# liquanzhou/ops_doc

运维简洁实用手册

Repository: https://github.com/liquanzhou/ops_doc
Canonical: https://ross.abutalabs.com/products/ops_doc
Language: Shell
License Family: other
Topics: shell, python, ops, sre
Last push: 2025-09-22T12:17:10+00:00

## Health v2 (maintenance only)
Score: 52/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 43, release rhythm 35, longevity 100
- inputs: {"age_days": 4117, "days_push": 345, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1363, forks 864 (observed 2026-08-28T04:04:30.393409+00:00)

## What it is
A concise, practical operations (Ops/SRE) handbook written in Chinese, covering monitoring, deployment, standardization, and Kubernetes usage experience. It is a curated knowledge document rather than executable software.

## Use cases
- learn ops best practices for monitoring and alerting
- standardize packaging and deployment processes
- understand kubernetes operational pitfalls
- onboard into a new ops role with a work checklist
- design service registration and health check standards
- reduce noisy alerts and improve incident response

## When to choose
- you are an ops/SRE engineer seeking practical, battle-tested guidance
- you want a concise Chinese-language operations reference
- you are setting up monitoring, release, or standardization practices in a small-to-mid company

## When to avoid
- you need executable tooling or scripts rather than a document
- you want comprehensive, formally reviewed documentation
- you need English-language material

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools, monitoring, deployment, documentation
- domain: developer-tools, tutorials, monitoring
- platform: cli
- tags: ops, sre, shell, operations-manual, best-practices, kubernetes, chinese, devops, linux

## Member repositories
- liquanzhou/ops_doc (main) score 52

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:30.393409+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:41:25.407749+00:00, confidence not recorded.
  - readme: https://github.com/liquanzhou/ops_doc (fetched 2026-08-28T04:04:30.393409+00:00, sha 811533714a18)
- Data as of 2026-08-30T08:39:29.467469+00:00.