2.2K skills · sorted by relevance
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
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Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
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Route natural-language cleanup, refactor, migration, and candidate ranking goals through ATM before local analysis.
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Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).
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Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.
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Automate Cloudinary media management including folder organization, upload presets, asset lookup, transformations, and usage monitoring through natural language commands
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Automate time tracking workflows in Clockify -- create and manage time entries, workspaces, and users through natural language commands.
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Automate Capsule CRM operations -- manage contacts (parties), run structured filter queries, track tasks and projects, log entries, and handle organizations -- using natural language through the Composio MCP integration.
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Automate recruiting and hiring workflows in Ashby -- manage candidates, jobs, applications, interviews, and notes through natural language commands.
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Automate Apollo.io lead generation -- search organizations, discover contacts, enrich prospect data, manage contact stages, and build targeted outreach lists -- using natural language through the Composio MCP integration.
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Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP integration.
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Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
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Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
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Use when a manager, dispatcher, or safety director needs to have a coaching conversation with a driver — about a safety event (harsh brake, hard turn, distraction), an HOS violation, a customer complaint, a missed delivery, repeated minor incidents, or to deliver positive recognition. Covers conversation structure, language, documentation, and escalation.
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Expert debugger for Next.js + Cloudflare Workers + Supabase stacks. Systematic troubleshooting for auth, caching, workers, RLS, CORS, and build issues. Activate on: 'debug', 'not working', 'error', 'broken', '500', '401', '403', 'cache issue', 'RLS', 'CORS'. NOT for: feature development (use language skills), architecture design (use system-architect).
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Generates production-ready design tokens from natural language descriptions. Outputs Tailwind configs, CSS custom properties, and DTCG W3C tokens from 24 trends + 31 AI-ready styles with 2800+ component examples.
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Expert DevOps engineer for CI/CD, IaC, Kubernetes, and deployment automation. Activate on: CI/CD, GitHub Actions, Terraform, Docker, Kubernetes, Helm, ArgoCD, GitOps, deployment pipeline, infrastructure as code, container orchestration. NOT for: application code (use language skills), database schema (use data-pipeline-engineer), API design (use api-architect).
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Chooses and designs developer surfaces for an agentic product: SDK, CLI, MCP, GUI, API, webhooks, and pd tube-style listener/sender workflows. Use when deciding why a user belongs in an SDK versus CLI/MCP/GUI, planning Python SDK parity, or making agent invocation easy across languages. NOT for implementing the full SDK, generic API docs, or UI visual design.
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Matches natural language task descriptions to appropriate skills using semantic similarity, ranks candidates by fit and performance history, and maintains the skill catalog. Use when assigning skills to DAG nodes, searching for the right skill for a task, ranking competing skills, or browsing the skill catalog. Activate on "find skill", "match skill", "which skill", "skill for this task", "skill catalog", "rank skills", "best skill". NOT for executing DAGs (use dag-runtime), creating skills (use skill-architect), or grading skills (use skill-grader).
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Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).
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The intelligence layer of Jury-rig. Decomposes natural language tasks into proposed Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, proposes wave and revision contracts, and expands nodes only when evidence supports a revised graph. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.
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Generates comprehensive, context-aware code review checklists tailored to the specific codebase, programming language, and team standards. Analyzes PR diffs and suggests what reviewers should focus on.
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Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
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Design and create a simulation persona for testing an AI agent. Guides through use case selection, voice and language configuration, behavior prompt crafting, and interruption calibration. Use when user says "create a persona", "design a persona", "set up a test persona", "configure simulation persona", or "build a caller profile".
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