「pipeline」の検索結果

Manage contacts, companies, deals, and pipeline with crm.cli — a headless CLI-first CRM backed by SQLite with a virtual filesystem interface

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Create and refine sales playbooks, deal strategies, MEDDPICC analyses, customer decks, and outreach sequences. Use for any sales methodology work, pipeline review, or go-to-market planning.

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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Import structured data into Neo4j — LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin database import full (offline bulk), apoc.load.csv/json, apoc.periodic.iterate, driver batch writes. Covers method selection, header file format, type coercion, null handling, ON ERROR modes, CONCURRENT TRANSACTIONS, pre-import constraint setup, and post-import validation. Use when importing CSV/JSON/Parquet files, migrating relational data to graph, or bulk-loading large datasets. Does NOT handle unstructured document/PDF/vector chunking pipelines — use neo4j-document-import-skill. Does NOT handle live app write patterns (MERGE/CREATE) — use neo4j-cypher-skill. Does NOT handle neo4j-admin backup/restore/config — use neo4j-cli-tools-skill.

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Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API. Use when creating, pausing, resuming, resizing, or deleting AuraDB Free/Professional/Business Critical/VDC instances; downloading credentials; scripting CI/CD pipelines; polling async status; or using the Terraform neo4j-labs/neo4jaura provider. Covers auth setup (client credentials OAuth2), credential lifecycle (download once — never recoverable), instance type selection, region codes, and Python provisioning scripts. Does NOT handle Cypher queries — use neo4j-cypher-skill. Does NOT cover Graph Data Science algorithms — use neo4j-gds-skill or neo4j-aura-graph-analytics-skill. Does NOT cover neo4j-admin/cypher-shell — use neo4j-cli-tools-skill.

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。

Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.md.

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。

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

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。

Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.

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Orchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building a notebook or app, or running the full onboarding pipeline. Time budget ≤15 min autonomous, ≤90 min HITL. Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or Cypher migration — use neo4j-migration-skill. Does NOT cover CLI/admin tasks on an existing DB — use neo4j-cli-tools-skill.

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Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

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。

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, semantic or structural nearest-neighbor lookup, or hybrid search (vector + fulltext, multiple vector sources, or graph-derived scores). Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext-only/keyword-only search — use neo4j-cypher-skill. Does NOT compute GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.

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。

Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher — covers graphdatascience client 2.x, GraphDataScience, gds.graph.project.native, gds.graph.project.cypher, snake_case endpoints, graph catalog operations, stream/stats/mutate/write modes, memory estimation, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and cleanup. Use for Aura Pro, self-managed, local, or offline Neo4j DBMS with the GDS plugin installed. Does NOT cover Aura Graph Analytics GDS Sessions, AuraGraphDataScience, GdsSessions, gds.graph.project.remote, or AuraDB Cypher API projection/session management — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.

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。

Guide for the Kimix web frontend (src/app/) — vanilla TypeScript + Vite, SSE-based chat UI mirroring sse_cli.py. Use when modifying the frontend, adding new UI features, changing the API client, or understanding the message rendering pipeline.

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。

Use when adding, importing, moving or loading textures, models, audio or scenes - per-platform compression, atlases, LODs, async loading per engine, engine metadata files (.meta, .import, .uid, .uasset), Git LFS, and asset validation tests.

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Use when writing tests for data or ML code — what to test at each layer (transforms, schemas, properties, pipeline smoke, model-quality thresholds, determinism and parity), how to build small synthetic fixtures, and what not to test

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Use when building or changing features, preprocessing, encoders, resampling or train/validation/test splits — keep every fitted transform inside a Pipeline fitted on training folds only, and build time and group features without seeing the future or the same entity twice

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Root-cause debugging discipline. Use when a test, build or pipeline fails, behaviour does not match expectations, a bug task arrives, or a task comes back in need_revision — before proposing any fix.

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Use when a mobile app needs a release pipeline - fastlane GitHub Actions to TestFlight (iOS) and Play internal track (Android), with code signing and the release naming that maps to TaskTrooper's prod deploy

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Design and manage infrastructure as code with Terraform — modules, remote state (S3 + DynamoDB), Stacks (deployments), test framework, preconditions/postconditions, moved/removed blocks, and CI/CD plan/apply separation. Use when user asks to write Terraform config, set up remote state, design modules, manage state, or automate infrastructure. Do NOT use for Kubernetes (use kubernetes), Docker (use docker), or CI/CD pipeline design (use ci-cd).

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Deploy, manage, and debug Kubernetes in production — Deployments, Services, Gateway API, Service Mesh (Istio/Linkerd/Cilium), eBPF observability (Cilium Hubble), security hardening (Pod Security Standards, OPA/Kyverno, seccomp, runtime security with Falco/Tetragon), Helm, HPA, PDB, topology spread, and debugging. Use when user asks to write K8s manifests, deploy to a cluster, debug pods, set up Gateway API, configure autoscaling, or harden cluster security. Do NOT use for Dockerfiles (use docker), CI/CD pipeline design (use ci-cd), or Terraform infrastructure (use terraform).

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Automate the complete Git development workflow — create feature branches with conventional naming, atomic commits with conventional commit messages, interactive rebase, squash merges, PR body generation from commit history, branch cleanup, and git worktree patterns. Use when user asks to create a branch, commit changes, make a PR, rebase, squash, clean up branches, or follow a Git workflow. Do NOT use for CI/CD pipeline configuration (use ci-cd), code review (use code-review), or GitHub Actions workflows.

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Optimize Docker images with multi-stage builds, distroless bases, BuildKit cache mounts, multi-arch builds, compose watch, security hardening (non-root, seccomp, capabilities drop), and vulnerability scanning via docker scout/trivy. Use when user asks to write a Dockerfile, optimize image size, set up docker-compose, debug containers, harden container security, or scan for CVEs. Do NOT use for Kubernetes deployments (use kubernetes), CI/CD pipeline design (use ci-cd), or Terraform (use terraform).

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Deep research with web search, source verification, and fact-checking. Runs before humanize + kami in the document pipeline. Use when user asks to research a topic, verify facts, gather sources, or do deep web investigation. Covers source validation, citation, and evidence hierarchy.

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Design database schemas with Prisma/Drizzle, PostgreSQL index strategy (B-tree, GIN, GiST, BRIN, Hash), query optimization (EXPLAIN ANALYZE), migration safety (expand/contract, zero-downtime), and sharding/partitioning. Use when user asks to design schema, create migrations, optimize slow queries, add indexes, choose between SQL/NoSQL, or set up Prisma/Drizzle. Do NOT use for data warehouse dimensional modeling, ETL pipeline design, or non-relational (MongoDB, DynamoDB) schema design.

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。