Data quality & reconciliation with exception
v1.0.0Reconciles data sources using stable identifiers (Pay Number, driving licence, driver card, and driver qualification card numbers), producing exception reports and “no silent failure” checks. Use when you need weekly matching with explicit reasons for non-joins and mismatches.
⭐ 1· 4k·16 current·17 all-time
by@kowl64
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 reconciliation of datasets by stable IDs and the provided SKILL.md, matching-rules.md, and CSV template all implement that workflow. The skill requests no binaries, env vars, or installs — consistent with a guidance/template skill that operates on user-provided CSV/XLSX files.
Instruction Scope
Runtime instructions focus on mapping, normalization, key validation, joins, exception categorization, and stop-and-ask gates. They explicitly require user-provided datasets and do not instruct reading unrelated system files, environment variables, or contacting external endpoints. The guidance to be read-only by default limits destructive actions.
Install Mechanism
No install spec and no code files are included; this is an instruction-only skill. That minimizes the surface for arbitrary code execution or supply-chain risk.
Credentials
The skill declares no required environment variables, credentials, or config paths — proportional for a documentation/plan-style reconciliation skill. Note: the skill is designed to process sensitive PII (pay numbers, licence numbers etc.), so operational data-handling controls are important even though the skill itself requests no secrets.
Persistence & Privilege
always is false and the skill does not request persistent system privileges. Autonomous invocation (model invocation enabled) is the platform default and reasonable here; nothing indicates the skill needs elevated or permanent presence.
Assessment
This skill appears coherent and low-risk from a packaging perspective, but it will be used to process sensitive identifiers (pay numbers, driving licence/card numbers). Before using: 1) ensure datasets you provide are allowed to be processed by the agent and comply with your data-protection rules (consider anonymizing or using synthetic data for testing); 2) confirm where exception reports are written and that storage/access controls are appropriate; 3) verify the agent's runtime will not upload data to external services you don't control; 4) test the matching rules on a small sample to confirm the join priority and thresholds behave as expected; 5) if you need automated remediation, update the SKILL.md to document safeguards — otherwise keep it read-only and require explicit approval for any edits. If you want stronger assurance, request an implementation (code) that you can review or run in an isolated environment.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.