Non-interactive hunk-level git staging, unstaging, discarding, undoing, fixup, amend, squash, commit splitting, and commit reordering. Use when selectively staging, unstaging, discarding, reverting, squashing, splitting, or reordering individual diff hunks by ID instead of interactively.
Structured root cause analysis for arriving at a concrete action. Use when something went wrong, a pattern keeps recurring, behavior has drifted, or you catch yourself resolving to "do better" / "remember to X" without a concrete artifact. Five-whys forces behavioral resolutions into file edits, config changes, memory block updates, or scheduled jobs — the action item must produce a diff someone else can verify. Do not use for simple debugging with an obvious cause.
System architecture and design thinking — requirements analysis, component design, data modeling, scaling strategy, and trade-off analysis. Use when: "design this system", "what's the architecture for", "trade-offs for X", "how should we architect", "system design for", "API design", "data model for", "service boundaries", "architecture doc", "create an ADR". When the design thinking is done, this skill hands off to /ship:write-docs to write the design document. Note: this is NOT for visual design (use /ship:visual-design) or implementation planning (use /ship:design). --- # Architectural Design Think through system design decisions rigorously before writing them down. This skill is about the **thinking** — requirements, components, trade-offs, boundaries. When the design is ready, you MUST invoke `Skill("write-docs")` to write the design document — do not write the doc inline. ## Scale to Complexity Not every decision needs all 5 phases. Match the depth to the decision: - **Small** (single component, clear constraints) — Phase 1 briefly, Phase 2, Phase 5. Skip deep dive and scaling. - **Medium** (multi-component, some unknowns) — All 5 phases, but keep each concise. - **Large** (new system, significant unknowns, cross-team) — All 5 phases in full depth, with diagrams and explicit load estimates. ## Red Flag **Never:** - Skip requirements gathering and jump straight to a solution - Design without understanding existing constraints (tech stack, team, timeline) - Omit trade-off analysis — every decision has alternatives that were rejected for a reason - Skip the Boundaries section — it's the core anti-drift mechanism - Propose a design without verifying assumptions against the actual codebase - Conflate "what we want" with "what exists" — be explicit about the gap ## Phase 1: Requirements Gathering Before designing anything, understand what you're solving. ### Functional Requirements - What must the system do? List concrete capabilities. - What are the input/output co
Esperto di HTML-JS Card per Home Assistant Lovelace. Usa quando devi creare card con HTML, CSS e JavaScript inline: variabili hass/entities/card, hass-update event, callService, shadow DOM.
Critically analyze content, claims, or arguments with rigorous evaluation.
自動標註尺寸：使用 Ray-Casting 或 BoundingBox 方法，在平面視圖中自動建立房間、走廊、MEP 設備的尺寸標註。觸發條件：使用者提到標註、尺寸、dimension、annotation、淨寬、淨高、measurement、自動標註、批次標註。工具：create_dimension_by_ray、create_dimension_by_bounding_box、get_room_info。
Use this skill when working with Signboard boards through the local MCP server (listing views/lists/cards, reading cards, and safely creating/updating/moving cards, boards, or board settings).
Analyze, re-engineer, or bootstrap projects to align with AI-first design principles. Use when asked to review, audit, improve, 'ai-firstify', or start a new project. Performs deep analysis across 7 dimensions, actively restructures existing projects, or guides new project setup through discovery questions. Based on the 9 design principles and 7 design patterns from the TechWolf AI-First Bootcamp.
Rebuild eforge from source and restart the daemon. Use during development after making code changes so the MCP tools pick up the latest build.
Publish the current Claude Code analysis to the civicaitools.org evidence registry as a cryptographically signed, timestamped, Rekor-logged evidence package. Invoke when the user has just completed a civic-data analysis (typically using the Socrata and/or Data Commons MCP tools) and says something like "publish this as evidence", "sign this analysis", "publish to civicaitools.org", or "make this a verifiable package.
Use when setting up a fresh HAINDY installation, configuring API credentials for the first time, migrating from a legacy .env file, or verifying the effective configuration.
Show the plugin source — name, version, and repo URL. Works from CLI or messaging. Triggers on /about, /version, /agent:about, /agent:version, "qué versión", "what version", "about the plugin", "about clawcode".
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:
