gemini-deep-research
Conducts comprehensive, autonomous deep research on specific topics using Google's Gemini Deep Research API. Initiates long-running background research tasks that synthesize multiple sources into detailed reports. Use when users request deep analysis, comprehensive research, or multi-source synthesis on complex topics. Triggered by phrases like "research", "deep dive", "comprehensive analysis", or when simple fact lookups are insufficient.
What this skill does
# Gemini Deep Research Perform autonomous deep research using the `gemini-deep-research` CLI tool, which leverages Google's Gemini Deep Research API to synthesize information from multiple sources. ## Usage Execute research with the `gemini-deep-research` command: ```bash gemini-deep-research research "Your detailed research query here" ``` **Optional flags:** - `--verbose` - Show detailed progress updates - `--poll-interval N` - Check status every N seconds (default: 10) **IMPORTANT**: - This is a **long-running operation** that typically takes 3-10 minutes or longer depending on query complexity - The tool saves the report to `/tmp/gemini-deep-research/<timestamp>/research.md` - You MUST read this file after the command completes to access the research results ## Workflow 1. Run the `gemini-deep-research research` command with the user's query 2. **Wait for the command to complete** - This will take several minutes (typically 3-10+ minutes). Do not timeout or cancel the operation prematurely. 3. Read the generated markdown file at the path printed by the tool 4. Present the research findings to the user ## Commands ### research Start a new research task: ```bash gemini-deep-research research "What are the latest developments in quantum computing?" gemini-deep-research research "Impact of AI on software development" --poll-interval 15 gemini-deep-research research "Future of renewable energy" --verbose ``` ### status Check the status of a running or completed research task: ```bash gemini-deep-research status <interaction-id> ``` ### fetch-results Retrieve and save completed research results (useful if polling was interrupted): ```bash gemini-deep-research fetch-results <interaction-id> ``` ## Example User: "Research the history and architectural evolution of transformer models in NLP" ```bash gemini-deep-research research "Research the history and architectural evolution of transformer models in NLP" ``` After completion, use the Read tool to access the research report at the path printed by the tool (e.g., `/tmp/gemini-deep-research/2025-12-29T08-15-00/research.md`).
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.