提示词与推理

提示词工程、角色设定和推理模式

124 个 Skills 可用

Skills 列表

oracle

oracle

385Kprompting-reasoning

Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.

steipete avatarsteipete
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sag

sag

385Kprompting-reasoning

ElevenLabs text-to-speech with mac-style say UX.

steipete avatarsteipete
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session-logs

session-logs

384Kprompting-reasoning

Search and analyze your own session logs (older/parent conversations) using jq.

steipete avatarsteipete
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cost-tracking

cost-tracking

238Kprompting-reasoning

Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log. Use when the user asks about costs, spending, usage, tokens, budgets, or cost breakdowns by model, session, or date.

affaan-m avataraffaan-m
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hermes-imports

hermes-imports

234Kprompting-reasoning

Convert local Hermes operator workflows into sanitized ECC skills and release-pack artifacts. Use when preparing a Hermes workflow for public ECC reuse without leaking private workspace state, credentials, or local-only paths.

affaan-m avataraffaan-m
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evm-token-decimals

evm-token-decimals

232Kprompting-reasoning

Prevent silent decimal mismatch bugs across EVM chains. Covers runtime decimal lookup, chain-aware caching, bridged-token precision drift, and safe normalization for bots, dashboards, and DeFi tools.

affaan-m avataraffaan-m
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openclaw-persona-forge

openclaw-persona-forge

232Kprompting-reasoning

为 OpenClaw AI Agent 锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡, 输出身份定位、灵魂描述(SOUL.md)、角色化底线规则、名字和头像生图提示词。 如当前环境提供已审核的生图 skill,可自动生成统一风格头像图片。 当用户需要创建、设计或定制 OpenClaw 龙虾灵魂时使用。 不适用于:微调已有 SOUL.md、非 OpenClaw 平台的角色设计、纯工具型无性格 Agent。 触发词:龙虾灵魂、虾魂、OpenClaw 灵魂、养虾灵魂、龙虾角色、龙虾定位、 龙虾剧本杀角色、龙虾游戏角色、龙虾 NPC、龙虾性格、龙虾背景故事、 lobster soul、lobster character、抽卡、随机龙虾、龙虾 SOUL、gacha。

affaan-m avataraffaan-m
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santa-method

santa-method

231Kprompting-reasoning

Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.

affaan-m avataraffaan-m
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council

council

231Kprompting-reasoning

Convene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.

affaan-m avataraffaan-m
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ck

ck

231Kprompting-reasoning

Persistent per-project memory for Claude Code. Auto-loads project context on session start, tracks sessions with git activity, and writes to native memory. Commands run deterministic Node.js scripts — behavior is consistent across model versions.

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

foundation-models-on-device

231Kprompting-reasoning

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

affaan-m avataraffaan-m
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agent-harness-construction

agent-harness-construction

230Kprompting-reasoning

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.

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

cost-aware-llm-pipeline

230Kprompting-reasoning

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.

affaan-m avataraffaan-m
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security-scan

security-scan

230Kprompting-reasoning

Scan your Claude Code configuration (.claude/ directory) for security vulnerabilities, misconfigurations, and injection risks using AgentShield. Checks CLAUDE.md, settings.json, MCP servers, hooks, and agent definitions.

affaan-m avataraffaan-m
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eval-harness

eval-harness

230Kprompting-reasoning

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

affaan-m avataraffaan-m
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skill-creator

skill-creator

151Kprompting-reasoning

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

anthropics avataranthropics
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ponytail-gain

ponytail-gain

85Kprompting-reasoning

Show ponytail's measured impact as a compact scoreboard: less code, less cost, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", "ponytail scoreboard".

dietrichgebert avatardietrichgebert
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ponytail-debt

ponytail-debt

84Kprompting-reasoning

Harvest every `ponytail:` comment in the codebase into a debt ledger, so the deliberate shortcuts and deferrals ponytail leaves behind get tracked instead of rotting into "later means never". Use when the user says "ponytail debt", "/ponytail-debt", "what did ponytail defer", "list the shortcuts", "ponytail ledger", or "what did we mark to do later". One-shot report, changes nothing.

dietrichgebert avatardietrichgebert
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ponytail-review

ponytail-review

83Kprompting-reasoning

Code review focused exclusively on over-engineering. Finds what to delete: reinvented standard library, unneeded dependencies, speculative abstractions, dead flexibility. One line per finding: location, what to cut, what replaces it. Use when the user says "review for over-engineering", "what can we delete", "is this over-engineered", "simplify review", or invokes /ponytail-review. Complements correctness-focused review, this one only hunts complexity.

dietrichgebert avatardietrichgebert
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ponytail-audit

ponytail-audit

83Kprompting-reasoning

Whole-repo audit for over-engineering. Like ponytail-review, but scans the entire codebase instead of a diff: a ranked list of what to delete, simplify, or replace with stdlib/native equivalents. Use when the user says "audit this codebase", "audit for over-engineering", "what can I delete from this repo", "find bloat", "ponytail-audit", or "/ponytail-audit". One-shot report, does not apply fixes.

dietrichgebert avatardietrichgebert
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interview-me

interview-me

78Kprompting-reasoning

Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

addyosmani avataraddyosmani
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cavecrew

cavecrew

75Kprompting-reasoning

将任务委托给穴居人风格子代理的决策指南。告诉主线程何时生成 `cavecrew-investigator`(定位代码)、`cavecrew-builder`(1-2 文件编辑)或 `cavecrew-reviewer`(差异审查),而不是内联执行或使用普通的 `Explore`。子代理输出经过穴居人压缩,因此注入回主上下文的工具结果约小 60%——在长时间会话中,主上下文能维持更久。 触发词:“委托给子代理”、“使用 cavecrew”、“生成 investigator/builder/reviewer”、“节省上下文”、“压缩代理输出”。

juliusbrussee avatarjuliusbrussee
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caveman-stats

caveman-stats

75Kprompting-reasoning

显示当前会话的实际令牌使用量和预估节省金额。直接从 Claude Code 会话日志读取——无 AI 估算。通过 /caveman-stats 触发。输出由 mode-tracker 钩子注入;模型本身不计算数字。

juliusbrussee avatarjuliusbrussee
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caveman-commit

caveman-commit

73Kprompting-reasoning

超精简提交信息生成器。去除提交信息中的噪音,同时保留意图和理由。采用 Conventional Commits 格式。主题行 ≤50 字符,仅在“为什么”不明显时添加正文。当用户说“写提交”、“提交信息”、“生成提交”、“/commit”或调用 /caveman-commit 时使用。暂存更改时自动触发。

juliusbrussee avatarjuliusbrussee
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