ClawHub

Insurance Claims Processor

v1.0.0

Processes and analyzes insurance claims across policy types, providing coverage mapping, liability scoring, reserve estimates, fraud detection, and settlemen...

⭐ 0· 501·1 current·1 all-time
by@1kalin
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
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Benign
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OpenClawOpenClaw
Benign
high confidence
✓
Purpose & Capability
The name/description (claims processing, liability scoring, reserves, fraud flags) matches the SKILL.md contents and the outputs it promises. The skill does not request unrelated binaries, credentials, or config paths.
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Instruction Scope
The SKILL.md stays on task (describe claim, run analysis, produce structured output). It does rely on the user/agent to supply potentially sensitive claim data (names, reports, photos), which is expected for this use case but is a privacy consideration — the instructions do not direct the agent to read arbitrary system files or exfiltrate data to hidden endpoints.
✓
Install Mechanism
No install spec and no code files (instruction-only), so nothing is written to disk or downloaded by the skill itself. README suggests a clawhub install command but the package contains no install artifacts.
✓
Credentials
The skill declares no required environment variables, credentials, or config paths. There are no unexplained secret requests.
✓
Persistence & Privilege
always:false and default model invocation settings are used. The skill does not request permanent presence or to modify other skills. Autonomous invocation is allowed (platform default) but not coupled with other red flags.
Assessment
This skill is internally consistent, but it expects you to provide real claim data — which may include personal data and confidential business information. Before using in production: (1) test with anonymized or synthetic claims, (2) verify jurisdiction-specific legal/regulatory outputs for your territories, (3) validate fraud and reserve recommendations with a human reviewer, and (4) note the package source is effectively 'unknown' (README links to an AfrexAI page); if provenance is important, confirm the provider and review their privacy/security practices before sending sensitive data.

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

claimsvk9734zx0t83af4d2ww7z02bf0181ncxjfraudvk9734zx0t83af4d2ww7z02bf0181ncxjinsurancevk9734zx0t83af4d2ww7z02bf0181ncxjlatestvk9734zx0t83af4d2ww7z02bf0181ncxjprocessingvk9734zx0t83af4d2ww7z02bf0181ncxj

License

MIT-0
Free to use, modify, and redistribute. No attribution required.
Termshttps://spdx.org/licenses/MIT-0.html

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