Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Productivity
Data quality & reconciliation with exception
Reconciles 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.
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Reconciles 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.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/matching-rules.md, assets/exceptions-report-template.csv
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- KOwl64
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
PURPOSE
Reconciles data sources using stable identifiers (Pay Number, driving licence, driver card, and driver qualification card numbers), producing exception reports and “no silent failure” checks.
WHEN TO USE
TRIGGERS: Reconcile these two data sources and produce an exceptions report with reasons. Match names and payroll numbers across files and flag anything that does not join. Build a ‘no silent failure’ check that stops the pipeline if counts do not match. Create a weekly variance report for missing records, duplicates, and date gaps. Design a data quality scorecard with thresholds and red flags. DO NOT USE WHEN… You need open-ended fuzzy matching without acceptance criteria. There are no stable identifiers in any source.
INPUTS
REQUIRED: At least two datasets (CSV/XLSX) with Pay Number and/or driver document numbers. Which fields must match (e.g., Name, expiry date). OPTIONAL: Normalization rules (case, spaces, punctuation). Thresholds for gates/scorecard (max % missing, etc.). EXAMPLES: Payroll export + compliance register Two weekly exports from different systems
OUTPUTS
Reconciliation plan (matching rules, normalization, join strategy). Exceptions report spec (CSV columns + reason codes) and variance checks. Optional artifacts: assets/exceptions-report-template.csv + references/matching-rules.md. Success = every record is categorized (matched/missing/duplicate/mismatch/invalid) with an explicit reason; pipelines stop on anomalies.
WORKFLOW
Confirm sources and key priority (Pay Number → Driver Card → Driving Licence → DQC). Normalize columns: trim spaces; standardize case; strip common punctuation for document numbers. Validate keys: flag blanks/invalid formats; identify duplicates per source. Join: exact join on Pay Number; then attempt secondary joins only for remaining unmatched items. Produce exception categories with reasons: Missing in A/B, Duplicate key, Field mismatch, Invalid key. “No silent failure” gates: counts within tolerance; unmatched rate below threshold; duplicate spikes flagged. STOP AND ASK THE USER if: columns are not mapped, multiple competing IDs exist with no priority, expected tolerances are unspecified.
OUTPUT FORMAT
exception_type,reason,source_a_id,source_b_id,pay_number,name,field,source_a_value,source_b_value Reason codes: MISSING_IN_A, MISSING_IN_B, MISMATCH, DUPLICATE_KEY, INVALID_KEY.
SAFETY & EDGE CASES
Read-only by default; don’t auto-edit source data. Route exceptions to review. Deterministic matching rules first; avoid fuzzy matching unless explicitly requested. Always produce an exceptions report; never drop unmatched rows.
EXAMPLES
Input: “Payroll vs compliance; match by Pay Number; flag name mismatch.” Output: join plan + mismatch reasons + exceptions report schema. Input: “Some rows have blank Pay Number.” Output: secondary key matching + invalid-key exceptions for truly unmatchable rows.
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Assets
- SKILL.md
- references/matching-rules.md
- assets/exceptions-report-template.csv