501 件 ・ 関連度順
概要と使いどころ
Design evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI system.
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Shared backlog conventions for Azure DevOps, GitHub, and Jira. Use for platform resolution, autonomy tiers, sanitization guards, and story quality, and for Azure DevOps pull request creation and build status or log retrieval.
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Only for explicit prior-work/reuse/history requests, not status checks. Finds and verifies existing work before new production when reuse is plausible. Use when the user explicitly references earlier work, existing code, history, or prior decisions, or says 以前做过, 已有代码, 别重复造轮子. Not for current tests, files, bug fixes, or mechanical verification. Produces a reuse/adapt/reject receipt; zero hits never prove absence.
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Fetch existing YouTube transcripts/subtitles or archive spoken video content as text, and download YouTube videos/audio or HLS streams (m3u8) with yt-dlp and ffmpeg. Use for transcript/subtitle retrieval, text-first video archiving, video/audio downloads, protected streams with authentication headers, and download failures including nsig, 403 or browser-cookie issues.
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Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.
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Database specialist for SQL, NoSQL, and vector database modeling, schema design, normalization, indexing, transactions, integrity, concurrency control, backup, capacity planning, data standards, anti-pattern review, and compliance-aware database design. Use for database, schema, ERD, table design, document model, vector index design, RAG retrieval architecture, migration, query tuning, glossary, capacity estimation, backup strategy, database anti-pattern remediation work, and ISO 27001, ISO 27002, or ISO 22301-aware database recommendations.
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Design schemas and migrations, tune queries, or plan vector retrieval and database operations. Application API implementation uses oma-backend.
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Use only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.
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INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).
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Configures Simulink simulations non-destructively using SimulationInput objects — parameter overrides without modifying the model, batch sweeps via parsim, custom input signals via Dataset, and simulation data retrieval via logsout. Use when running sim()/parsim() with setVariable, setBlockParameter, setExternalInput, or when performing parameter sweeps and multi-run analysis. Not needed for one-shot simulations without configuration.
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Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
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Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.
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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
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Conversational search runtime: send messages, keep sessions consistent, and verify retrieval behavior and responses.
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Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
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Monitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection, and continuous regression testing.
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Analyse the vault's link graph and report on its shape: orphan rate, average degree, components, hubs, bridges and clusters, with what each number means for retrieval. Use this skill when the user asks about the health or structure of their graph, wants metrics, asks which pages are most connected or most isolated, or asks whether the vault is fragmented. Do NOT use for fixing what it finds, which is second-brain-lint, or for answering a content question.
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Evaluate an AI workflow against representative cases and its permitted actions. Use for model, retrieval or agent evaluation; tests do not grant release authority.
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SEC EDGAR filings retrieval and financial document analysis
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Financial market data retrieval and analysis via Alpha Vantage API
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AI-powered academic paper search and retrieval
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Designs production-grade RAG systems with document chunking, embedding generation, vector store configuration, hybrid search pipelines, reranking, and retrieval quality evaluation.
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Compliant, retrieval-grounded research over investor-State dispute settlement (ISDS) awards and decisions. Use when the user asks about ICSID / investment-treaty arbitration cases, awards, or doctrines (fair and equitable treatment, expropriation, jurisdiction, costs, annulment, etc.) and wants answers grounded in the actual document text with pinpoint citations. Identifies the correct document on the case page, confirms it against the PDF's own first pages, retrieves primary documents on demand from ICSID, PCA, etc., never scrapes or hosts a corpus in violation of applicable terms, and cites only retrieved text.
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Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.
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。