427 件 ・ 関連度順
概要と使いどころ
Parse, search, analyze, and ingest LinkedIn GDPR data exports into structured JSON or RLAMA for semantic search. Covers messages, connections, profile data, and Markdown export. Requires a LinkedIn GDPR ZIP file. Triggers on 'LinkedIn data', 'search messages', 'analyze connections', 'LinkedIn export', 'GDPR download'.
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Build and query fully local RAG knowledge bases from documents (PDF, MD, code, etc.) using RLAMA and Ollama — no cloud, no data leaving the machine. Triggers on 'local knowledge base', 'search documents', 'document Q&A', 'RAG query', 'ingest files', 'semantic search'.
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。
Agent Brain /brain-wiki: ingesting sources or updating durable project knowledge.
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Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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。
Manage an engagement buffer that ingests, prioritizes, rate-limits, deduplicates, and tracks state for incoming engagement items across platforms. Generates periodic digests and enforces cooldown periods. Composes with du-dum: du-dum sets the observation/action cadence, this skill manages the queue between beats.
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MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
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 ingesting source material for a writing project, building a knowledge map from notes and documents, identifying research gaps, or preparing a research corpus for content creation.
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 you need to hunt in the organisation's own Microsoft Sentinel workspace — sweep the logs for IOC hits (IP, domain, hash, URL, account), hunt behavioural TTPs from MITRE ATT&CK techniques, run KQL against live data, or check which tables the workspace actually ingests. Discovers available tables first and only generates KQL for tables that verifiably exist, so hunts adapt to each environment's connectors. Commonly chained by the /*-investigation skills to answer "were we exposed?". Reads $SENTINEL_TENANT_ID, $SENTINEL_CLIENT_ID, $SENTINEL_CLIENT_SECRET, $SENTINEL_WORKSPACE_ID. Read-only.
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 you need Liberty91 platform intelligence — what actually happened (deduplicated Threat Events with every source, Admiralty reliability/credibility and verification stage), whether an IOC is already known to your account, the canonical threat library (actors, malware, vulnerabilities, clusters, ATT&CK TTPs), alert matches, or which of your customer organizations an occurrence affects — or when you need to push intelligence back: ingesting your own report, generating an intelligence package, or uploading an organization document. Also supplies event provenance fields, when the platform returns them, and Threat Library alias resolution to /source-provenance and /quality-of-information-check. Two-way, first-party integration. Commonly invoked by /ip-investigation and friends, /ioc-enrichment-workflow, /threat-actor-profiling, /campaign-tracking and /vulnerability-intelligence. Reads $LIBERTY91_API_KEY.
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。
Set up a slushpile workspace. Ingests an existing resume or LinkedIn export, interviews the user to fill the gaps, then writes profile.md, preferences.yaml, and stories.md. Run once per workspace, before any other slushpile 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。
Broad, pre-curation evidence reconnaissance for a declared Gaia skill, intake batch, upstream repository, or proposed generic capability. Run before choosing a discovery strategy or mapping topology, to give the curator independent research leads and counterevidence. Context only: never scores, grades, ratifies, ingests, or mutates the registry. Later ev-discovery and ev-pipeline phases validate/promote any selected leads.
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。
End-to-end Gaia curation pipeline orchestrator. Routes each phase to its dedicated skill and keeps you on track from first discovery to closed intake. Use when starting a fresh curation run or when you want a single skill to hold the sequence together without duplicating each phase's instructions. Trigger phrases: "/gaia-full-pipeline", "run the full pipeline", "start a curation from scratch", "take this skill through the full pipeline", "curate end-to-end", "pipeline run", "full curation flow". Fused from: gaia-curate + gaia-curate-chain + gaia-curate-dynamic + gaia-curate-trending + gaia-bot-curate + gaia-draft-curate + ev-pipeline + gaia-ingest + gaia-intake-close.
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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".
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。
Comprehensive, reusable playbook for multi-component suite intake under Yggdrasil III. Audits component installability, maps origin candidates to generic capabilities, ingests named components with shared-repo baseline caps, manifests suites via gaia dev fuse, calibrates capstone and component ranks, and closes out via Class S regeneration.
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Curate Gaia bot crawler branches into real-skill catalog entries and clean up processed bot/* branches. Use when the user says: "process bot branches", "curate the crawl output", "clean up bot proposals", "run bot curation", "merge the crawler results", "review bot/* branches", "ingest crawler data", or explicitly types /gaia-bot-curate. Also triggers when the user asks to review pending `bot/*` branches, turn crawler JSON proposals into registry nodes, decide which crawled skills to accept vs reject, or batch-add skills from an automated crawl run. This skill handles the full pipeline: fetch remote bot branches, triage proposals, run `gaia dev` mutations, validate, open a PR, and delete consumed bot branches.
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。
Phase 4 of the Gaia evidence verification pipeline. Checks every URL in the evidence data lake for HTTP liveness — catches dead links (404, 403, timeout, connection errors) before evidence is ingested into the registry. Use this skill when running the full /ev-pipeline, asked to "validate links", "check evidence URLs", "run link validation", "find dead links in the data lake", "check HTTP status of evidence sources", or "verify evidence links still work". Wraps validate_sources.py as a temporary coexistence URL-health shim.
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。
Run this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the 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。
API security automation skill for REST, GraphQL, gRPC, and WebSocket APIs. Covers OpenAPI/AsyncAPI ingestion, authenticated fuzzing, OWASP API Top 10 (BOLA, BFLA, mass assignment, SSRF), schema diffing, GraphQL introspection abuse, JWT and OAuth misuse, rate-limit and replay testing. Use to automate API assessments with safe, scoped, evidence-backed findings.
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Use when designing, validating, deploying, changing, or troubleshooting Volcengine TLS LogCollector collection on Linux hosts or Kubernetes, including Project, Topic, host group, collector rule, index, binding, heartbeat, parsing, and end-to-end log ingestion checks through volclog. 当用户需要设计、部署、 变更或排查火山引擎日志服务 LogCollector 采集（主机、Kubernetes DaemonSet/Controller、机器组、采集配置、 心跳、解析、日志未入库）时调用。
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 Volcengine cloud resources using the Volcengine CLI (`ve` command). Supports all Volcengine services including ECS, VPC, CLB, RDS, Redis, and more. Trigger this skill whenever the user asks to create, query, modify, or delete cloud resources on Volcengine, mentions the `ve` command, says "volcengine CLI", or describes infrastructure tasks such as "create an ECS instance", "set up a VPC", "list security groups", "allocate an EIP". Also trigger on Chinese prompts mentioning "火山引擎" or "火山" (e.g., "火山引擎上有哪些 ECS"、"查一下我火山的云服务器"、 "火山引擎创建一个 VPC"、"火山的 Redis 实例列一下"). Also trigger when the user encounters errors from `ve` commands and needs troubleshooting help. Also use for TOS bucket/object operations, tosutil (sometimes written tosutils), Ark CLI / arkcli model discovery, inference, generation, endpoints, fine-tuning, plans, or usage, and TLS (日志服务) / volclog log search, analysis, ingest, export, or LogCollector collection, through `ve tosutil`, `ve arkcli` and `ve volclog`.
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。
Building robust document ingestion — chunking, metadata, and multi-format pipelines for search and RAG.
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。
CSV processing done right — parsing edge cases, dialects, and large files — use when ingesting or exporting CSV data.
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。
Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.
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Generate a production-grade OEE (Overall Equipment Effectiveness) measurement pipeline for a manufacturing line — data ingestion from MQTT/OPC-UA/Modbus/CSV, A×P×Q calculation engine, Six Big Losses categorizer, MTBF/MTTR/TEEP metrics, Grafana or Streamlit dashboard.
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。