Data Silo Detection
v2.1.0Detect and map data silos in construction organizations. Identify disconnected data sources and integration opportunities
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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 (data silo detection) align with the content: SKILL.md contains Python classes and methods to analyze user-provided data sources. The claw.json permission for filesystem access is consistent with reading user files. Minor oddity: the skill is restricted to win32 in metadata despite Python code being cross-platform; this is likely a packaging/metadata choice rather than a functional inconsistency.
Instruction Scope
instructions.md and SKILL.md focus on processing user-supplied project data (CSV/Excel/JSON/direct input), validating inputs, analyzing connectivity, duplicates, and producing reports. There are no instructions in the provided material to read unrelated system files, pull environment variables, or transmit data to external endpoints.
Install Mechanism
No install specification or code files to fetch — the skill is instruction-only and requires python3 to be present. This minimizes the risk of hidden third-party downloads or arbitrary code installation.
Credentials
The skill declares no required environment variables or credentials. The only declared permission in claw.json is filesystem access, which is proportionate for a tool that reads user-provided files. There are no requests for unrelated cloud or API credentials.
Persistence & Privilege
The skill is not set always:true and does not request special persistent privileges. It is user-invocable and can be invoked autonomously by the agent by default (normal), but it does not attempt to modify other skills or agent-wide configuration in the materials provided.
Assessment
This skill appears internally consistent with its stated purpose. Before installing or running it: (1) verify the origin (homepage and author) if you care about provenance; (2) run it in a sandbox or with non-sensitive sample data first — the skill reads files, so avoid supplying documents that contain credentials or personal data; (3) confirm you are comfortable granting filesystem access (the skill needs to read user-supplied files); (4) note the win32 OS restriction in metadata — if you plan to run on macOS/Linux, validate compatibility; (5) if you want network-enabled analysis (e.g., querying APIs), ensure explicit instructions/consent are added, since the current materials do not show external calls.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.
Runtime requirements
🔗 Clawdis
OSWindows
Binspython3