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Found 6,690 Skills
Request coding agents to review code, verify review results and fix confirmed issues
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
DPoP-signed (RFC 9449) authenticated calls to Alien-aware services. Discover any Alien-aware service's manifest at /.well-known/alien-agent-id.json, render its operations as actionable markdown, emit DPoP headers for one request, or one-shot a signed HTTP call with the agent's identity attached. Use when the user gives you a URL on an Alien-aware service (alien-api.com, alien.org, agent-sso.*), asks to call an Alien-aware endpoint, asks what an Alien-aware service can do, or mentions DPoP, agent-bound access tokens, or `cnf.jkt`.
Run `gbrain skillpack-check` to produce an agent-readable JSON health report for the gbrain install. Wraps `gbrain doctor` + `gbrain apply-migrations --list` so a host agent (your OpenClaw's morning-briefing, any OpenClaw cron) can see at a glance whether the skillpack needs attention. Use when the user asks "is gbrain healthy?", when a cron fires a morning check, or proactively when something seems off (jobs not running, brain not updating, autopilot silent).
Build AI agent UIs using the AG-UI protocol with pydantic-ai (Python backend) and CopilotKit (React frontend). Use when creating agentic chat interfaces, human-in-the-loop workflows, generative UIs with state management, tool-based rendering, shared state between frontend and backend, or predictive state updates. Covers FastAPI integration, state events (StateSnapshotEvent, StateDeltaEvent, CustomEvent), useCoAgent hooks, useCopilotAction for tool rendering, and real-time agent-frontend synchronization.
Create or refresh hierarchical AGENTS.md documentation for Claude Code, Codex/OMX, Gemini, and Antigravity/OMA projects, preserving manual notes while excluding runtime state such as root .omc, .omx, .survey, .codex, and generated build folders.
Design, audit, and refactor production-safe agentic harnesses with provider-neutral best practices for tools, permissions, planning, context, and observability.
Build AI-driven security operations automation with ASP's agent-centric SIRP, modules, and playbooks
Enable AI agents to safely make real-world merchant purchases using Snaplii's tokenized gift card payment layer with up to 10% savings.
Use OpenClaw MemX for long-term agent memory with self-learning, relationship graphs, and automatic maintenance
Helps users discover and install capabilities from the open agent skills ecosystem. Use when users ask "how do I do X" for specialized tasks, request "find a skill for X", want to extend agent capabilities, or need help with specific domains (testing, design, deployment, etc.).
Validar prompts dirigidos a agentes de IA (Claude Code, Cursor, Copilot, etc.) contra reglas de redacción efectiva. Calcular un porcentaje de efectividad del prompt y devolver sugerencias de mejora concretas, más una propuesta de prompt reescrito. Cubre verbos no imperativos, lenguaje conversacional, acciones vagas, términos subjetivos, alcance difuso, prohibiciones implícitas, intenciones múltiples y nombres genéricos. Las reglas de detalle técnico (alcance, nombres exactos) se aplican solo a prompts de implementación; en prompts funcionales (user stories, descripciones de comportamiento) se marcan N/A. Usar siempre que el usuario pida validar, revisar, auditar, mejorar, corregir o "pulir" un prompt antes de enviarlo a un agente, o cuando pegue un prompt y pida feedback sobre cómo está redactado.