「selection」の検索結果

Select one target opportunity from an OST using evidence-based weighted scoring and tie-breakers.

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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.

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Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrieval_query Cypher fragments, query_params, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama), embedder setup, index creation, token usage tracking, Cypher 25 SEARCH clause, and LangChain/LlamaIndex integration. Does NOT handle KG construction — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.

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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.

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Write, run, and analyze structured test suites for Agentforce agents — functional AND security. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration; requests security testing, OWASP LLM Top 10, red-teaming, penetration testing, prompt-injection tests, a security grade, or a vulnerability assessment of an agent. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use agentforce-generate); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use agentforce-observe); performs a static safety review of .agent file content (use agentforce-generate Section 15).

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Use before booting anything - choosing local (start_task_preview or a workspace boot), the task's preview or stage, confirming the build is the PR head, and what to do when nothing can run

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Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

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Build and grow online communities across all platforms and contexts — from developer/B2B communities (Discord, Slack, Circle, Discourse) to social communities (Twitter/X, Reddit, Farcaster) to crypto/holder communities (Telegram). Covers strategy, platform selection, channel architecture, member journey mapping, onboarding, engagement rituals, ambassador/champions programs, moderation, governance, growth, retention, crisis management, and metrics. Use for: community-led growth, community strategy, developer community, user community, Discord/Slack/forum/Telegram setup, ambassador programs, community health, holder psychology, engagement systems, social community building, cross-platform coordination.

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ESPHome device configuration, firmware, and IoT product development. Covers ESP32, ESP32-S3, ESP32-C3, ESP32-C6, ESP32-H2, ESP32-P4, ESP8266, RP2040, RP2350, nRF52, LibreTiny, Shelly, Sonoff, Tuya, BLE proxy, Matter firmware, Thread, Zigbee, GPIO, sensor YAML, LVGL displays, LED strips, voice assistant hardware, device flashing, Arduino conversion, alarm_control_panel, lock, valve, media_player, microphone, speaker, audio DAC, event entities, datetime entities, Z-Wave proxy, MIPI DSI displays, and DLMS smart meters. Also covers designing new ESPHome-based products: hardware selection, component sourcing, PCB design (KiCad), enclosures, 3D printing, CE/FCC certification, BOM optimization, and manufacturing from prototype to production scale.

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Coverage-design method for qa-engineer — pyramid ratios per archetype, equivalence/boundary/property case selection, mutation score as the real coverage signal, and a flake-quarantine policy. Turns "coverage is 90%" (a number with no method) into a defensible test plan. Emits TEST-STRATEGY-{slug}.md, which the QA gate checks exists.

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Email/SMS lifecycle and deliverability framework for SMB Product-Builder products that send transactional or lifecycle messages (booking reminders, CRM sequences, receipts, win-back). Codifies provider selection (Resend/Postmark/Twilio/SendGrid), domain auth (SPF/DKIM/DMARC), consent and compliance (TCPA, CAN-SPAM, CASL, quiet hours, double opt-in), suppression-list discipline, and the transactional-vs-marketing split. Applied by integrations-engineer and senior-dev whenever a feature sends messages — so deliverability and consent are designed in, not bolted on after the first spam complaint.

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Use when auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.

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Guides mobile platform selection and delivery across native and cross-platform stacks. Use when planning auth, push, deep links, releases, or app architecture for iOS/Android.

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Use when Codex needs Genesis capabilities through MCP without joining Genesis's session lifecycle. Covers tool selection, explicit state changes, and session-boundary limits.

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Code understanding tool selection. Use when exploring architecture, finding definitions, tracing call chains, assessing blast radius of changes, or debugging code paths in the Genesis codebase.

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Submit expense reimbursements through Benepass (app.getbenepass.com). For users whose employer uses Benepass as their benefits platform. Handles login, benefit selection, form filling, receipt upload, and submission. Requires browser/computer-use capabilities.

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Help order groceries for delivery. Concierge-style flow — store selection, occasion-based list building, budget tracking, and cart assembly.

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 @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

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Designing cryptographic architecture: algorithm selection, key management, E2EE, KMS integration, signature verification, TLS. Use when designing crypto protocols or key rotation flows.

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Search and filter TikTok global e-commerce products based on FastMoss data, supporting keyword search, multi-dimensional filtering (category, shop type, commission rate, sales, creator count, etc.) and sorting. Trigger when users mention TikTok product selection, TikTok product search, TikTok product data, TikTok creator sales, TikTok commission rate, TikTok bestseller tracking, TikTok GMV analysis, TikTok product search, TikTok product research, TikTok creator sales, TikTok commission rate, TikTok GMV analysis, FastMoss. Even if the user does not explicitly mention "FastMoss", trigger this skill whenever their need involves searching product data or analyzing product performance on TikTok.

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Filter Temu products by multiple dimensions (keyword/product ID/store ID, front/backend categories, price, rating, reviews, total/weekly/daily sales, listing date, fully-managed/semi-managed, semi-managed regions, tags, etc.). Triggered when users mention Temu products, Temu product selection, Pinduoduo cross-border, Temu bestsellers, Temu semi-managed, fully-managed products, Temu product query, or temu items. Even if the user does not mention the tool name, this skill should be triggered whenever searching for products, viewing sales/ratings/prices, or filtering products on Temu.

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Search and browse Walmart product listings by keyword, category, price range, and other conditions. Trigger when users mention Walmart product search, Walmart product listing, Walmart price comparison, Walmart competitor analysis, Walmart product research, Walmart market data, find products on Walmart, Walmart search, Walmart products, Walmart product selection, Walmart pricing, Walmart competitors, Walmart market. Even if the user does not explicitly mention "Walmart search", trigger this skill whenever their need involves searching for products on Walmart, checking product availability, comparing Walmart prices, or analyzing Walmart product listings.

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AI Overview (AI Mode) scraping via Google Search. Returns AI-summarized key points for a single keyword, ideal for deep research, technical Q&A, long-tail product selection, and cross-border consumer preference analysis using the latest web information. Single-round only; follow-ups require the agent to summarize context and issue a new request. Triggered by: Google AI, AI Overview, AI Mode, Google AI search, AI search, deep research, consumer preference analysis, web summary, long-tail product research, cross-border market insights.

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。