'pipeline' 검색 결과

Integrate a model or pipeline into diffusers, from file structure and weight conversion to tests and documentation. Verify numerical agreement with the original implementation, with modular pipelines as the preferred starting point for new integrations.

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• Adding a new model and pipeline to diffusers
• Converting a pipeline to modular format
• Converting weights for a new version of a supported model

Review a sales team's pipeline by representative and deal stage. The skill summarizes progress against targets, highlights deals that could affect the quarter, and suggests concrete manager actions and coaching questions using CRM data or an uploaded export.

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• Prepare for a team pipeline review
• Compare each representative's forecast with their target
• Identify major deals and risks affecting the quarter

Run a five-stage finishing workflow for a drafted LaTeX paper: polish, revise, adapt its structure to a target journal, polish again, and verify references. Receive updated files, stage backups, a reference audit spreadsheet, and a final report.

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• Running a complete finishing workflow on a first draft
• Adapting a paper's structure to a target journal
• Auditing references after manuscript revisions

[omh] Data pipeline work -- an ETL or streaming job, a backfill or replay, duplicate events, a schema change downstream, a lineage question, a data-quality regression: make every rerun idempotent, bound every replay, and gate each load on observed checks. Use when the user says: data-pipelines, data pipeline, data pipelines, etl, elt, etl pipeline, etl job, etl backfill.

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Configures CI/CD pipelines using AWS CodePipeline, CodeBuild, CodeDeploy, CodeConnections, and CodeArtifact. Covers CodePipeline V2 (triggers, variables, execution modes, cross-account), buildspec.yml (caching, VPC, Docker), CodeDeploy strategies (blue/green, canary, linear), CodeArtifact (private package registries, auth tokens, cross-account), and source connections (GitHub, GitLab, Bitbucket). Applies when CodePipeline, CodeBuild, CodeDeploy, CodeConnections, CodeArtifact, buildspec.yml, appspec.yml, or CI/CD pipeline orchestration is referenced. Does NOT cover: ECS Fargate services or task definitions (use aws-containers), CDK Pipelines or cdk deploy (use aws-cdk), sam deploy (use aws-serverless), Amplify deployments (use aws-amplify), or GitHub Actions/GitLab CI.

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Force-links a development or target environment to a Power Platform Pipelines host, overriding any existing association with a previous host. Use when creating a deploymentenvironments record fails with "this environment is already associated with another pipelines host", or when intentionally migrating an environment from one host to another (e.g., Platform Host → Custom Host, or between two Custom Hosts). Calls the documented `ManageEnvironmentStamp` Dataverse action (the API behind the "Force Link" button in the Deployment Pipeline Configuration app). DESTRUCTIVE to the previous host: makers lose access to any pipelines in that host that used this environment. Reversible by running Force Link from the previous host. Use when asked to: "force link environment", "force-link to new host", "switch pipelines host", "environment already associated with another host", "take over pipelines association", "relink environment to host".

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Triggers a Power Platform Pipeline deployment run for a Power Pages solution. Selects a target stage, validates the package, optionally configures deployment settings (environment variables, connection references), then deploys and polls for completion. Use when asked to: "deploy pipeline", "run pipeline", "trigger deployment", "deploy to staging", "deploy to production", "run power platform pipeline", "deploy solution via pipeline", "promote solution", "push to staging", "push to production".

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Generate and optimize CI/CD pipelines for GitLab CI and CircleCI. Use when a user asks to set up GitLab CI, create a CircleCI pipeline, build a CI pipeline for GitLab, automate deployments with CircleCI, add test automation to GitLab, or configure continuous integration on non-GitHub platforms. For GitHub Actions pipelines, use the github-actions skill instead.

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Top-level orchestrator for the Gaia Skill Tree evidence verification pipeline. Run this when you want to do a full evidence pass — collecting raw sources, verifying live GitHub star counts, running Phase 2B benchmark-source verification, adversarially auditing for quality/formatting issues, and checking that every URL is reachable — all in one coordinated sequence. Trigger phrases: "run the evidence pipeline", "full evidence pass", "verify evidence", "evidence verification pipeline", "run ev-pipeline", "check all evidence", "audit the data lake", "evidence quality sweep", "refresh evidence", "validate evidence sources". Also aliased as /evidence-verification-pipeline. Use the individual sub-skills (ev-collection, ev-star-verification, ev-adversarial-audit, ev-link-validation) only when you need to re-run one phase in isolation; for end-to-end work, always start here.

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Meta-/Umbrella-Skill für die Familie „Projekt-/Pipeline-Aufbau & -Umbau". Kennt alle Skills für Anlegen, Aufnehmen, Umbauen und Analysieren von Projekten und Pipelines und leitet zum passenden weiter. Nutze diesen Skill, wenn unklar ist, ob etwas neu angelegt (Greenfield) oder umgebaut (Bestand) werden soll bzw. ob es um ein einzelnes Projekt oder eine ganze Pipeline geht. Auch auslösen bei „neues Projekt/Pipeline anlegen", „bestehendes umbauen", „Projekt aufnehmen", „Ordnerstruktur renovieren", „welcher Bootstrapper passt".

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Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.

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Comprehensive CI/CD pipeline exploitation methodology covering GitHub Actions injection vectors (expression injection via PR titles and issue bodies, workflow_run event abuse, GITHUB_TOKEN over-scoping, composite action supply chain compromise), Jenkins attack paths (Groovy sandbox escapes, script console remote code execution, Java remoting deserialization, credential store dumping, shared library injection), GitLab CI exploitation (YAML anchor injection, runner registration token abuse, CI variable extraction, protected branch bypass via merge request pipelines), and Azure DevOps pipeline agent compromise with service connection theft. Includes artifact poisoning techniques across all platforms, tooling guidance for gato and jenkins-attack-framework, and maps to MITRE ATT&CK T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Covers enumeration of pipeline configurations, privilege escalation from contributor to code execution, lateral movement through pipeline trust boundaries, and p...

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Design machine learning workflows from data preparation through training, validation, deployment, and monitoring. Use templates and guidance to connect stages, track versions and experiments, handle failures, and plan gradual releases with rollback options.

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• Designing an end-to-end machine learning pipeline
• Building reproducible training workflows
• Comparing models before production deployment

Design CI/CD pipelines that move applications through development, staging, and production. Define approval and security checks, gradual release strategies, health checks, and rollback procedures, and investigate gates or deployments that fail to progress.

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• Designing CI/CD pipelines across multiple environments
• Introducing canary or blue-green releases
• Investigating stalled approvals and release gates

Prepare a sales forecast narrative from CRM records or an uploaded pipeline export. Organize deal amounts and counts by forecast category, explain changes since the previous review, and highlight closing risks and support needed.

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• Preparing for a quarterly sales forecast meeting
• Reviewing deal outlooks in a manager one-on-one
• Building a forecast narrative from a pipeline export

Review revenue plans through six questions covering pipeline coverage, win rates, customer revenue retention, sales rep ramp time, discounts, and lead sources. Produce a structured assessment of gaps and three concrete actions before committing targets or expanding sales.

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• Review assumptions before committing a quarterly revenue target
• Find pipeline stages where win rates are weakening
• Separate retention, contraction, and expansion in customer revenue

Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.

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• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Manage prospects and clients for GEO services, which help sites prepare for AI search visibility. Keep contact details, audit history, notes, and monthly deal values in a local JSON file, and summarize sales stages and potential revenue.

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• Register prospects and review their sales stages
• Keep audit history and dated interaction notes together
• Summarize won contracts and potential monthly revenue

Inspect CircleCI runs, identify failing steps, and validate configuration locally using the CircleCI CLI, its command-line tool. It also guides targeted reruns and pipeline launches, reporting results, permission gaps, and next steps.

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• Investigating failed CircleCI runs
• Validating configuration before committing changes
• Rerunning a specific workflow or job

Turn articles, notes, or code history into talk preparation materials, including angles, titles, scripts, slide drafts, and revision sheets. Coordinate the stages with user checkpoints, or run an individual stage again.

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• Prepare a talk from articles or notes
• Build an experience report supported by Git history
• Develop a script after choosing an angle and title

Provides GitHub Actions and GitLab CI templates for automating tests, builds, and releases. It adapts configurations to project types and covers caching, parallel jobs, credential handling, and controls for production deployments.

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• Setting up CI/CD for a frontend or backend project
• Automating tests and releases for a library
• Running checks only for changed parts of a monorepo

Design or revise Marin experiment pipelines by connecting reusable tasks and their outputs in a dependency graph. Organize shared implementations separately from experiment settings, then check that the workflow connects the intended inputs and stages.

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• Creating an experiment workflow entrypoint
• Connecting pipeline stages and outputs
• Separating reusable code from experiment settings

End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

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Universal SDLC pipeline orchestrator with stack provider auto-discovery. Reads stack.md profiles from installed plugins, picks the highest-priority match, executes a 5-phase pipeline (BA → Dev → QA → Sec → Docs) plus stack-defined extra phases. Use when: - User invokes /sdlc:start "<feature>" - User asks to "run the SDLC pipeline" or "go through the full pipeline" - You need to coordinate specialist agents to deliver a complete feature Do NOT use for: - Trivial single-file edits (just edit directly) - Read-only questions about the codebase - Casual conversation

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