Audit a NousResearch/hermes-agent checkout or fork for Hermes-specific runtime-contract drift, command-surface splits, memory/skill/gateway health, and agent architecture risks. Uses the hermescheck Python library (hermescheck.report.v1) for structured reports with severity-ranked findings and code-first fix plans.
Cross-platform disk space management toolkit with intelligent optimization. REQUIREMENTS: Python 3.7+. UNIVERSAL COMPATIBILITY: Works with ALL AI IDEs (Cursor, Windsurf, Continue, Aider, Claude Code, etc.). PLATFORM-INDEPENDENT: Works at any location - global, project, or user level. SELF-CONTAINED: No pip install needed, includes intelligent bootstrap. KEY FEATURES: (1) PROGRESSIVE SCANNING: Quick sample (1s) + Progressive mode for large disks, (2) INTELLIGENT BOOTSTRAP: Auto-detection of skill location and auto-import of modules, (3) CROSS-PLATFORM ENCODING: Safe emoji/Unicode handling on all platforms, (4) DIAGNOSTIC TOOLS: check_skill.py for quick verification, (5) OPTIMIZED SCANNING: 3-5x faster with os.scandir(), concurrent scanning, intelligent sampling. AGENT WORKFLOW: (1) Check Python, (2) Find skill package (20+ locations auto-detected), (3) Run diagnostics, (4) Use progressive scanning for large disks. The skill package includes all optimization modules - no features are lost!
AdVooster_Electron 프로젝트(/Users/tk/AdVooster_Electron)의 Python 코드를 분석하여 viruagent-cli에 포팅할 비즈니스 로직, API 엔드포인트, 인증 흐름, 데이터 구조를 추출한다. 'AdVooster 분석', '카페 API 분석', '카페 가입 분석', 'AdVooster에서 가져와', 'advooster', '기존 코드 분석' 등을 언급하면 이 스킬을 사용할 것.
FastAPI engineering choices. To be used when asked to "create router", "create crud router", "add new endpoint" or similar in a fastapi codebase.
Build, integrate, or migrate WorkOS Widgets in modern web apps. Use this skill when implementing User Management, User Profile, Admin Portal SSO Connection, or Admin Portal Domain Verification widgets across Next.js, React Router, TanStack Router, TanStack Start, Vite, SvelteKit, Ruby, Python, Go, PHP, or Java stacks. Detect the active stack, auth/token strategy, data-layer style, and UI conventions; then implement widget integration with correct access-token flow and API calls based on the bundled Widgets OpenAPI spec.
Interactive reactive Python notebook development with marimo - best practices, UI components, MCP integration, and deployment workflows
Generate action scaffold code for an Orca machine in TypeScript, Python, or Go. Use when the user has a verified machine and wants implementation stubs for the action functions. When the machine file also contains decision tables, compiled evaluator functions and wired action stubs are included automatically.
分析 JSON Schema 生成测试数据的 Python 脚本
Designs intuitive Python library APIs following principles of simplicity, consistency, and discoverability. Handles API evolution, deprecation, breaking changes, and error handling. Use when designing new library APIs, reviewing existing APIs for improvements, or managing API versioning and deprecations.
Guides using and contributing to LLMaps: Python library for interactive web maps (MapLibre, single HTML). Use when building maps with llmaps (pip or repo), when editing the llmaps repo, or when the user mentions llmaps, MapLibre, or map generation.
每日复盘。根据 Claude Code 本地对话记录，生成结构化的每日工作复盘报告。支持当天、昨天、近 3 天、近 7 天。 当用户说"复盘"、"agent review"、"/agent-review"、"/复盘"时触发。 --- # 每日复盘 ## 启动横幅 技能启动时，**必须**在执行任何操作之前，先输出以下横幅： ``` ═══════════════════════════════════════════════════════════════ ▌ 每日复盘 ▐ 根据 Claude Code 本地对话记录，生成结构化的每日工作复盘报告 ═══════════════════════════════════════════════════════════════ 磊叔 │ 微信：AIRay1015 │ github.com/akira82-ai ─────────────────────────────────────────────────────────────── - 支持 4 种时间范围：今天 / 昨天 / 近 3 天 / 近 7 天 - 自动提取对话记录、工具调用统计、Git 提交记录 - 生成结构化报告：概要 / 工作量统计 / 成功与进展 / 困难与卡点 / AI 自评 - 报告自动保存至当前工作目录 ═══════════════════════════════════════════════════════════════ ``` ## 参数处理 如果用户没有指定时间范围，用 AskUserQuestion 询问，选项为： - 今天 - 昨天 - 近 3 天 - 近 7 天 不提供其他选项。根据用户选择，计算对应的日期范围（当天、前 1 天、前 3 天、前 7 天），时间戳使用 UTC 时区。 ## 数据提取步骤 ### 第 1 步：从 history.jsonl 获取消息列表 用 Bash 执行 Python 脚本，读取 ~/.claude/history.jsonl，按时间戳筛选指定日期范围内的所有记录。 每条记录包含：display（用户输入内容）、timestamp（Unix 毫秒）、project（项目路径）、sessionId。 统计精确的消息条数。 如果选择了多天（近 3 天、近 7 天），按天分别统计。 ### 第 2 步：获取涉及的 session 列表 从第 1 步中提取不重复的 sessionId 和对应的项目路径。 ### 时间戳格式说明（重要） 两个数据源的时间戳格式不同，脚本中**必须**统一处理： 1. `history.jsonl` 的 timestamp 字段是 **int**（Unix 毫秒），如 `1770288337219` 2. 项目 JSONL 文件的 timestamp 字段是 **ISO 8601 字符串**，如 `"2026-03-31T04:24:20.514Z"` 在脚本开头定义统一的解析函数： ```python def to_ms(ts): if isinstance(ts, (int, float)): return ts if isinstance(ts, str): dt = datetime.datetime.fromisoformat(ts.replace('Z', '+00:00')) return int(dt.timestamp() * 1000) return 0 ``` 后续所有时间戳比较和过滤都使用 `to_ms()` 转换后再比较。 ### 第 3 步：从项目 JSONL 文件中提取详细内容 使用技能自带的 `extract.py` 脚本提取数据，确保时间戳处理稳定可靠。 **调用脚本**： ```bash python ~/.claude/plugins/marketplaces/airay-skills/skills/airay-agent-review/scripts/extract.py --start_ms <start_ms> --end_ms <end_ms> ``` **脚本返回的数据结构**： ```json { "sessions": [...], "total_messages": N, "tool_calls": {"Bash": 36, "Read": 2, "Write": 2, ...}, "tool_errors": {...}, "files_touched": ["path/to/file1", "path/to/file2", ...], "projects": ["/path/to/project1", "/path/to/project2"], "user_messages":
IDA Pro reverse-engineering skill for Codex, Claude Code, and OpenCode. Use when a user needs live IDA or Hex-Rays analysis through the local ida-pro-skill CLI and installed IDA bridge, especially for instance discovery, metadata, cursor or selection context, entrypoints, functions, callers, imports, strings, xrefs, pseudocode, globals, structs, renames, comments, byte patches, function creation, or explicit IDAPython, including WSL-to-Windows IDA setups.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
