Chance Fc3d Predictor
PassAudited by ClawScan on May 11, 2026.
Overview
This is an instruction-only lottery analysis skill with no credentials or bundled executable code, but users should verify its install references and avoid over-trusting its betting claims.
Before installing, confirm you are installing the reviewed `chance-fc3d-predictor` skill rather than the differently named package in the README. If you copy the Python examples, use a virtual environment and approve the network request. Treat all lottery predictions as entertainment, not reliable financial advice.
Findings (4)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
A user might over-trust the analysis and spend more money on lottery bets than intended.
The skill includes responsible-gambling disclaimers, but these phrases could make lottery recommendations seem more predictive or advantageous than random lottery odds justify.
"**理论中奖率**: 约30-40%" and "长期坚持有一定优势"
Treat outputs as entertainment or statistical illustration only, set a strict spending limit, and do not rely on the skill as a way to improve gambling outcomes.
A user following the README could install a package different from the one reviewed here.
The evaluated registry slug is `chance-fc3d-predictor`, while the README install command points to a differently named package. This may be stale documentation, but it is a provenance/identity mismatch users should verify.
`# Install this skill` / `npx clawhub install @gechengling/lottery-fc3d-analyst`
Install only the exact reviewed ClawHub skill/package you intended, and verify the package owner and slug before running installation commands.
If the user copies and runs the example, their Python environment will install external dependencies.
The reference documentation provides optional user-run setup for third-party Python packages. This is expected for an analysis script, but the packages are unpinned and would be fetched from the user's configured package index.
`pip install requests pandas plotly kaleido`
Run optional code in a virtual environment, review packages before installing, and pin trusted versions if reproducibility matters.
If executed, the user's environment will make a network request to a third-party site.
The optional Python example contacts an external lottery-data website. This is purpose-aligned for fetching historical results and does not show credential use or unrelated data transfer.
`url = "https://datachart.500.com/ssq/history/newinc/history.php"` ... `response = requests.get(url, params=params, headers=headers, timeout=10)`
Only run the data-fetching example when you approve the network access, and verify the endpoint and data source are acceptable for your use.