Openclaw Skills helps you design, execute, record, and analyze inspection sampling workflows from raw data to audit-ready reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-inspection-sampling
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-inspection-sampling using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Inspection Sampling Assistant?
Openclaw Skills for inspection sampling is a quality-control workflow skill that turns raw inspection data into structured sampling plans. It supports scheme creation, data parsing, sample selection, record keeping, and statistical analysis across CSV, Excel, and JSON inputs, with methods aligned to GB/T2828-style sampling needs.
With Openclaw Skills, teams can standardize sampling operations, preserve traceability, and evaluate whether sampled results represent the full dataset. The skill is built for repeatable quality inspection work where deterministic scheme management, clear selection rules, and evidence-based reporting matter.
Inspection Sampling Assistant Use Cases
Openclaw Skills is useful when you need to:
- Create reusable sampling schemes for quality inspection, acceptance sampling, or standard QA workflows.
- Parse incoming datasets from CSV, Excel, or JSON into a normalized JSON layer for sampling.
- Generate sample plans for simple random, stratified, systematic, or cluster sampling.
- Track sample selection history for audit trails, traceability, and compliance reviews.
- Analyze sampled outputs against the original dataset to judge representativeness and sampling quality.
- Handle large inspection batches with a workflow that can be reviewed and replayed later.
How Inspection Sampling Assistant Works
Openclaw Skills follows a clear inspection sampling lifecycle:
- Define or update a sampling scheme with
scheme_manager.py, choosing a type and parameters such assample_size,sample_rate,strata_field,interval, orcluster_field. - Load inspection data with
data_parser.py, which converts CSV, Excel, or JSON into a normalized JSON structure. - Run
sampler.pyto apply the selected sampling algorithm and generate sample indices plus selection-rule notes. - Enable
--recordto persist a sampling log under./sampling_records/for traceability and future review. - Feed the sample result and original data into
analyzer.pyto produce coverage, distribution, missingness, and quality metrics. - Review the report output and compare the sampled set against inspection requirements before accepting the sampling outcome.
Inspection Sampling Assistant Setup
- Prepare a Python environment with the required data libraries.
pip install 'pandas>=1.5.0' 'numpy>=1.21.0'
- Run from the repository root so relative paths resolve correctly.
mkdir -p sampling_schemes sampling_records
- Verify the helper scripts are available.
ls scripts
Use
references/format_spec.mdwhen you need to confirm dataset format or scheme parameter conventions.Start with a scheme, then parse data, generate samples, and analyze results in that order.
Inspection Sampling Assistant Data Schema & Taxonomy
Openclaw Skills organizes inspection sampling work into four layers:
| Layer | Path / Artifact | Purpose | Key fields |
|---|---|---|---|
| Scheme store | ./sampling_schemes/ |
Persist sampling templates | name, type, sample_size, sample_rate, strata_field, interval, cluster_field |
| Parsed dataset | data_parser.py output JSON |
Normalize source files | rows, objects, field names, source format |
| Sampling result | sampler.py output JSON |
Store selected samples and rules | sample indices, method notes, selection metadata |
| Audit record | ./sampling_records/ |
Keep permanent trace logs | scheme parameters, time, data summary, indices, operation log |
| Analysis report | analyzer.py output JSON |
Summarize sample quality | coverage rate, distribution comparison, missing-field stats, numeric stats, categorical frequency, conclusions |
Additional taxonomy:
- Input formats: CSV, Excel (
.xlsx/.xls), JSON array, JSON object array. - Scheme types:
simple,stratified,systematic,cluster. - Parameter model:
sample_sizeandsample_rateare mutually exclusive; stratified sampling requiresstrata_field; systematic sampling requiresinterval; cluster sampling requirescluster_field. - Paths are relative to the user working directory, which keeps Openclaw Skills portable across projects.
Inspection Sampling Assistant Advanced Features
- Full scheme CRUD via
scheme_manager.py(create,list,get,update,delete) - Algorithm coverage for simple random, stratified, systematic, and cluster sampling
- Multi-format ingestion with automatic delimiter handling and Chinese encoding support for CSV
- Persistent sampling records for traceability, auditing, and historical replay
- Statistical reporting that compares sampled data to the original population
- Memory-conscious workflow guidance for large datasets, including batch processing when needed
- Relative-path-friendly execution so Openclaw Skills can run inside different local workspaces
SKILL.md
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