AK Data Timeout Market Query
v1.0.0Query timeout status and market timeout ranking for any single job type on huge sharded job tables (job_a~job_d) without index changes.
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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 describe querying timeout metrics on job_a~job_d and the SKILL.md contains SQL templates, per-day split rules, and performance constraints that directly implement that purpose. Required resources (none) are consistent with an instruction-only SQL query helper.
Instruction Scope
Instructions confine work to the four sharded tables and to single-day windows, and include concrete SQL templates and aggregation/merge guidance. Note: the skill assumes specific schema details (columns like created_at, break_at, deliver_at, payload JSON path) and an index named index_created_at_status; it also uses UTC_TIMESTAMP() and JSON functions — if the target DB lacks those features or the index name differs the SQL will fail. SKILL.md does not instruct the agent to read unrelated files, env vars, or exfiltrate data.
Install Mechanism
Instruction-only skill with no install spec and no code files — nothing is written to disk and no external packages are fetched.
Credentials
No environment variables, credentials, or config paths are requested. The input contract requires tenantId/jobType/date etc., which are appropriate for the described queries.
Persistence & Privilege
always is false and the skill does not request persistent agent privileges or modify other skills. Autonomous invocation remains enabled by platform default but is not combined with other concerning privileges here.
Assessment
This skill is internally consistent for running per-day timeout analytics on the specified sharded tables, but before installing / using it you should: 1) verify your database schema (created_at, break_at, deliver_at, payload JSON path) and that an index named index_created_at_status exists or adapt the SQL accordingly; 2) run the queries against a read-only replica or staging instance first — the SQLs aggregate over very large tables and may still be heavy; 3) ensure the agent or tool that executes these templates uses parameterized queries or properly escapes inputs (tenantId, jobType, dates) to avoid SQL injection if templates are filled dynamically; 4) confirm the DB supports UTC_TIMESTAMP() and the JSON functions used (JSON_EXTRACT/JSON_UNQUOTE); 5) consider limiting result sizes and minMarketSample to avoid large detail scans. If any of the above cannot be met, treat the skill as potentially unsafe to run directly on production.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.