2.4K skills · sorted by relevance
Rank possible gene regulatory relationships from bulk or single-cell expression data using GRNBoost2 or GENIE3. The skill guides input preparation, compatible execution, method comparison, and stability checks to help prioritize candidates for further biological investigation.
Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.
Develop AxonX plugins and manage quantitative finance research through the command line or MCP, an agent connection interface. Track data preparation, training, prediction, and historical strategy tests using execution status, logs, artifacts, and dependencies.
Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.
Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.
Store and retrieve execution output, model responses, reports, and operation receipts in YYLO Ledger. Choose storage and retention settings, attach provenance describing the source, and verify immutable evidence using record identifiers and content digests.
Define security controls for AI coding agents used by a team. Covers command and file permissions, secret scanning, isolated execution environments, code review gates, and audit trails, with configuration examples to adapt.
Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.
Design AI agents that use tools, plan tasks, retain relevant context, and recover from failures. Choose execution patterns, permission boundaries, memory handling, and runtime limits, and assess whether coordinating multiple agents is justified for the task.
Document the tools an AI agent may call, including input rules, caller permissions, execution limits, and retry conditions. Produce a tool register and verification plan for reviewing registrations or MCP routing designs.
Explore unfamiliar code with GitNexus to understand project structure and execution flows. Find related functions, inspect their callers and callees, and read implementation files to explain how a feature works, with repository and index freshness information.
Trace queued jobs from submission to their eventual effects in a CTF sandbox. Compare request-time and worker-time settings, follow retries and failed-message handling, and document the smallest sequence that explains a worker-specific result.
Analyze custom virtual machines written in JavaScript by extracting instruction tables, examining constants, and capturing runtime state. Use the findings to reconstruct how obfuscated code executes, including custom runtimes used in risk-control engines.
Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.
Investigate slow SQL using execution plans, which show how a database processes a query. Review indexes, filters, joins, and maintenance practices through PostgreSQL examples to identify bottlenecks and plan changes to queries or database structure.
Test Temporal workflows in Python with pytest. Use time skipping to avoid long waits, replace activities with controlled test versions, and replay execution histories to check that workflow changes remain consistent with previous runs.
Review source code for MCP, a protocol connecting AI applications to tools. Check authentication, input validation, unsafe execution, and other security risks, then report findings with file and line references and separate manual follow-ups.
Update GitHub Actions when workflow logs warn about deprecated runtimes. The skill guides compatible version selection, pins actions to exact commits, preserves existing workflow behavior, and checks execution results and outputs after the upgrade.
Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.
Turn a vague idea into a written specification through focused questions. The skill scores clarity across goals, constraints, and success criteria, explores hidden assumptions, and requires explicit approval before moving from planning to execution.
Turns vague development requests into reviewed plans through planning, architecture, and quality-review agents. It compares alternatives, defines testable acceptance criteria, and records decisions, stopping before implementation unless execution has been explicitly approved.
Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.
Review trading rules across China A-shares, Hong Kong stocks, US stocks, and crypto markets. Use comparison tables and checklists to identify execution limits, short-selling constraints, trading hours, fees, and tax considerations when designing historical trading simulations.
Detect attacks that misuse legitimate Windows tools by examining command arguments, parent and child processes, and network connections. Create Sysmon logging configurations and Sigma detection rules, analyze endpoint logs, and review application controls while accounting for legitimate usage.