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概要と使いどころ
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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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로, flujo de trabajo académico, investigación a artículo, flujo completo de artículo, pipeline de investigación completa, publicación de investigación, flujo de trabajo completo del artículo.
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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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Multi-agent exploration-to-PR pipeline for safely exploring, fixing, and shipping a feature or CLI. Use when the user says: "explore X", "test and ship X", "run the pipeline on X", "run /feature-pipeline on X", "let's properly investigate X", "stress-test X and file issues", or "take X through the full pipeline." Also triggers on: "find what's broken in X", "test X end-to-end", "do a sandbox review of X", "file issues for X and fix them." Do NOT use for single-file bug fixes or one-off edits — the overhead is only justified when exploration, planning approval, and CI watching all matter. Manages state via scripts/state.sh (jq required). Five phases: LIGHTER explores → ORCHESTRATOR drafts PR → HEAVIER plans (user approves first) → ORCHESTRATOR commits → HEAVIER sandbox review → LIGHTER iterates CI → LIGHTER drift check + summary. Four mandatory stop hooks (M1–M4) gate each phase transition.
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Reference guide for the full manual Gaia curation pipeline. Invoke at any phase when unsure what command to type next: gaia-curate, L4 review, gaia push, ev-pipeline (Phases 0–4), gaia-ingest, or close. Trigger phrases: "/gaia-consult", "what's the command for", "remind me how to", "I'm lost in the pipeline", "what do I do next", "consult the pipeline doc", "pipeline reference".
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Execute ENCODE Whole Genome Bisulfite Sequencing (WGBS) pipeline from FASTQ to methylation calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing WGBS/bisulfite-seq data, calling methylation levels, generating bedMethyl files. Trigger on: WGBS pipeline, bisulfite sequencing, methylation calling, DNA methylation pipeline, bismark, bwa-meth, bedMethyl.
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Use when authoring or reviewing CI/CD pipelines — GitHub Actions workflows, reusable workflows, composite actions, OIDC federation to AWS/GCP, caching, artifacts, and pipeline security hardening. Triggers on edits to .github/workflows/**, action.yml, composite action definitions, or mentions of "CI", "CD", "pipeline", "GitHub Actions", "workflow", "OIDC", "runner", "deploy script", "release", or "build pipeline".
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Builds multi-touch pipeline attribution models and generates stakeholder-ready narratives from campaign performance data. Use when analyzing channel contribution to pipeline, preparing monthly/quarterly revenue reports for leadership, diagnosing pipeline misses, modeling budget reallocation scenarios, or translating raw attribution data into board-deck language. Designed for B2B SaaS demand gen teams that report on pipeline and revenue, not leads or MQLs.
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Leader view - roll up a team's pipeline by rep and stage, flag at-risk deals, and surface coaching moments. Use when the user asks "show my team's pipeline", "team forecast", "prep for my team's pipeline review", or "where does my team need help".
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Watch and monitor GitHub Actions workflow runs using the gh CLI. Use when the user wants to check workflow status, watch a running workflow, view CI/CD jobs, or monitor build progress. Trigger keywords - watch pipeline, pipeline status, CI status, check build, monitor CI, view pipeline, pipeline progress, workflow status, actions status.
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CRM-lite for managing GEO agency prospects and clients. Track leads through the full sales pipeline: Lead → Qualified → Proposal Sent → Won → Lost. Store audit history, notes, deal values, and generate pipeline summaries. Use when user says "prospect", "lead", "client", "pipeline", "crm", "nuovo prospect", "aggiungi cliente", or when managing the business side of GEO services.
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Orchestrates the complete talk preparation pipeline from raw material to revision sheets, running 6 stages in sequence with human-in-the-loop checkpoints for REX or Concept mode talks. Use when starting a new talk pipeline, resuming a pipeline from a specific stage, or running the full end-to-end preparation workflow.
日本語の概要は準備中です。原文の説明を表示しています。
Provide CI/CD best practices and pipeline templates for GitHub Actions and GitLab CI, recommending configurations based on project type (frontend, backend, fullstack, library, monorepo, mobile). Trigger when users ask about setting up CI/CD, automating builds, improving pipelines, or mention keywords like GitHub Actions, GitLab CI, pipeline templates, or deployment automation.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 9-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
日本語の概要は準備中です。原文の説明を表示しています。
NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.
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GitHub Actions CI/CD pipeline configuration for Golang projects — workflow files for test, lint, SAST, coverage and vulnerability-scan jobs, Dependabot and Renovate config files, GoReleaser release pipelines, Docker build/push, repository security settings, and AI-driven PR review. Use when setting up or improving Go project CI, writing or fixing `.github/workflows/*.yml`, adding a linter or security scanner as a pipeline job, wiring automated dependency-update bots, or adding quality gates. Covers wiring tools into a pipeline, not the analysis they perform: do NOT use for choosing or interpreting security findings (→ See `samber/cc-skills-golang@golang-security` skill) or for choosing, upgrading, or auditing dependency versions (→ See `samber/cc-skills-golang@golang-dependency-management` skill).
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Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.
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Expert knowledge for Azure Pipelines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring YAML pipelines, agents, service connections, Key Vault secrets, deployments to Web Apps/Kubernetes, or GitHub-hosted agents, and other Azure Pipelines related development tasks. Not for Azure DevOps (use azure-devops), Azure Boards (use azure-boards), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).
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Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
日本語の概要は準備中です。原文の説明を表示しています。