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Try a sample that invokes another skill from within a skill. Running its command triggers example-skill internally, providing a concrete test case for skill chaining in the bundled skills registry.
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Identify Open Redirect vulnerabilities where applications route users to arbitrary external URLs based on unvalidated input. Prove maximum impact by chaining Open Redirects with OAuth token theft, SSRF, or Phishing, graduating this typically low-risk finding into a high-severity report.
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Connect agent tasks into a sequence where each step receives the previous output. Use custom prompts or predefined workflows to analyze code, plan refactoring, generate tests, optimize performance, or transform data through multiple stages.
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Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.
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Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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Research-to-implement pipeline chaining 5 MCP tools with graceful degradation
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Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
中文简介正在准备中。当前显示原文说明。
Research-to-implement pipeline chaining 5 MCP tools with graceful degradation
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Use when you need to apply Java exception handling best practices — including using specific exception types, managing resources with try-with-resources, securing exception messages, preserving error context via exception chaining, validating inputs early with fail-fast principles, handling thread interruption correctly, documenting exceptions with @throws, enforcing logging policy, translating exceptions at API boundaries, managing retries and idempotency, enforcing timeouts, attaching suppressed exceptions, and propagating failures in async/reactive code. This should trigger for requests such as Exception handling; Use try-with-resources in Java code; Create exception chaining in Java code; Apply fail-fast validation in Java code; Review Java exception taxonomy and propagation. Part of Plinth Toolkit
中文简介正在准备中。当前显示原文说明。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
中文简介正在准备中。当前显示原文说明。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
中文简介正在准备中。当前显示原文说明。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
中文简介正在准备中。当前显示原文说明。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
中文简介正在准备中。当前显示原文说明。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
中文简介正在准备中。当前显示原文说明。
Design and execute DAG-based automation workflows using the NV oOS Pro Workflow Builder — a ReactFlow visual builder for chaining tool calls, agent runs, and actions into repeatable pipelines. Covers the visual builder UI, 9 preset categories, 10 node types, Kahn's algorithm execution engine, scheduling via Pro Schedule Manager, template variable syntax, and best practices. Use when designing workflows, debugging DAG execution failures, choosing between a workflow schedule and raw tool chaining, or building CRM/PM automation pipelines with toolkit_cpt nodes.
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Design and execute DAG-based automation workflows using the NV oOS Pro Workflow Builder — a ReactFlow visual builder for chaining tool calls, agent runs, and actions into repeatable pipelines. Covers the visual builder UI, 10 preset categories (incl. AI Fashion Production, v1.1.92+), 10 node types, Kahn's algorithm execution engine, scheduling via Pro Schedule Manager, template variable syntax, and best practices. Use when designing workflows, debugging DAG execution failures, choosing between a workflow schedule and raw tool chaining, or building CRM/PM automation pipelines with toolkit_cpt nodes.
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Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware Poisson GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.
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为音乐视频和短视频规划色彩、光线、运动与踩点剪辑，依据angelcore和hyperpop等风格统一生成素材与编辑方向。
Delegate code lookup, small edits, and diff reviews to three specialized subagents. Their compact, structured results help conserve the main agent's context, with guidance on when to delegate and when to keep work in the main conversation.
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Connect agent tasks in sequence, passing each result into the next step. Build custom workflows or use predefined pipelines for code analysis, refactoring, testing, and optimization, with execution progress, timing, and consolidated results.
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Design connections between APIs and services, including webhooks, which notify other systems when events occur. Define event formats, retries, duplicate prevention, and recovery steps for workflows such as order processing and data pipelines.
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Audit and repair existing Cypress tests that fail intermittently. The skill identifies fixed delays, fragile command chains, shared state, and version compatibility issues, then guides targeted changes and repeated test runs to check the results.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Use Bash on macOS or Linux for file searches, log inspection, text processing, and process checks. Apply command chaining, pipes that pass text between commands, environment variables, and error handling when writing scripts.
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