功能描述

Caching strategies, invalidation, eviction policies, HTTP caching, distributed caching, and anti-patterns. Use when designing cache layers, choosing eviction policies, debugging stale data, or optimizing read-heavy workloads.

使用说明 (SKILL.md)

Caching Patterns

Name: caching
Author: wpank

A well-placed cache is the cheapest way to buy speed. A misplaced cache is the most expensive way to buy bugs.

Cache Strategies

Strategy	How It Works	When to Use
Cache-Aside (Lazy)	App checks cache → miss → reads DB → writes to cache	Default choice — general purpose
Read-Through	Cache fetches from DB on miss automatically	ORM-integrated caching, CDN origin fetch
Write-Through	Writes go to cache AND DB synchronously	Read-heavy with strong consistency
Write-Behind	Writes go to cache, async flush to DB	High write throughput, eventual consistency OK
Refresh-Ahead	Cache proactively refreshes before expiry	Predictable access patterns, low-latency critical

Cache-Aside Flow:

  App ──► Cache ──► HIT? ──► Return data
              │
              ▼ MISS
          Read DB ──► Store in Cache ──► Return data

Cache Invalidation

Method	Consistency	When to Use
TTL-based	Eventual (up to TTL)	Simple data, acceptable staleness
Event-based	Strong (near real-time)	Inventory, profile updates
Version-based	Strong	Static assets, API responses, config
Tag-based	Strong	CMS content, category-based purging

TTL Guidelines

Data Type	TTL	Rationale
Static assets (CSS/JS/images)	1 year + cache-busting hash	Immutable by filename
API config / feature flags	30–60 seconds	Fast propagation needed
User profile data	5–15 minutes	Tolerable staleness
Product catalog	1–5 minutes	Balance freshness vs load
Session data	Match session timeout	Security requirement

HTTP Caching

Cache-Control Directives

Directive	Meaning
`max-age=N`	Cache for N seconds
`s-maxage=N`	CDN/shared cache max age (overrides max-age)
`no-cache`	Must revalidate before using cached copy
`no-store`	Never cache anywhere
`must-revalidate`	Once stale, must revalidate
`private`	Only browser can cache, not CDN
`public`	Any cache can store
`immutable`	Content will never change (within max-age)
`stale-while-revalidate=N`	Serve stale for N seconds while fetching fresh

Common Recipes

# Immutable static assets (hashed filenames)
Cache-Control: public, max-age=31536000, immutable

# API response, CDN-cached, background refresh
Cache-Control: public, s-maxage=60, stale-while-revalidate=300

# Personalized data, browser-only
Cache-Control: private, max-age=0, must-revalidate
ETag: "abc123"

# Never cache (auth tokens, sensitive data)
Cache-Control: no-store

Conditional Requests

Mechanism	Request Header	Response Header	How It Works
ETag	`If-None-Match: "abc"`	`ETag: "abc"`	Hash-based — 304 if match
Last-Modified	`If-Modified-Since: \x3Cdate>`	`Last-Modified: \x3Cdate>`	Date-based — 304 if unchanged

Prefer ETag over Last-Modified — ETags detect content changes regardless of timestamp granularity.

Application Caching

Solution	Speed	Shared Across Processes	When to Use
In-memory LRU	Fastest	No	Single-process, bounded memory, hot data
Redis	Sub-ms (network)	Yes	Production default — TTL, pub/sub, persistence
Memcached	Sub-ms (network)	Yes	Simple key-value at extreme scale
SQLite	Fast (disk)	No	Embedded apps, edge caching

Redis vs Memcached

Feature	Redis	Memcached
Data structures	Strings, hashes, lists, sets, sorted sets	Strings only
Persistence	AOF, RDB snapshots	None
Pub/Sub	Yes	No
Max value size	512 MB	1 MB
Verdict	Default choice	Pure cache at extreme scale

Distributed Caching

Concern	Solution
Partitioning	Consistent hashing — minimal reshuffling on node changes
Replication	Primary-replica — writes to primary, reads from replicas
Failover	Redis Sentinel or Cluster auto-failover

Rule of thumb: 3 primaries + 3 replicas minimum for production Redis Cluster.

Cache Eviction Policies

Policy	How It Works	When to Use
LRU	Evicts least recently accessed	Default — general purpose
LFU	Evicts least frequently accessed	Skewed popularity distributions
FIFO	Evicts oldest entry	Simple, time-ordered data
TTL	Evicts after fixed duration	Data with known freshness window

Redis default is noeviction. Set maxmemory-policy to allkeys-lru or volatile-lru for production.

Caching Layers

Browser Cache → CDN → Load Balancer → App Cache → DB Cache → Database

Layer	What to Cache	Invalidation
Browser	Static assets, API responses	Versioned URLs, Cache-Control
CDN	Static files, public API responses	Purge API, surrogate keys
Application	Computed results, DB queries, external API	Event-driven, TTL
Database	Query plans, buffer pool, materialized views	`ANALYZE`, manual refresh

Cache Stampede Prevention

When a hot key expires, hundreds of requests simultaneously hit the database.

Technique	How It Works
Mutex / Lock	First request locks, fetches, populates; others wait
Probabilistic early expiration	Random chance of refreshing before TTL
Request coalescing	Deduplicate in-flight requests for same key
Stale-while-revalidate	Serve stale, refresh asynchronously

Cache Warming

Strategy	When to Use
On-deploy warm-up	Predictable key set, latency-sensitive
Background job	Reports, dashboards, catalog data
Shadow traffic	Cache migration, new infrastructure
Priority-based	Limited warm-up time budget

Cold start impact: A full cache flush can increase DB load 10–100x. Always warm gradually or use stale-while-revalidate.

Monitoring

Metric	Healthy Range	Action if Unhealthy
Hit rate	> 90%	Low → cache too small, wrong TTL, bad key design
Eviction rate	Near 0 steady state	High → increase memory or tune policy
Latency (p99)	\x3C 1ms (Redis)	High → network issue, large values, hot key
Memory usage	\x3C 80% of max	Approaching max → scale up or tune eviction

NEVER Do

NEVER cache without a TTL or invalidation plan — data rots; every entry needs an expiry path
NEVER treat cache as durable storage — caches evict, crash, and restart; always fall back to source of truth
NEVER cache sensitive data (tokens, PII) without encryption — cache breaches expose everything in plaintext
NEVER ignore cache stampede on hot keys — one expired popular key can take down your database
NEVER use unbounded in-memory caches in production — memory grows until OOM-killed
NEVER cache mutable data with immutable Cache-Control — browsers will never re-fetch
NEVER skip monitoring hit/miss rates — you won't know if your cache is helping or hurting

安全使用建议

This skill is a documentation/reference skill about caching and appears internally consistent. Before installing, verify the skill source (there's no homepage and the owner ID is opaque). Do not run unfamiliar install commands you find in README (especially ad-hoc npx commands pointing at arbitrary GitHub paths). If you want the content, prefer reviewing SKILL.md/README locally or copying the text rather than executing remote install scripts. If you plan to add code from an external repo, inspect the repository contents first. Overall risk is low for this instruction-only skill, but always avoid providing secrets or running unreviewed installs.

功能分析

Type: OpenClaw Skill Name: caching Version: 1.0.0 The skill bundle provides comprehensive documentation on caching strategies and related concepts. Neither SKILL.md nor README.md contain any prompt injection attempts against the AI agent, malicious execution commands, data exfiltration, persistence mechanisms, or obfuscation. The installation instructions in README.md use standard package managers (`npx add`) and file copy commands (`cp -r`) to install the skill itself, which is a common and benign practice for skill distribution.

能力评估

✓ Purpose & Capability

The skill is purely documentation about caching strategies, HTTP caching, eviction policies, distributed caching, and anti-patterns. It requests no binaries, env vars, or config paths — all of which align with a knowledge/guide-style skill.

✓ Instruction Scope

SKILL.md contains explanatory guidance, recipes, and examples for caching behavior. It does not instruct the agent to read unrelated system files, access credentials, or send data to external endpoints.

ℹ Install Mechanism

There is no formal install spec (instruction-only), which is low risk. The README contains an example 'npx add https://github.com/…/tree/…' command that is non-standard (GitHub tree URL) — it's an odd or potentially invalid usage of npx rather than a tracked package install. This is a documentation oddity rather than executable install metadata in the skill itself; avoid running ad-hoc install commands from unknown URLs.

✓ Credentials

The skill requests no environment variables, credentials, or config paths. That is proportionate for a read-only documentation skill.

✓ Persistence & Privilege

always is false and the skill is instruction-only with no code to persist or run on install. It does not request elevated or persistent platform privileges.

版本历史

v1.0.0

Initial release of the caching skill. - Covers core caching strategies: cache-aside, read-through, write-through, write-behind, refresh-ahead. - Details cache invalidation methods, TTL guidelines, and HTTP caching best practices. - Summarizes common application caching solutions: in-memory, Redis, Memcached, SQLite. - Explains distributed cache concepts, eviction policies, and cache layer design. - Includes anti-patterns, stampede prevention, cache warming, and monitoring recommendations.

元数据

Slug caching

版本 1.0.0

许可证 —

累计安装 9

当前安装数 9

历史版本数 1

常见问题

caching 是什么？

Caching strategies, invalidation, eviction policies, HTTP caching, distributed caching, and anti-patterns. Use when designing cache layers, choosing eviction policies, debugging stale data, or optimizing read-heavy workloads. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1040 次。

如何安装 caching？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install caching」即可一键安装，无需额外配置。

caching 是免费的吗？

是的，caching 完全免费（开源免费），可自由下载、安装和使用。

caching 支持哪些平台？

caching 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 caching？

由 wpank（@wpank）开发并维护，当前版本 v1.0.0。

caching