提示词与推理

提示词工程、角色设定和推理模式

312 個 Skills 可用

Skills 列表

mantis-critic

mantis-critic

1.5Kprompting-reasoning

Assesses the production viability of findings, filtering out debug-only features and assertion traps. Use when findings have been validated and you need to confirm they are triggerable in production release builds (with assertions disabled). Don't use for writing reproduction scripts or patches.

google avatargoogle
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mantis-report

mantis-report

1.5Kprompting-reasoning

Generates a human-readable security review packet compiled from confirmed findings and exploit chains. Use at the end of a review cycle to produce stakeholder-facing documentation. Don't use for auditing code or verifying patches directly.

google avatargoogle
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mantis-patch

mantis-patch

1.5Kprompting-reasoning

Generates minimal security fixes using transactional isolation (shadow directories or file backups), applies patches, and verifies them. Use when security findings are successfully reproduced and need patches applied and verified. Don't use for initial vulnerability research or reproduction payload generation.

google avatargoogle
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prompt-engineering

prompt-engineering

1.5Kprompting-reasoning

Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

neolabhq avatarneolabhq
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write-judge-prompt

write-judge-prompt

1.4Kprompting-reasoning

Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.

ai-evals-course avatarai-evals-course
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generate-synthetic-data

generate-synthetic-data

1.4Kprompting-reasoning

Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.

ai-evals-course avatarai-evals-course
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m07-concurrency

m07-concurrency

1.4Kprompting-reasoning

CRITICAL: Use for concurrency/async. Triggers: E0277 Send Sync, cannot be sent between threads, thread, spawn, channel, mpsc, Mutex, RwLock, Atomic, async, await, Future, tokio, deadlock, race condition, 并发, 线程, 异步, 死锁

actionbook avataractionbook
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m01-ownership

m01-ownership

1.4Kprompting-reasoning

CRITICAL: Use for ownership/borrow/lifetime issues. Triggers: E0382, E0597, E0506, E0507, E0515, E0716, E0106, value moved, borrowed value does not live long enough, cannot move out of, use of moved value, ownership, borrow, lifetime, 'a, 'static, move, clone, Copy, 所有权, 借用, 生命周期

actionbook avataractionbook
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m04-zero-cost

m04-zero-cost

1.4Kprompting-reasoning

CRITICAL: Use for generics, traits, zero-cost abstraction. Triggers: E0277, E0308, E0599, generic, trait, impl, dyn, where, monomorphization, static dispatch, dynamic dispatch, impl Trait, trait bound not satisfied, 泛型, 特征, 零成本抽象, 单态化

actionbook avataractionbook
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m02-resource

m02-resource

1.4Kprompting-reasoning

CRITICAL: Use for smart pointers and resource management. Triggers: Box, Rc, Arc, Weak, RefCell, Cell, smart pointer, heap allocation, reference counting, RAII, Drop, should I use Box or Rc, when to use Arc vs Rc, 智能指针, 引用计数, 堆分配

actionbook avataractionbook
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m05-type-driven

m05-type-driven

1.4Kprompting-reasoning

CRITICAL: Use for type-driven design. Triggers: type state, PhantomData, newtype, marker trait, builder pattern, make invalid states unrepresentable, compile-time validation, sealed trait, ZST, 类型状态, 新类型模式, 类型驱动设计

actionbook avataractionbook
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m03-mutability

m03-mutability

1.4Kprompting-reasoning

CRITICAL: Use for mutability issues. Triggers: E0596, E0499, E0502, cannot borrow as mutable, already borrowed as immutable, mut, &mut, interior mutability, Cell, RefCell, Mutex, RwLock, 可变性, 内部可变性, 借用冲突

actionbook avataractionbook
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m09-domain

m09-domain

1.4Kprompting-reasoning

CRITICAL: Use for domain modeling. Triggers: domain model, DDD, domain-driven design, entity, value object, aggregate, repository pattern, business rules, validation, invariant, 领域模型, 领域驱动设计, 业务规则

actionbook avataractionbook
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m12-lifecycle

m12-lifecycle

1.4Kprompting-reasoning

Use when designing resource lifecycles. Keywords: RAII, Drop, resource lifecycle, connection pool, lazy initialization, connection pool design, resource cleanup patterns, cleanup, scope, OnceCell, Lazy, once_cell, OnceLock, transaction, session management, when is Drop called, cleanup on error, guard pattern, scope guard, 资源生命周期, 连接池, 惰性初始化, 资源清理, RAII 模式

actionbook avataractionbook
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public-speaking

public-speaking

1.4Kprompting-reasoning

Help users master the art of public speaking by decomposing the skill into manageable components, leveraging storytelling frameworks, and utilizing professional acting techniques for executive presence.

refoundai avatarrefoundai
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eli5

eli5

1.3Kprompting-reasoning

Explain any topic, code, concept, or error tailored to a specific audience's level of understanding. Use this skill whenever the user says 'explain like I am', 'ELI5', 'explain this to my', 'break this down for', 'dumb it down', 'simplify this for', or asks you to explain something to a specific person or audience type (e.g., 'explain this to a manager', 'how would I explain this to my mom', 'make this understandable for a 5th grader'). Also trigger when the user mentions wanting to understand something at a particular level, or asks for an explanation targeting a non-technical audience. Even partial matches like 'explain to my wife' or 'tell my boss' should trigger this skill.

dreambigou avatardreambigou
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managing-up

managing-up

1.3Kprompting-reasoning

Help users master the art of proactive communication and alignment with their managers and founders to unlock autonomy, secure resources, and drive business impact.

refoundai avatarrefoundai
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fundraising

fundraising

1.2Kprompting-reasoning

Help users navigate the end-to-end lifecycle of funding a startup, from choosing between bootstrapping and VC to closing a Series A or managing a strategic exit.

refoundai avatarrefoundai
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langsmith-online-eval-engineering

langsmith-online-eval-engineering

1.2Kprompting-reasoning

Iteratively inspect traces, interview the user, and create LangSmith online evaluators one at a time. Use specifically for creating online evaluators for use within LangSmith -- use "eval-engineering" for Harbor-style online evaluations.

langchain-ai avatarlangchain-ai
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financekit

financekit

967prompting-reasoning

Access eligible Wallet financial data using FinanceKit and FinanceKitUI. Use when querying transactions or balances, reading Apple Card, Apple Cash, Savings, or U.K. connected-account data, requesting financial-data authorization, using TransactionPicker, enabling iOS 26 background delivery, or saving and checking Wallet orders.

dpearson2699 avatardpearson2699
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prompt-optimizer

prompt-optimizer

957prompting-reasoning

Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals.

getsentry avatargetsentry
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music-caption-rewriter

music-caption-rewriter

849prompting-reasoning

Turn a brief music description and optional tagged lyrics into a professional MiniMax Music 3 structured caption with Global Metadata, Vocal Details, and a section-aware Arrangement. Use when users ask to enhance a music-generation prompt, preserve lyric-section directives, retrieve a similar style from bundled templates, fuse styles, or produce JSON or JSONL caption output.

minimax-ai avatarminimax-ai
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agentforce-observe

agentforce-observe

783prompting-reasoning

Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

forcedotcom avatarforcedotcom
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agentforce-d360-analyze

agentforce-d360-analyze

783prompting-reasoning

Data Cloud 360° view of a single Agentforce session. TRIGGER when user asks to trace, inspect, summarize, or describe a specific Agentforce session by session id (Agent Session UUID `019d…` or MessagingSession id `0Mw…`). Also triggers on session discovery — find/list/search sessions by time, agent, channel, outcome, or conversation text — when the user has no session id yet. DO NOT TRIGGER for design-time architecture questions (use agentforce-architecture-analyze instead) or for runtime perf/latency/SLO questions that require platform telemetry beyond Data Cloud.

forcedotcom avatarforcedotcom
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