Results for “sources”

Create, update, and review Terraform provider documentation for Terraform Registry using HashiCorp-recommended patterns, tfplugindocs templates, and schema descriptions. Use when adding or changing provider configuration, resources, data sources, ephemeral resources, list resources, functions, or guides; when validating generated docs; and when troubleshooting missing or incorrect Registry documentation.

Look up current research and scientific information across three backends: fast web search, deep multi-source synthesis, and scholarly paper searches. Automatically routes each query to the best backend and saves every result for reproducible citation.

AL App/Test project layout diagnosis for Business Central. Use when the main app compiles test sources, AL0297 appears on test object IDs, Library Assert or Library - Sales is missing, test/app.json is not recognized, or a multi-root workspace with app and test projects needs diagnosis.

Comprehensive guide for working with HashiCorp Terraform Stacks. Use when creating, modifying, or validating Terraform Stack configurations (.tfcomponent.hcl, .tfdeploy.hcl files), working with stack components and deployments from local modules, public registry, or private registry sources, managing multi-region or multi-environment infrastructure, or troubleshooting Terraform Stacks syntax and structure.

Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.

AI-optimized web search using Tavily Search API. Use when you need comprehensive web research, current events lookup, domain-specific search, or AI-generated answer summaries. Tavily is optimized for LLM consumption with clean structured results, answer generation, and raw content extraction. Best for research tasks, news queries, fact-checking, and gathering authoritative sources.

Scaffold and implement Model Context Protocol (MCP) servers that expose external services, APIs, and data sources as typed tools and resources for LLM agents. Use when the user says "build an MCP server", "give Claude access to X", "create an MCP tool", "expose my API to an agent", or "AI agent integration".

AI-optimized web search using Tavily Search API. Use when you need comprehensive web research, current events lookup, domain-specific search, or AI-generated answer summaries. Tavily is optimized for LLM consumption with clean structured results, answer generation, and raw content extraction. Best for research tasks, news queries, fact-checking, and gathering authoritative sources.

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.

This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.

Expert knowledge for Azure Artifacts development including best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing feeds, upstream sources, views/promotion, retention, GitHub Actions CI/CD, or npm/.npmrc config, and other Azure Artifacts related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines).

Use for writing, reviewing, debugging, migrating, or testing Python application configuration built with pydantic-settings. Trigger for BaseSettings, SettingsConfigDict, environment names, dotenv, secrets directories, nested settings, CLI sources, custom source precedence, and secret-safe startup configuration. Do not use for ordinary Pydantic model validation, direct os.environ access in a small script, or external secret manager administration.

Build and maintain a personal knowledge base (wiki) using LLMs. Instead of RAG-style retrieval, the LLM incrementally compiles, cross-references, and maintains a persistent structured wiki from raw sources. Use when user wants to create a knowledge base, build a personal wiki, organize research notes, ingest documents into a structured wiki, or maintain a living knowledge repository.

Mine proof-heavy papers, notes, PDFs, Markdown, or LaTeX sources into source-indexed proof-material files. Use when the user wants to extract technically nontrivial lemmas, estimates, definitions, dependencies, reductions, constructions, or proof strategies, preserve paper locations and stable material IDs, run DeepSeek screening/backtests, and update the proof-material index rather than writing directly to proof-usage.

Chrome-dependent academic literature scouting and idea planning. Use when the user gives a research idea, topic, question, or draft direction and wants Codex to browse Google, Google Scholar, and academic sources through the Codex Chrome plugin, identify relevant papers, explain what can be borrowed from them, and write a planNN.md before collecting PDFs or BibTeX.

Serverless computing patterns. AWS Lambda, Azure Functions, GCP Cloud Functions. Deployment frameworks: Serverless Framework, AWS SAM, SST. Cold starts, event sources, and production optimization. USE WHEN: user mentions "serverless", "Lambda", "cloud function", "Azure Functions", "SAM", "SST", "Serverless Framework", "cold start", "FaaS" DO NOT USE FOR: container orchestration - use `kubernetes` or `docker`; always-on servers - use backend framework skills

Query Google Data Commons for public statistical data aggregated from global sources, resolving geographic entities and pulling time-series statistics. Use when working with demographic data, economic indicators, health statistics, or environmental data — population counts, GDP figures, unemployment rates, disease prevalence — or when resolving places to DCIDs and exploring relationships between statistical entities. Part of the AlterLab Academic Skills suite.

Use this skill when the user requests to generate, create, write, or draft a newsletter, email digest, weekly roundup, industry briefing, or curated content summary. Supports topic-based research, content curation from multiple sources, and professional formatting for email or web distribution. Trigger on requests like "create a newsletter about X", "write a weekly digest", "generate a tech roundup", or "curate news about Y".

Queries the FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources, covering GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, fetching U.S. or international economic indicators by FRED series ID, and academic research requiring historical economic time series. Part of the AlterLab Academic Skills suite.

Systematic methodology for auditing the Hermes Skills Hub (and external sources) to identify gaps, overlaps, redundancies, and conflicts when building a skill library for any project. Covers browsing, targeted searching, cross-referencing with web/GitHub, comparing candidates against existing inventory, verifying install safety, and planning installation order. Use when starting a new project and assembling tools, or when evaluating whether to add external skills beyond what's currently installed.

Layer alternative channels / scrapers / data sources with explicit fall-through, where each rung covers a different failure mode. Use when interacting with the world (sending a notification, fetching from a third party, detecting a state change, authenticating) and the single canonical path could fail in known ways. Distinct from retrying — retries handle transient failures; fallback chains handle modal ones (channel down, API gone, cookie expired). Three layers tops; the terminal rung is loud.

Before any cold touch on a target account, find and rank every warm way in. Inventories your team's collective network, customers, past colleagues, investors, and community overlap, scores each path, picks one connector, and writes the forwardable intro request. Built for B2B sellers, customizable to your network sources and CRM. Trigger on "warm intro path", "who can intro me", "bridge before cold", "warm path into this account", "find a referral path".

Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)

Operate, configure, provision, secure, and troubleshoot Grafana OSS, Enterprise, and Cloud, including dashboards, folders, data sources, annotations, alert rules, contact points, notification policies, silences, mute timings, service accounts, RBAC, plugins, APIs, and as-code workflows. Use for Grafana product work and Grafana-side integrations. Do not use for defining SLOs or paging policy, operating Prometheus/Loki/Tempo/InfluxDB backends, generic Docker/Kubernetes/Terraform/reverse-proxy work, plugin development, or authorized security assessments; use the corresponding specialist skill.

Expert knowledge for Azure Sentinel development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Sentinel data connectors, analytics rules, playbooks/Logic Apps, ASIM schemas, or SAP/AWS/GCP sources, and other Azure Sentinel related development tasks. Not for Azure Defender For Cloud (use azure-defender-for-cloud), Azure Security (use azure-security), Azure Monitor (use azure-monitor), Azure Network Watcher (use azure-network-watcher).

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `smart-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Intelligent hybrid search combining web and academic sources via AIsa Smart Search endpoint. Best when you need both web and scholarly results. Use when: the user needs web search, research, source discovery, or content extraction.

Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to a molecular-ML skill (e.g. deepchem). Part of the AlterLab Academic Skills suite.

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

Strategic search planning for agent-driven research. Generates structured search-term matrices with tiered fallback strategies, engine-specific operators, and grading criteria before executing any searches. Use this skill whenever research requires more than a single search query — comparing technologies, verifying claims across sources, surveying a landscape, investigating a multi-faceted question, or building evidence for a decision. Do NOT use for quick factual lookups, fetching a single known URL, or questions answerable from a single source. Covers tech, academic, regulatory, and general domains. Think of it as "research planning" — the matrix is the plan, execution comes after.

Build and maintain a persistent markdown wiki that an LLM updates on the user's behalf, usually inside an Obsidian vault or git-tracked notes repo. Use when raw sources such as web articles, papers, meeting notes, transcripts, screenshots, or past analyses need to be turned into an interlinked knowledge base with immutable source files, LLM-written wiki pages, `index.md`, `log.md`, schema rules in `AGENTS.md` or `CLAUDE.md`, source summaries, query notes, and recurring lint passes. Triggers on: llm-wiki, personal wiki, obsidian wiki, research vault, knowledge base, source ingest, persistent notes, wiki maintenance, source summaries, query filing.

Cultural adaptation for translated content. Run AFTER blog-translate completes. Adjusts brand examples, CTAs, legal references, and formality for the target market (German, French, Japanese, Spanish, etc.). Deep cultural adaptation of translated blog posts. Goes beyond translation to swap brand examples, adapt CTAs, substitute legal references, localize statistic sources where possible, and adjust formality (Sie/du, tu/vous, formal/informal). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets, plus a custom-locale template. Makes content feel locally authored, not translated. Use when user says "localize blog", "blog localize", "cultural adaptation", "adapt for Germany", "adapt for France", "lokalisieren", "localiser", "adaptar".

Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

Decide, before an expensive workflow runs, whether an artifact needs a full rebuild, a partial refresh, a skip, or a recycle. Inspects how old the artifact is and what actually changed underneath it, then returns a section-by-section plan with the token and cost savings. The cheap gate that keeps expensive generation from running on data that did not move. Built for anyone running repeatable AI workflows, customizable to your artifacts and your data sources. Trigger on "should we regenerate this", "refresh the report", "is this still fresh", "what needs updating", "when was this last run", "do we have a fresh brief", or any artifact-refresh question.

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," or "find out why customers churn/convert/buy." Use for both analyzing existing research assets AND gathering new research from online sources. For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.