Total 54,069 skills, AI & Machine Learning has 8990 skills
Showing 12 of 8990 skills
Comprehensive guide to Harper's Model Context Protocol (MCP) interface, covering server setup, client connection, automatic and custom tools, prompts, resources, rate limiting, durable quotas, and the security model. Triggers on tasks involving MCP servers on Harper, AI-client integration, and exposing Harper data or behavior to LLM agents.
(NS) Generate or refresh lean architecture-rules.md for AI agents — stack, layout, patterns, constraints. Use when the user asks for architecture rules, .nextstage-harness/rules/architecture-rules.md, agent conventions, or "document how this repo works for the AI". Do NOT use for business specs (ns-harness-codebase-reverse-spec), brownfield maps (ns-harness-bootstrap-brownfield), or one-off Cursor rules without a full architecture pass.
(NS) Orchestrate partitioned version implementation slice-by-slice — one subagent per slice, commit, auto-advance until done or stop. Use when version-roadmap.md has pending slices and the user asks to orchestrate/execute all slices. Do NOT use for non-partitioned versions, ad-hoc coding (ns-code-coder), or partitioning itself (ns-sdd-version-partitioner).
Advanced and operational chat.agent capabilities for Trigger.dev, loaded on demand. Load this when working on the raw Sessions primitive (sessions / SessionHandle), a custom chat transport or the realtime wire protocol, durable sub-agents (AgentChat, chat.stream.writer), human-in-the-loop, steering, actions, background injection (chat.defer / chat.inject), fast starts (preload, Head Start via @trigger.dev/sdk/chat-server), context resilience (compaction, recovery boot, OOM, large payloads), chat.local run-scoped state, offline testing with mockChatAgent, or prerelease/version upgrades. For the everyday chat.agent({...}) definition and the useTriggerChatTransport happy path, use the trigger-authoring-chat-agent skill instead.
Use when a developer wants to iterate on ONE specific Agent Observability / LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as /agent-observability-replay-trace <trace-id> [changes to test]. Signals: "replay this trace"; "iterate on a trace"; "this trace's output is wrong, fix it and re-run"; "re-run trace <id> with <change>"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace / LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser "Replay" button (that's agent-observability-replay-experiment), building an experiment from a dataset/CSV, writing evaluators, root-causing failed traces, or RUM/HTTP session replay.
(NS) Ad-hoc coding worker — bug fixes, small refactors, scripts, migrations — without full SDD. Entry priority 5: use for "just implement this", "quick fix", or concrete coding without execution-handoff (also C2 under ns-code-autonomous). Do NOT use for GitLab ISSUE_URL (ns-execution-gitlab-issue), multi-day/version scope (ns-spec-driven), diagnosis-only (ns-code-investigator), or when execution-handoff.md exists. Do NOT generate requirements/tasks/handoff.
Analyzes observability signals from customer GenAI applications with DQL. Reads OpenTelemetry GenAI spans and LLM evaluation bizevents. Use for: golden signals (traffic, errors, latency, saturation); LLM signals (model, provider, tokens); cost/token analytics, usage attribution, and prompt caching; agent signals (tool calls, steps, failures, loop detection, Smartscape topology); conversation/session analytics; guardrails (blocked/truncated responses); and evaluation signals (quality, pass/fail). Trigger: "LLM latency", "token usage by model", "cost by model and provider", "cost per conversation", "who is driving token spend", "do I have prompt caching", "failing agent tool calls", "find runaway agents", "responses truncated or blocked", "failed evaluations", "am I hitting rate limits", "token throughput / TPM", "provider throttling or 429s". Do NOT use for: Davis CoPilot/MCP telemetry (dt-platform), generic service metrics (dt-obs-services), logs (dt-obs-logs), or non-GenAI tracing (dt-obs-tracing).
An executable Skill for developing, maintaining, distributing, and validating DeepSeek Harness (DSH) plugins. Covers host/client form judgment, bundle/profile contracts, Service and function plugins, tools, HTTP, persistence, slots, Conversation Nodes, client-side building, HMR, GitHub installation, and real combination verification.
Author and run a durable AI chat agent with chat.agent from @trigger.dev/sdk/ai: the per-turn run loop, why you MUST spread ...chat.toStreamTextOptions() first, returning a StreamTextResult vs calling chat.pipe(), the two server actions (chat.createStartSessionAction + auth.createPublicToken), and wiring useChat to useTriggerChatTransport. Load this when building, modifying, or debugging a chat backend (the agent task or its lifecycle hooks) or its React transport, when declaring typed tools or custom data parts, or when migrating a plain AI SDK streamText route to chat.agent.
Plan, art-direct, generate, edit, and quality-check tactile minimal-zine bitmap imagery from almost any subject: landscapes, portraits, objects, products, architecture, interiors, existing photographs, moods, article ideas, cultural essays, data, reviews, and social-media card series. Use for 小红书图片, Instagram carousels, poetic posters, editorial covers, photo restyling, visual essays, evidence-aware explainers, or any request for aged paper, photocopy, risograph, letterpress, sparse type, and restrained single-ink character. Routes the subject before choosing composition, preserves identity and factual content when editing, saves reproducible prompts, invokes the runtime-native image generator, and audits the real raster output. Do not use for glossy commercial ads, deterministic UI/vector assets, or exact diagram rendering.
Art-direct and generate one high-craft minimal-zine bitmap from a landscape, portrait, person, object, product, food, plant, building, interior, photograph, mood, sentence, or article concept. Use when the user wants a standalone poetic poster, editorial cover, visual concept, aesthetic photo upgrade, photo-to-zine restyle, or a subject-aware minimal-zine image without the overhead of a social carousel. Chooses adaptive composition and a physically coherent print process, preserves subject identity, saves the final prompt, invokes the runtime-native image generator, and audits the raster result.
AE/TE/ThinkingEngine/ThinkingAI ae-cli manual for AI Agent Team tasks: managing teams (list, create, update, delete, AI-generate, templates) and executing TeamRuns (start, chat, cancel, reply, result, artifacts). Use when the user asks to find a team, run a team task, check run status, retrieve results or artifacts, or set up multi-agent workflows. Must use ae-cli, read the matching references/<command>.md before composing commands, and never guess team IDs, run IDs, config structures, or parameter formats.