: · Trading Example · Estimates:
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
README.md | doc | 4.6 KB |
SKILL.md | doc | 8.8 KB |
scripts/decision_tree.py | script | 4.9 KB |
External hosts referenced
en.wikipedia.org clawdhub.com
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Llm analyzer. A language model reads the skill and reports intent-level issues the rules cannot express.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
- Meta analyzer. A second model pass that validates, deduplicates and re-scores the other analyzers' findings.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://clawhub.ai/skills/decision-trees
- Raw scan result: decision-trees-scan.json
- Badge for your README:
- Request a re-scan