Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Cargo.toml dependency management patterns for HASH workspace. Use when adding dependencies, managing workspace dependencies, organizing Cargo.toml sections, setting version pinning, configuring default features, or working with public dependencies.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
This skill provides comprehensive guidance on adding and managing dependencies in Cargo.toml files within the HASH repository's workspace structure.
Automatically activates when:
HASH uses a strict workspace dependency pattern:
✅ DO:
[workspace.dependencies]version = "1.0.0" = ^1.0.0)default-features = false for all dependencies unless specifically neededworkspace = true in package Cargo.tomlpublic = true for dependencies exposed in public API❌ DON'T:
= prefix (e.g., =1.0.0) in workspace rootdefault-features without considering impactpublic = true for dependencies exposed in public APIEvery package Cargo.toml must organize dependencies into these sections:
[dependencies]
# Public workspace dependencies
hash-graph-types = { workspace = true, public = true }
hashql-core = { workspace = true, public = true }
# Public third-party dependencies
serde = { workspace = true, public = true, features = ["derive"] }
tokio = { workspace = true, public = true }
# Private workspace dependencies
error-stack = { workspace = true }
hash-codec = { workspace = true }
# Private third-party dependencies
tracing = { workspace = true }
regex = { workspace = true }
Keep all 4 section comments even if a section is empty.
1.2.3workspace = true (+ public = true if needed)Choose the guide that matches your task:
Use when: Adding new dependencies to workspace root
Use when: Adding dependencies to a package Cargo.toml
public = trueUse when: Looking for real examples from HASH codebase
@local/codec@local/hashql/core# 1. Add to workspace root Cargo.toml
[workspace.dependencies]
my-crate = { version = "1.2.3", default-features = false }
# 2. Add to package Cargo.toml (appropriate section)
[dependencies]
# Private third-party dependencies
my-crate = { workspace = true }
# Use when the dependency appears in your public API
serde = { workspace = true, public = true, features = ["derive"] }
tokio = { workspace = true, public = true }
[dependencies]
serde = { workspace = true, optional = true, features = ["derive"] }
[features]
serde = ["dep:serde", "other-dep/serde"]
Skill Status: Production-ready following Anthropic best practices ✅ Line Count: < 150 (following 500-line rule) ✅ Progressive Disclosure: 3 detailed resource files ✅
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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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