Professional writing assistant for drafting and polishing many kinds of text. Trigger when users need writing help, copy editing, or content rewrites.
Discovers architecture from codebases and authors Sruja DSL (repo.sruja). Use when discovering architecture, generating or refactoring repo.sruja, validating architecture against code, maintaining architecture docs, or when the user mentions architecture-as-code, C4, or .sruja files.
cross-surface agent contracts, evaluate routing behavior, tighten invocation
This skill should be used when the user asks to "write a PR/FAQ", "prfaq", "working backwards", "product discovery", "evaluate a product idea", "press release FAQ", "test product value", "revise prfaq", "update prfaq", "add research to prfaq", "add FAQs", "run a meeting", "review meeting", "hive meeting", "autonomous meeting", "consensus meeting", "stress test my prfaq", "go/no-go decision", "should we build this", "vote on prfaq", or wants to use the Amazon Working Backwards process to evaluate whether a product or feature is worth building. --- # Working Backwards: PR/FAQ ## Purpose Guide the user through the Amazon Working Backwards process to produce a professional PR/FAQ document. The output is a LaTeX file that compiles to a polished PDF suitable for executive review and product decision-making. The process forces clarity about customer value, surfaces risks early, and creates a shared artifact for go/no-go decisions. ## When to Use - Evaluating whether a new product or feature is worth building - Forcing specificity on a vague product idea - Preparing a product pitch for leadership review - Testing whether a team truly understands the customer problem - Structuring a go/no-go decision with an auditable artifact ## Revise Mode Before starting the full workflow, check if a `prfaq.tex` file already exists in the project root (or the path the user specifies). If it does, enter **revise mode** instead of starting from scratch. 1. **Read the existing document.** Parse the `.tex` file to understand what's already written — the press release, FAQs, and risk assessment. 2. **Ask what to revise.** Present the user with the sections found and ask what they want to improve. Common revision goals: - **Refine the product** — sharpen the problem statement, solution, or differentiation based on new thinking - **Incorporate research** — thread new primary data (customer interviews, market analysis, survey results) into existing sections. Run Phase 0 research discovery to find
Spawns AI coding agents in isolated git worktrees. Use when the user asks to spawn or launch an agent, delegate a task to a separate agent, work in a separate worktree, or parallelize development across features.
Agile Sprint Master for regular user check-ins and sprint management. Use this for /agile, /sprint, starting a sprint, checkpoint requests, or any time you are running structured iterative development. Triggers on "start sprint", "checkpoint", "sprint status", "run task", or whenever you need to manage layered build progress across Skeleton → Muscles → Skin.
Install and configure the memory-decay plugin for OpenClaw. Use when the user says "install memory-decay", "setup memory", "메모리 설치", "memory-decay 설치", or wants to add human-like memory with decay to their OpenClaw agent.
Use when a GitHub Issue with domain knowledge (gotcha, benchmark, forcing question, scoring calibration) needs to be integrated into a skill's references/ files. Reads the Issue, converts to proper format, checks for contradictions, creates a PR. Run as: /contribute-review #123 or /contribute-review scan.
Use when user says 'superflow', 'суперфлоу', or asks for full dev workflow. Four phases: (0) project onboarding & CLAUDE.md bootstrap, (1) collaborative Product Discovery with multi-expert brainstorming and git workflow selection, (2) fully autonomous execution with selected PR/branch strategy, git worktrees, dual-model reviews, max parallelism, and verification discipline, (3) merge with documentation update.
Resolve specific ESLint errors and warnings that appear in this project. Use when fixing lint failures, ESLint reported issues, or autofix conflicts (e.g. no-void, canonical/export-specifier-newline vs prettier, no-shadow trailing underscores, sonarjs/deprecation, you-dont-need-lodash-underscore, testing-library/prefer-screen-queries, testing-library/await-async-events, jest-dom/prefer-*).
>-
DEVORQ-DDD v1.0.0 — Skill de exploração de domínio para devorq_v3. Guia descoberta do modelo mental ANTES de escrever SPEC.md. Gera domain-model.json com entidades, contextos delimitados e invariantes.
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:
