feast
No security findings. Scanned 1 time, most recently 2026-02-06.
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
Comprehensive meal planning system with cultural themes, authentic recipes, intelligent shopping, and surprise reveals. Use when: - Planning weekly meals or menus - Generating shopping lists - Asking for recipe ideas or cooking help - Reviewing past meals or planning ahead - Onboarding a new user to the meal system - Looking for cuisine inspiration or cultural food events - Tracking dietary goals or nutrition - Managing favourites, failures, or meal history
- Package
- 14 files, 71 KB
- SKILL.md
- 1118 words, 12+ sections
- Code
- Python (1)
SKILL.md covers: Feast · Quick Start · Core Files · Weekly Cadence · Workflows · Onboarding · Planning (Thursday) · Confirmation (Friday) · Shopping List (Saturday) · Daily Reveal · Review (End of Week) · Recipe Regionalisation
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 8.7 KB |
references/conversions.md | doc | 3.3 KB |
references/events.md | doc | 2.9 KB |
references/nutrition.md | doc | 3.2 KB |
references/onboarding.md | doc | 7.8 KB |
references/price-checking.md | doc | 7.0 KB |
references/theme-research.md | doc | 8.4 KB |
scripts/update-history.py | script | 4.6 KB |
templates/profile.yaml | config | 3.5 KB |
templates/shopping-list.md | doc | 4.0 KB |
templates/week.md | doc | 11 KB |
references/cuisines/README.md | doc | 1.5 KB |
references/seasonality/README.md | doc | 1.1 KB |
references/seasonality/uk.md | doc | 4.5 KB |
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/feast
- Raw scan result: feast-scan.json
- Badge for your README:
- Request a re-scan