提示词与推理

提示词工程、角色设定和推理模式

270 个 Skills 可用

Skills 列表

santa-method

santa-method

231Kprompting-reasoning

多智能体对抗性验证与收敛循环。两个独立的审查智能体必须同时通过,输出才能发布。

affaan-m avataraffaan-m
获取
council

council

231Kprompting-reasoning

召集四声议会,处理模糊决策、权衡取舍和放行/叫停判断。当存在多条合理路径且需要在选择前进行结构化辩论时使用。

affaan-m avataraffaan-m
获取
ck

ck

231Kprompting-reasoning

Claude Code 的持久化项目记忆。自动加载项目上下文,通过 Git 活动追踪会话,并写入原生记忆。命令运行确定性 Node.js 脚本——行为在不同模型版本间保持一致。

affaan-m avataraffaan-m
获取
foundation-models-on-device

foundation-models-on-device

231Kprompting-reasoning

Apple FoundationModels 框架,用于设备端 LLM — 文本生成、使用 @Generable 的引导生成、工具调用以及 iOS 26+ 中的快照流式传输。

affaan-m avataraffaan-m
获取
agent-harness-construction

agent-harness-construction

230Kprompting-reasoning

设计和优化AI智能体的动作空间、工具定义和观察格式化,以提高任务完成率。

affaan-m avataraffaan-m
获取
cost-aware-llm-pipeline

cost-aware-llm-pipeline

230Kprompting-reasoning

LLM API 使用成本优化模式——根据任务复杂度进行模型路由、预算跟踪、重试逻辑和提示缓存。

affaan-m avataraffaan-m
获取
security-scan

security-scan

230Kprompting-reasoning

使用 AgentShield 扫描您的 Claude Code 配置(.claude/ 目录),检查安全漏洞、配置错误和注入风险。检测 CLAUDE.md、settings.json、MCP 服务器、hooks 和代理定义。

affaan-m avataraffaan-m
获取
eval-harness

eval-harness

230Kprompting-reasoning

Claude Code 会话的正式评估框架,实现评估驱动开发(EDD)原则

affaan-m avataraffaan-m
获取
skill-creator

skill-creator

151Kprompting-reasoning

创建新 Skill、修改并优化现有 Skill,以及评估 Skill 的运行表现。当用户需要从头创建 Skill、编辑或优化现有 Skill、运行 eval 测试 Skill、通过方差分析对 Skill 性能进行基准测试,或者优化 Skill 的 description 以提高触发准确率时使用。

anthropics avataranthropics
获取
caveman-explore

caveman-explore

98Kprompting-reasoning

Read-only repository explorer. Use PROACTIVELY for cold-start exploration, broad cross-file localization, or when a direct search has failed and you need to find where something lives. Skip it when the issue already names the exact file or symbol, or a previous turn already returned usable file:line evidence. Returns only compact path:line citations; its reads and greps never enter the main conversation.

juliusbrussee avatarjuliusbrussee
获取
caveman-optimize

caveman-optimize

98Kprompting-reasoning

Turn Caveman's exact report-only repository observations into an operator-chosen optimization candidate with a paired baseline/candidate evaluation. Use when the user asks to inspect an optimization observation, evaluate a candidate change, or act on the current Caveman optimization report. Require a logged-in Caveman CLI connection and explicit approval; never infer money or actuation from a profile.

juliusbrussee avatarjuliusbrussee
获取
investigate-first

investigate-first

98Kprompting-reasoning

Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.

juliusbrussee avatarjuliusbrussee
获取
verify-and-stop

verify-and-stop

98Kprompting-reasoning

Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.

juliusbrussee avatarjuliusbrussee
获取
surgical-patch

surgical-patch

98Kprompting-reasoning

Fix bugs and small behavior changes at the narrowest responsible layer. Use when regression proof, preserved surrounding behavior, and task-relevant tests matter.

juliusbrussee avatarjuliusbrussee
获取
safe-refactor

safe-refactor

98Kprompting-reasoning

Restructure code while preserving behavior. Use for extraction, consolidation, ownership moves, or cleanup where verification must bracket structural edits.

juliusbrussee avatarjuliusbrussee
获取
caveman-learn

caveman-learn

98Kprompting-reasoning

Close the loop on a Caveman learn report — review the ranked token sinks and apply cost-lowering fixes (trim config, offload recurring context to cavemem) with per-edit consent. Use when the user runs "caveman learn", asks to lower their agent's token cost, wants to trim a heavy CLAUDE.md, or wants to offload context they re-paste every session into cavemem.

juliusbrussee avatarjuliusbrussee
获取
lean-build

lean-build

98Kprompting-reasoning

Build feature work with high overbuilding risk. Use for new behavior, product slices, or integrations where repository reuse, strict scope, and an explicit stop condition matter.

juliusbrussee avatarjuliusbrussee
获取
caveman-evidence-review

caveman-evidence-review

98Kprompting-reasoning

Review Caveman Cloud evidence read-only: costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use when the user asks what Caveman found, where LLM spend goes, why cost or quality changed, which workflows need attention, or asks for a trace or analytics review. Prefer Caveman MCP tools; fall back to CLI JSON.

juliusbrussee avatarjuliusbrussee
获取
caveman-discover

caveman-discover

98Kprompting-reasoning

Find every LLM workflow in the current repository and label it, so Caveman Cloud groups spend by what the code actually does (support-reply, nightly-digest) instead of one anonymous bucket. Use when the user pastes the Caveman discovery prompt, says "discover workflows", or asks to break LLM spend down by workflow. The repo should already route through the Caveman gateway (the caveman-setup skill does that part).

juliusbrussee avatarjuliusbrussee
获取
caveman-manage

caveman-manage

98Kprompting-reasoning

Inspect Caveman Cloud's eval-gated experiment lifecycle and block unsafe execution. Use when the user asks to start, approve, cancel, promote, or roll back a Caveman experiment, or asks what action an experiment's evidence supports. Read evidence first; do not execute lifecycle mutations until server-authoritative transition and evidence gates ship.

juliusbrussee avatarjuliusbrussee
获取
migration

migration

98Kprompting-reasoning

Implement reversible compatibility-safe transitions. Use for schema, data, API, protocol, configuration, or dependency migrations requiring rollback and preservation proof.

juliusbrussee avatarjuliusbrussee
获取
ponytail-gain

ponytail-gain

85Kprompting-reasoning

以紧凑记分牌形式展示 ponytail 的实测影响:更少代码、更低成本、更高速度,数据来自基准测试中位数。一次性显示,非持久模式,也非每个仓库的数值。触发方式:/ponytail-gain、“ponytail gain”、“what does ponytail save”、“show ponytail impact”、“ponytail scoreboard”。

dietrichgebert avatardietrichgebert
获取
ponytail-debt

ponytail-debt

84Kprompting-reasoning

将代码库中所有 `ponytail:` 注释收集到债务台账中,使 ponytail 留下的有意捷径和延期得到跟踪,而不是腐烂成“以后意味着永远”。当用户说“ponytail debt”、“/ponytail-debt”、“what did ponytail defer”、“list the shortcuts”、“ponytail ledger”或“what did we mark to do later”时使用。一次性报告,不改变任何内容。

dietrichgebert avatardietrichgebert
获取
ponytail-review

ponytail-review

83Kprompting-reasoning

专注于过度工程的代码审查。找出可以删除的内容:重新发明的标准库、不必要的依赖、投机性抽象、死掉的灵活性。每个发现一行:位置、要删除的内容、替代方案。当用户说“审查过度工程”、“我们可以删除什么”、“这是否过度工程”、“简化审查”或调用 /ponytail-review 时使用。补充以正确性为重点的审查,此审查仅寻找复杂性。

dietrichgebert avatardietrichgebert
获取