Hunt threat-intelligence indicators of compromise (IoCs) across Dynatrace logs and spans and produce a 0-100 threat-exposure score. Extracts and normalizes IoCs — IPs, Domains (hostnames included), URLs, Emails, CVEs, File hashes (md5/sha1/sha256), MITRE TTPs — from unstructured reports, advisories, advisory URLs, pasted text, or STIX, then hunts them in fetch logs and fetch spans. Trigger: hunt these IoCs, am I exposed to this threat, check these indicators in my logs and traces, threat exposure report, extract IoCs from this advisory URL, search these hashes/domains/IPs in my environment. Routes CVE-to-vulnerability, IP/Domain/URL/MITRE-to-detection legs to dt-sec-insights. Do NOT use for: querying security.events directly (vulnerabilities, detections, compliance, THREAT_REPORT — use dt-sec-insights); general log queries not tied to an IoC hunt (use dt-obs-logs); general span/trace analysis (use dt-obs-tracing); explaining DQL syntax (use dt-dql-essentials).

Analyze dashboards and notebooks using Davis analyzers — anomaly detection, novelty scoring, and correlation. Use when the user references a specific Dynatrace dashboard or notebook (by URL, UUID, or name) and asks what it shows, which DQL queries it runs, whether a tile looks off, or wants to find anomalies, score novelty, or correlate its metrics. The trigger is a dashboard or notebook as the data source, not a general DQL question. This skill extracts timeseries queries efficiently without reading the full raw document JSON, then optionally runs Davis analyzers on the extracted metrics. Trigger phrases: "what's wrong on this dashboard", "analyze this notebook", "find anomalies", "novelty score", "correlate metrics", "extract DQL from dashboard", "dashboard URL", "tile", "run-analyzer", "timeseries extraction", "Davis analyzer".

Design a goal-oriented agent loop or review one for failure modes: spinning, Goodhart-gaming the verifier, or running a wrong answer to completion. Covers machine-decidable goals, loop types, plan/build/judge skeletons, and runaway prevention; mechanism wiring lives in autonomous-loops. Use when designing, writing, or checking an agent loop. 中文触发：写 loop、设计 loop、做一个 loop、检查 loop 对不对、loop 体检、loop 会不会跑飞、可判定目标、五个崩法、plan build judge。

Instrument a LaunchDarkly metric event in a codebase by adding a track() call. Use when the user wants to wire up an event, instrument an action for a metric, add tracking to a feature, or confirm that an event is flowing to LaunchDarkly.

Turn docs/product/SCENARIOS.md into testable claims for the deployed-stand suite — QA scenario design for Insight. Pick a scenario (S-1…S-10), a persona (EXEC/LEAD/IC/ADMIN), an invariant (§5 rules 1-12) or an Appendix A question (A1…G4), ground it in the seeded roster, and emit claims with an oracle, a layer and a priority that stand-api-test / stand-ui-test can implement. Use when the task is deciding WHAT to test: 'what should we test for S-9', 'design e2e scenarios from the product doc', 'which product rules are unverified', 'is the IC boundary covered', 'plan coverage for the reach matrix', 'convert the scenarios doc into tests'. Not for writing test code — that is stand-api-test and stand-ui-test; for scoping tests from a GitHub issue or an unbuilt feature use scope-feature-tests instead.

Fetch a real product's current price, stock, rating, or images from retailer pages over the Apify MCP server, and return them as typed fields rather than prose. Use when someone asks what a product costs right now, whether it is in stock, how it is rated, to compare a product across retailers, or to check a product URL they pasted. Also use when an agent would otherwise answer a product question from training data. Not for managing your own store's catalog, not for payments, and not for questions answerable without live retailer data.

Control-plane workflow for coordinating multi-agent, multi-session project work from a single Codex, GitHub Copilot, or agent-app control session. Use this skill whenever the user asks to orchestrate agents, create or steer worker sessions, run a workflow-like effort, fan out audits/research/migrations, coordinate parallel implementation streams, monitor other project sessions, or compare this control-session pattern to Claude Code dynamic workflows. This skill is especially relevant when the current session can spawn persistent project sessions and those sessions can spawn their own subagents, creating a two-level orchestration hierarchy.

Captura screenshots via Playwright MCP de URLs/elementos pra usar em posts de blog ou documentação. Lida com viewport, cookie banners, full-page vs section, scroll a âncora, formatos PNG/JPG. Compõe com skill 41 (blog-publisher). Trigger em: "tira print", "screenshot do site", "captura tela", "screenshot da página", "print da landing", "print do dashboard", "screenshot pro blog".

Use when writing or reviewing Java code in this repo that calls Deepgram Management APIs for projects, project models, API keys, members, invites, usage, and billing. Covers `client.manage().v1().*` plus related think-model discovery under `client.agent().v1().settings().think().models()`. Use `deepgram-java-voice-agent` for live agent conversations instead of admin APIs. Triggers include "management api", "list projects", "api keys", "members", "invites", "usage", "billing", and "models".

Use when writing or reviewing JavaScript/TypeScript in this repo that calls Deepgram Text Intelligence / Read (`/v1/read`) for sentiment, summarization, topic detection, and intent recognition on text input. Covers `client.read.v1.text.analyze(...)` with `body: { text }` or `body: { url }`. Use `deepgram-js-audio-intelligence` when the source is audio instead of text. Triggers include "read API", "text intelligence", "analyze text", "sentiment", "summarize text", "topics", "intents", and "read.v1".

Shadcn/ui theming and component customization — CSS variables, OKLCH colors, dark mode, variants, wrappers. Load for any ticket involving colors, theme, UI layout, or component styling.

Anthony Fu's opinionated tooling and conventions for JavaScript/TypeScript projects. Use when setting up new projects, configuring ESLint/Prettier alternatives, monorepos, library publishing, or when the user mentions Anthony Fu's preferences.

Authors, reviews, installs, and debugs GitHub Agentic Workflows in repositories, including workflow markdown, frontmatter, gh aw compile and run flows, safe outputs, security guardrails, and operational patterns. Use when creating or maintaining GH-AW automation. Don't use for standard deterministic GitHub Actions YAML, generic CI pipelines, or non-GitHub automation systems.

Installs, configures, audits, and operates Agent Package Manager (APM) in repositories. Use when initializing apm.yml, installing or updating packages, validating manifests, managing lockfiles, compiling agent context, browsing MCP servers, setting up runtimes, or packaging resolved context for CI and team distribution. Don't use for writing a single skill by hand, generic package managers like npm or pip, or non-APM agent configuration systems.

Authors, reviews, installs, and debugs GitHub Agentic Workflows in repositories, including workflow markdown, frontmatter, gh aw compile and run flows, safe outputs, security guardrails, and operational patterns. Use when creating or maintaining GH-AW automation. Don't use for standard deterministic GitHub Actions YAML, generic CI pipelines, or non-GitHub automation systems.

Authors, reviews, installs, and debugs GitHub Agentic Workflows in repositories, including workflow markdown, frontmatter, gh aw compile and run flows, safe outputs, security guardrails, and operational patterns. Use when creating or maintaining GH-AW automation. Don't use for standard deterministic GitHub Actions YAML, generic CI pipelines, or non-GitHub automation systems.

Installs, configures, audits, and operates Agent Package Manager (APM) in repositories. Use when initializing apm.yml, installing or updating packages, validating manifests, managing lockfiles, compiling agent context, browsing MCP servers, setting up runtimes, or packaging resolved context for CI and team distribution. Don't use for writing a single skill by hand, generic package managers like npm or pip, or non-APM agent configuration systems.

Use when the agent needs to apply a tool to a target object in ALFWorld to accomplish an interaction such as cleaning, heating, cooling, or examining. This skill handles locating both the tool and target object, then executing the correct environment action (e.g., `clean`, `heat`, `cool`, `use`) to progress the task.

LP tokenized stocks onchain — range-LP Coinbase tokenized equities (NVDA, AAPL, GOOGL, META) and AERO/USDC on Aerodrome Slipstream (Base) for trading-fee + AERO emission yield. Use when the user wants to LP stocks or Aerodrome pools on Base, open/recenter/exit a Slipstream position, check pool status, NAV, or yields, get a portfolio overview ("how are my LP positions doing?") with P&L and projected APR, run a manage pass, or set up scheduled/price-triggered LP automations in the Bankr console. Auto-routes every position to the higher-yielding side — staked (AERO emissions) vs unstaked (trading fees) — at entry and re-checks on every manage pass. Bundled node scripts do the chain reads, gate checks, and calldata; writes go via the Bankr arbitrary-transaction flow. NOT for perps, spot trading, or Uniswap.

One token recommendation and one prediction-market pick per run — with falsifiable thesis, entry, sizing guidance, and an explicit kill criterion. Skip branch fires when no candidate has both a named/dated catalyst and asymmetric upside. The discipline is the skip branch — empirically the highest-EV output on flat days is no pick. Triggers: "what should I trade today", "give me a token pick", "prediction-market rec", "is there an asymmetric setup".