1.7万 件 ・ 関連度順
概要と使いどころ
Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, dedupe, relabel, reassign, and post summaries — all from the shell without clicking through the UI.
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。
Turn meeting transcripts or rough notes into crisp summaries with decisions, risks, and owner-tagged action items; use for Zoom/Meet/Teams transcripts, call notes, or long meeting chats to generate share-ready outputs.
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。
Analyzes your recent Codex chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
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。
These are instructions for creating design philosophies - aesthetic movements that are then EXPRESSED VISUALLY. Output only .md files, .pdf files, and .png 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。
Codified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships.
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。
Systematically finds and fixes bugs using proven debugging techniques. Traces from symptoms to root cause, implements fixes, and prevents regression.
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。
This skill ensures all code follows security best practices and identifies potential vulnerabilities. Use when implementing authentication or authorization, handling user input or file uploads, or creating new API endpoints.
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。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
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。
Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.
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 robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.
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。
Azure Cosmos DB SDK for Rust (NoSQL API). Use for document CRUD, queries, containers, and globally distributed 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。
Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed 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。
Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.
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。
AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g
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。
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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。
You are an API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development.
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。
You are an expert in the interface between LLMs and the outside world. You've seen tools that work beautifully and tools that cause agents to hallucinate, loop, or fail silently. The difference is almost always in the design, not the implementation.
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。
You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.
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。
You know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.
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。
You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.
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。
Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.
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。
Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.
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。
Designs cytosine (CBE, C-to-T) and adenine (ABE, A-to-G) base-editor guides by positioning the target base at the activity-peak of the editing window (protospacer positions ~5-7, PAM-distal numbering), minimizing bystander edits for product purity, reading dinucleotide context (APOBEC1 TC favored / GC disfavored), and selecting the editor variant (BE4max, ABEmax, ABE8e, YE1/SECURE, TadCBE, CGBE, SpG/SpRY-BE). Covers knockout by premature stop (CRISPR-STOP/iSTOP) and splice-site disruption, the three off-target classes (Cas-dependent, Cas-independent DNA, RNA), outcome prediction (BE-Hive/DeepBE), and the base-vs-prime-vs-HDR decision. Use when installing a transition mutation without a double-strand break, knocking out a gene without indels, or choosing CBE vs ABE. Generic guide scoring, prime editing, and HDR donors are separate skills.
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。