4,247 artifacts
Every listing is pinned to a commit and evaluated before it goes public.
Scrape the Hacker News front page (titles, points, comment counts).
Clear the freeze boundary set by /freeze, allowing edits to all directories again. (gstack)
DuckDB-powered skills for Claude Code: read any data file, attach and query DuckDB databases, search DuckDB/DuckLake docs, search past session logs, and install/update DuckDB extensions.
Keep gbrain current with this repo's code and refresh agent search guidance in CLAUDE.md. Wraps the gstack-gbrain-sync orchestrator with state (gstack)
Turn vague intent into a precise, executable spec in five phases. (gstack)
MiniMax AI skills library for frontend, fullstack, Android, iOS, shader, GIF sticker, document, presentation, spreadsheet, and multimodal media workflows
Codify the most recent successful /scrape flow into a permanent browser-skill on disk. (gstack)
Set up gbrain for this coding agent: install the CLI, initialize a local PGLite or Supabase brain, register MCP, capture per-remote trust policy. (gstack)
Official Claude plugin for Dart and Flutter that installs Flutter/Dart Skills and Dart MCP server for building natively compiled, visually stunning applications for mobile, web, desktop, and embedded devices from a single codebase
Import cookies from your real Chromium browser into the headless browse session. (gstack)
Systematically QA test a web application and fix bugs found. (gstack)
Verify Single Step Instrumentation (SSI) is working end-to-end on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use after enable-ssi has run.
Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). (gstack)
Designer's eye plan review — interactive, like CEO and Eng review. (gstack)
Diagnose and fix Single Step Instrumentation (SSI) issues on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are already configured but traces are missing or instrumentation is not working.
Pair a remote AI agent with your browser. (gstack)
Launch GStack Browser — AI-controlled Chromium with the sidebar extension baked in.
Generate a live Single Step Instrumentation (SSI) onboarding confirmation report — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed successfully.
Turn any markdown file into a publication-quality PDF. (gstack)
Regenerate the iOS debug bridge against the latest upstream gstack templates. (gstack)
Enable Single Step Instrumentation (SSI) on Kubernetes — automatically instruments applications for APM without code changes. Only use if the Datadog Agent is already running on the cluster — if not, use agent-install first.
Visual design audit for iOS apps on real hardware. (gstack)
Install the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically instruments applications for APM without code changes. Only use if no Datadog Agent is deployed on the cluster yet.
Load when investigating a failing PR CI pipeline or checking PR health. Attributes each CI failure as flaky, infra, or regression, proposes a targeted action, and reports code coverage and quality/security status.
Crypto wallet operations via the awal CLI — sign in, check balances, send USDC/ETH/POL/SOL, trade tokens, fund the wallet, and use the x402 payment protocol to discover paid services, pay for API calls, monetize an API, or query onchain data. Use whenever the user mentions signing in, login, authentication, wallet status, balance, address, sending money, paying someone, transferring tokens, ENS names, swapping/trading/converting tokens, funding/topping up/onramp, USDC, ETH, POL, SOL, the x402 bazaar, paid APIs, monetizing an endpoint, or querying onchain data on Base.
Load when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.
Answer "who did what" security questions from Audit Trail — deletions, config changes, login activity, permission changes, actions from a specific user or IP.
USE FOR web search. Returns ranked results with snippets, URLs, thumbnails. Supports freshness filters, SafeSearch, Goggles for custom ranking, pagination. Primary search endpoint.
Investigate a potentially compromised Datadog API key — timeline of actions, geo/IP breakdown, endpoints called, anomaly flags, and remediation steps.
Investigate a Datadog product usage or cost spike by correlating Usage Metering data (when/what spiked) with Audit Trail config changes (who changed what in the preceding window).
USE FOR video search. Returns videos with title, URL, thumbnail, duration, view count, creator. Supports freshness filters, SafeSearch, pagination.
Generate auditor-ready compliance evidence from Datadog Audit Trail for SOC 2 and PCI DSS. Maps framework controls to specific query patterns and produces formatted output.
Audit what the Bits AI assistant (MCP server) has done in your Datadog org — tool calls by user, resources accessed, and anomaly flags for AI governance.
USE FOR query autocomplete/suggestions. Fast (<100ms). Returns suggested queries as user types. Supports rich suggestions with entity info. Typo-resilient.
Guides developers building Datadog Apps with TypeScript, React, the @datadog/apps scaffolder, and @datadog/vite-plugin. Use when a user wants to scaffold, run, debug, upgrade, build, upload, publish, upload without publishing (draft upload), add an upload-no-publish script, set up CI/CD, use OAuth or API/application key auth, trigger/poll Workflow Automation, choose DDSQL or Action Catalog for backend data access, or query app datastores with DDSQL, including backend function troubleshooting.
Create and manage APM service remapping rules — rewrite service names at ingestion time to collapse noisy inferred entities, clean up auto-generated names, handle org renames, or normalize naming conventions. Use for any request involving service renaming, service mapping, inferred service cleanup, peer.service normalization, or collapsing fragmented service names.
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says "generate Python experiment", "write an SDK experiment", "create a ddtrace experiment", "Python notebook experiment", "use the Agent Observability SDK", or has ddtrace installed and wants idiomatic SDK code.
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.
End-to-end Agent Observability pipeline for an instrumented mlapp — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each. Pure orchestration over the agent-observability sub-skills (agent-observability-session-classify, agent-observability-trace-rca, agent-observability-eval-bootstrap, agent-observability-experiment-py-bootstrap, agent-observability-experiment-analyzer). Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", "onboard me to datasets and experiments", "walk me through experiments", "I have an mlapp, now what", "Agent Observability onboarding", "guided experiment setup", "from traces to experiments", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. Stop early with --stop-after <phase> to short-circuit at evaluators or dataset, or resume mid-flow with --start-at <phase>.
Bootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.
Datadog CLI (Rust). OAuth2 auth with token refresh.
Recommends the right Datadog products for a codebase and/or a stated goal — grounded in a tech-stack→product map and a use-case→product map built from Datadog product capabilities and common technology patterns. Recommendation only; no setup instructions. Use when a user asks which Datadog products fit their app, what to monitor, or which products serve a goal like security, cost, or LLM observability.
Monitor management - list, search, file-based create, and alerting best practices.
Log management - search, archives, metrics, and cost control.
Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
Audit Trail investigations - who changed what, key compromise, cost spike root cause, compliance evidence (SOC 2/PCI), and AI activity auditing.
APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.