Results for “flow”

Use this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.

The English summary is being prepared. Showing the original description.

Scheduled Paths in record-triggered Flow: delayed execution, time-offset from field, batch size tuning, monitoring Paused Flow Interviews, async limits. NOT for picking between Scheduled Path, Wait and Scheduled Flow, or migrating time-based workflow rules — use flow/flow-time-based-patterns. NOT for a flow that runs on a recurring schedule — use flow/scheduled-flows.

The English summary is being prepared. Showing the original description.

Monitor Power Automate flow health, track failure rates, and inventory tenant assets using the FlowStudio MCP cached store. The live API only returns top-level run status. Store tools surface aggregated stats, per-run failure details with remediation hints, maker activity, and Power Apps inventory — all from a fast cache with no rate-limit pressure on the PA API. Load this skill when asked to: check flow health, find failing flows, get failure rates, review error trends, list all flows with monitoring enabled, check who built a flow, find inactive makers, inventory Power Apps, see environment or connection counts, get a flow summary, or any tenant-wide health overview. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app

The English summary is being prepared. Showing the original description.

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

Check a DeerFlow installation from code updates through deployment and basic service checks. Supports local and Docker setups, verifies frontend pages and API health, and produces a report with results and troubleshooting recommendations.

• Checking DeerFlow after installation
• Verifying startup after a code update
• Checking a Docker deployment

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Generate images, video, and music through ComfyUI in OpenMontage. Helps adapt community workflows, select the final output, identify missing models, and choose workflows suited to available GPU memory while recording settings for reproducibility.

• Generating images, video, or music in OpenMontage
• Importing a community ComfyUI workflow
• Choosing workflows for limited GPU memory

Write Workflow scripts that divide research, reviews, or migrations among multiple agents. This reference explains parallel tasks, independent checks, result synthesis, and resuming interrupted runs, with examples for organizing larger jobs into manageable stages.

• Divide a code review among independent agents
• Organize research from multiple search angles
• Plan file migrations with isolated edits

Inspect FCS files from flow cytometry, a method for measuring cell properties, using FlowIO. Extract measurement data into NumPy arrays, review channel metadata, export tables, and create FCS 3.1 files for downstream work.

• Inspect measurement settings and channel labels in FCS files
• Extract measurement data into NumPy arrays or CSV tables
• Read legacy FCS files containing multiple datasets

Review GitHub Actions workflows for unsafe input handling, excessive token permissions, exposed secrets, and changeable action references. Get findings grouped by issue type, with severity ratings, exact YAML locations, and concrete fixes to review.

• Reviewing CI workflows for security issues
• Checking privileged triggers that run fork code
• Restricting GITHUB_TOKEN permissions

Investigate failed workflows and tasks in Managed Service for Apache Airflow. The skill compares remote logs, task states, and source code to explain failures, show supporting evidence, and recommend specific code or configuration fixes.

• Investigating a failed DAG run
• Diagnosing task timeouts and queue delays
• Checking service errors against DAG code

Plan data ingestion and transformation workflows with Apache Airflow, Spark, and dbt. Get guidance on processing changed records, validating data quality, and rerunning failed or historical jobs without creating duplicate results.

• Designing data ingestion and transformation workflows
• Processing only new or changed records
• Adding data quality checks between processing stages

Manage machine learning experiments with MLflow by recording settings, metrics, files, and trained models. Covers experiment comparison, model version management, reproducible project configurations, and deployment workflows across local and cloud environments.

• Recording experiment settings and evaluation results
• Comparing model performance across experiments
• Managing model versions for production use

Analyze Android packages by decompiling them into readable code. Identify app structure, list API endpoints—the addresses used for network requests—and trace how screen actions reach those requests, producing source files and structured documentation.

• Investigating the internal structure of Android packages
• Listing API endpoints used by an app
• Tracing screen actions through to network requests

Trace system behavior across code, APIs, authentication, data, and networks through an interactive workflow. Store steps, branches, and findings in SQLite, then export diagrams and documentation for security audits, debugging, architecture reviews, or incident analysis.

• Documenting authentication flows for security audits
• Tracing branches while debugging system behavior
• Reviewing how system components interact

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

Implement core TypeScript modules for claude-flow v3 using domain-driven design, which organizes code around distinct responsibilities. Build task and session domains, connect storage and services, and add unit and integration tests.

• Build the core structure for claude-flow v3
• Implement task assignment and status rules
• Connect SQLite storage through dependency injection

Use this skill when the user is measuring the host or DPU-CPU control-plane rate of a DOCA Flow pipeline with doca_flow_perf — picking a JSON policy from configs/, choosing the DPDK or DOCA backend, running the single-iteration smoke then the iterative eval loop, interpreting per-iteration CPU cycles and num_pushed / num_failed, or capturing the four-tuple (DOCA version, BlueField/firmware, JSON policy, worker/queue/burst config) that makes a Kops/sec number defensible. Trigger even when the user does not explicitly mention "doca-flow-perf" — typical implicit phrasings include "how many rules per second can my BlueField insert", "5-tuple hairpin rule rate", "Kops/sec for steering", "flow-perf number does not match release notes", "DPDK vs DOCA benchmark", or "rule-install variance too high". Refuse and route elsewhere for optimizing a live Flow app (doca-flow-tune), the DPA-offloaded path (doca-flow-dpa-perf), dataplane throughput or latency, or library-internal pipe semantics — those belong to other skills.

The English summary is being prepared. Showing the original description.

DOCA Flow packet steering on NVIDIA NICs/DPUs, linked into a C application: match/action pipes, ports, representors, forwarding, hairpin, counters. Trigger on packet steering, classifier, rule-matching, representor, hairpin or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Remote or non-C++ control over gRPC belongs to doca-flow-grpc-server. Also covers pipe validation before hardware programming, Flow/DOCA version match, and Flow API errors. First app: follow the local bundle (doca-setup, doca-programming-guide, doca-flow, doca-public-knowledge-map). Stage 1 pull nvcr.io/nvidia/doca/doca (NGC catalog tag, never invent); Stage 2 needs a real NIC. Then ls samples inside the container; never emit main.c, a Dockerfile, or hand-typed -l flags. Route software-only host pipelines, plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.

The English summary is being prepared. Showing the original description.

Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).

The English summary is being prepared. Showing the original description.

Create complete Claude Code workflow directories with curated skills. Use when user wants to (1) create a new workflow for specific use case (media creator, developer, marketer, etc.), (2) set up a Claude Code project with pre-configured skills, (3) download and organize skills from GitHub repositories, or (4) generate README.md and AGENTS.md documentation for workflows. Triggers on phrases like "create workflow", "new workflow", "set up workflow", "build a xxx-workflow".

The English summary is being prepared. Showing the original description.

Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

The English summary is being prepared. Showing the original description.