This skill should be used when the user asks to "test the harness", "run integration tests", "validate features with real API", "test with real model calls", "run agent loop tests", "verify end-to-end", or needs to verify OpenHarness features on a real codebase with actual LLM calls.
This skill should be used when the user asks to "test a saas cross-repo feature", "deploy a feature branch to staging", "test SDK against OH Cloud branch", "e2e test a cloud workspace feature", "test secrets saas inheritance", or when changes span the SDK and OpenHands enterprise and need end-to-end validation against a staging deployment.
Create a temporary real project and prove a prove_it feature works (or doesn't) end-to-end. Builds a disposable git repo, writes a focused config, runs real dispatches through the installed or local prove_it, and produces a human-readable session transcript. Use when you need to prove a feature, reproduce a bug, or validate a fix against a real project — not just unit tests. --- # Prove a feature works (or doesn't) Build a throwaway project and exercise a prove_it feature through the real dispatcher pipeline. The output is a human-readable transcript the user can read to confirm the system works end-to-end. ## What "prove" means — read this first **Proving a feature means watching the feature do its actual job, not just watching the dispatcher accept a config and return a decision.** If the feature is a reviewer that detects dead code, you must: 1. Create a project that **contains dead code** → run the reviewer → see it **catch** the dead code 2. Create a project that **has no dead code** → run the reviewer → see it **pass clean** If the feature is a task that validates API design, you must: 1. Write an API file with **real design violations** → see the task **reject** it 2. Write a clean API file → see the task **approve** it If the feature is a when-condition gate, you must: 1. Run with the condition **unmet** → see the task **get skipped** 2. Run with the condition **met** → see the task **actually execute and produce its real output**
Structures AI and ML product decisions including model selection, data requirements, evaluation frameworks, and responsible AI considerations. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Triggers on "AI product", "LLM feature", "AI canvas", "build with AI", "AI integration", "AI-powered", "machine learning feature".
Interactive interview to fill artifacts directory. Walks through domain, features, infrastructure, decisions, and unknowns. Use at project start or when adding features.
Process multiple features or tasks in parallel. Batch execution of PDCA commands across features.
Create a new feature module by duplicating the canonical `tasks` module template. Use when adding a new module to the application, scaffolding a new domain area from scratch, or generating the boilerplate for a new feature.
Create a new feature module by duplicating the canonical `tasks` module template. Use when adding a new module to the application, scaffolding a new domain area from scratch, or generating the boilerplate for a new feature.
Plan and execute feature requests, bug fixes, and improvements using the agent harness. Auto-detect when the user shares feedback, bug reports, or feature requests and enter triage mode automatically. Create per-ticket plans with acceptance criteria, then use the executor + evaluator pattern to implement and verify each task.
Use when adding a new feature, skill, tool, or MCP server to the Valor system. Triggered by 'add a feature', 'create a new tool', 'extend the system', or 'how do I add...'.
Skill do Product Owner para especificação de features. Use quando precisar definir requisitos de negócio, escrever user stories, critérios de aceitação, priorização de backlog, ou qualquer documento de especificação de produto. Trigger em: "nova feature", "especificação", "user story", "requisito", "backlog", "PO", "definir escopo", "critério de aceitação", "MVP", "roadmap". --- # Product Owner - Especificação de Features O PO é o guardião do valor de negócio. Toda feature nova começa aqui. ## Governanca Global Esta skill segue `GLOBAL.md`, `policies/execution.md`, `policies/handoffs.md`, `policies/token-efficiency.md` e `policies/evals.md`. Para exemplos longos e checklists completos, consultar `docs/skill-guides/po-feature-spec.md` apenas quando necessario. ## Quando Usar - definir feature nova, escopo e prioridade - transformar necessidade de negocio em criterios testaveis ## Quando Nao Usar - para decidir implementacao tecnica detalhada - para substituir UI/UX, Backend, QA ou Reviewer ## Entradas Esperadas - objetivo de negocio - restricoes e dependencias conhecidas - contexto do usuario ou da operacao ## Saidas Esperadas - spec curta e acionavel - criterios de aceitacao testaveis - handoff claro para UI/UX e pipeline seguinte ## Responsabilidades 1. Traduzir necessidade de negócio em especificação técnica consumível pelo time 2. Definir prioridade e impacto 3. Escrever critérios de aceitação claros e testáveis 4. Validar que a entrega final atende o esperado ## Estrutura Minima da Feature Spec Toda nova feature deve cobrir, no minimo: - resumo do problema e da solucao proposta - user stories com criterios de aceitacao testaveis - regras de negocio e dependencias - escopo `IN` e `OUT` - prioridade e metricas de sucesso Para spec completa e exemplos extensos, consultar `docs/skill-guides/po-feature-spec.md`. ## Critérios de Aceitação - Boas Práticas Critérios de aceitação devem ser: - **Específicos**: sem ambiguidade - **Mensuráveis**: pode ser verificado como ve
Generate a Product Requirements Document (PRD) for a new feature. Use when planning a feature, starting a new project, or when asked to create a PRD. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out.
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:
