Data Completeness Check
v1.0.0数据完整性判断机制。通过前晚基准值 vs 早上值对比，判断 Power BI 数据是否回灌完毕。适用于任何有延迟回灌的数据源。避免发送不完整数据。
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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
The name/description (data completeness check for Power BI / T+1 sources) matches the instructions (capture a sentinel metric at 23:30 and 09:00 and compare). Nothing requested (no env vars, no binaries, no installs) is disproportionate to that stated purpose.
Instruction Scope
The SKILL.md is high-level and prescribes a manual/automated sampling workflow (capture metric at two times, compare, use thresholds). It does not specify HOW to fetch metrics (e.g., Power BI API, DB query, or exported file) or where results are recorded. This ambiguity is not inherently malicious but means implementers must decide which connector/credentials to use.
Install Mechanism
No install spec and no code files are present (instruction-only), so nothing is written to disk and no third-party packages are pulled. This is the lowest-risk install surface.
Credentials
The skill declares no required environment variables, secrets, or config paths. The described task could require Power BI or database credentials in practice, but those are not requested by the skill itself—so the declared access is proportionate.
Persistence & Privilege
always is false and the skill does not request persistent presence or modify other skills or agent-wide settings. Autonomous invocation is allowed by platform default but is not excessive given the simple nature of the instructions.
Assessment
This skill is high-level and coherent, but check these practical points before enabling it: 1) Decide and document how metrics will be fetched (Power BI API, DB query, exported file) and ensure any credentials used are minimal-scope and provided separately from this skill. 2) If you allow the agent to call connectors autonomously, confirm which tokens/accounts it will use and limit their scope. 3) Define and record where the sampled values and logs will be stored and who can view them. 4) Test the procedure on non-sensitive data to validate thresholds and retry logic (30-minute wait) to avoid false positives/negatives. 5) If you need the skill to perform automated fetches, consider adding explicit instructions about the connector and required permissions so users understand what access will be needed.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.