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Found 13,656 Skills
Use when deciding where knowledge goes or reading/writing durable docs: AGENTS.md, .context/, KB, docs/, and work directories.
Graph engineering for parallel task execution: convert a task, PRD, SPEC, or issue set into a dependency graph (DAG), layer it into supersteps, then implement each independent node concurrently with subagents — each node runs /goal → /review-it → /ship-it in an isolated git worktree, with a fan-in barrier between waves. Triggers on: graph, graph engineering, build a graph, task graph, dependency graph, DAG, parallel implement, 并发实现, 并行实现, 任务图, 把任务变成图, fan-out fan-in, superstep, dynamic workflow.
Network flow analysis in Dynatrace across three sources: OneAgent flows (host/process/pod-to-peer connections in the `default_network_flows` Grail bucket), NetFlow/IPFIX/sFlow (via an OpenTelemetry Collector), and cloud flow logs (AWS VPC / Transit Gateway; Azure and GCP planned). Use to analyze traffic between entities, find top talkers by bandwidth, map communication dependencies, investigate connection health (resets, timeouts, retransmissions, RTT), and resolve peers to monitored entities. Routes each question to the right source; source-specific DQL lives in the reference files. Trigger: "network flows", "top talkers", "traffic between hosts", "connection resets", "TCP retransmissions", "RTT", "pod connections", "network dependencies", "NetFlow", "IPFIX", "sFlow", "VPC flow logs", "cloud network traffic". Do NOT use for host NIC throughput or packet drops (use dt-obs-hosts), service request rate or latency (use dt-obs-services), or synthetic/uptime monitoring (use dt-obs-ext-monitors).
Read Cursor / Claude Code / Codex agent logs and call write_knowledge for each durable learning found. Default auto-writes then reports what was cached and expected token savings (analytics-style: rediscovery/generation cost reused on each future read), and opens the HTML report. Dry-run lists the exact write_knowledge payloads (title, content, repo, branch) without writing. Use when the user says "Please bootstrap my knowledge with Dosu", "bootstrap agent knowledge", "/bootstrap-agent-knowledge", "log to dosu knowledge", "mine my sessions into Dosu", "backfill branch notes from my agent logs", "save my agent logs to Dosu", or wants a one-shot pass over local histories.
MagicPay handles first-time setup, exact balance checks, crypto transfers and reconciliation, plus approved login, identity, checkout, donation, subscription, and payment workflows. It applies when a task needs protected Memory, payment operations, or approval; an open-ended purchase or booking without a checkout URL routes through MagicSearch before browser navigation or generic web search. For browser work, it uses the current agent's native page-control when available. MagicBrowse is fallback page-control.
Emit and maintain a dagr run file — a live, contract-valid JSON description of recursive projects, tasks, attempts, gates, evidence, policies, events, and operator-message resolutions that `dagr view` renders as a DAG. Use when orchestrating agents or tracking multi-step work that a dagr pane should display.
Coordinate Codex sub-agents for substantial multi-step work. Requires Codex collaboration tools and the listed Luna, Terra, and Sol model families. Use proactively when a request involves two or more separable workstreams; repository exploration plus implementation or verification; production diagnosis across logs, code, and live systems; PR or release review; multi-source analytics or research; or long-running tests, transfers, workflows, and monitors that should not block user communication. Assign bounded leaf work to Luna and collaborative or high-stakes work to Terra or Sol. Skip only trivial single-step or tightly sequential tasks.
Sharpen the prompt before you send it, whether the one-shot instruction you're about to hand a coding agent or the system prompt your application ships. Use when the user says "optimize this prompt", "what's wrong with this prompt", "write or rewrite the system prompt my app ships", or "/promptkit".
Use this skill when users work within a local, single-user, compound-growth Markdown personal wiki (Karpathy's 'LLM owns wiki' model) — covering: ingesting raw/ materials (papers/ clippings/ external repo symlinks), querying and cross-page synthesis/ contradiction reconciliation, archiving conclusions back to the wiki, linting orphan/ outdated summaries, and format upgrades. Three core rules: raw/ controlled by users + wiki/ owned by LLM + AGENTS.md as the single source of truth. Triggers: "Ingest this paper into the wiki" / "Does the wiki have/ summarize content about X?" / "There's a contradiction between A and B in the wiki" / "Save the previous conclusion to the wiki" / "Check the wiki for orphan pages/ outdated summaries" / "Upgrade the wiki/ check wiki version" / "Include repo X in the wiki". Always use this skill whenever users need to digest materials/ retrieve wiki deposits/ archive new conclusions — even if they don't mention the skill name. Not applicable to: cloud/ team wikis (Notion/ Confluence/ Outline, etc.); wiki metadata configuration, wiki creation/deletion, session start/stop (use a single llmw command directly). **Trigger only when the cwd is the wiki root (containing `wiki_metadata.toml` + AGENTS.md skeleton)**; cross-wiki/ workspace operations go to `yzr-llm-workspace-management`; not applicable to other directories.
Build AI features with the first-party Laravel AI SDK (Laravel 13+); agents, embeddings, images, audio, and tool calling with provider-agnostic APIs
Set up or extend Polygres from either a short request such as "Help me set up Polygres" or a detailed ingestion, memory, graph, embedding, synchronization, or retrieval specification. Also use for questions such as "What can I do with Polygres?" by scanning the accessible current workspace and project read-only and giving a personalized recommendation without changing anything. Ask one short direction question first when a setup request identifies neither a source nor an outcome; otherwise inspect the user's accessible data and application, resolve only critical unknowns, design the smallest useful schema and retrieval setup, generate source-specific ingestion and retrieval code, configure the selected project after one consolidated approval, verify a small vertical slice, and optionally connect capture and recall to the user's agent. Use whenever the user intends to make their data usable through Polygres, even if they do not say "data pipeline."
Use for any codebase exploration or understanding task — reads historical intent from Entire checkpoints instead of guessing. Orchestrates other Entire skills to give the agent provenance-backed answers about code.