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概要と使いどころ
Review a pull request or contribution deeply, explain it tutorial-style for a maintainer, and produce a polished report artifact such as HTML or Markdown. Use when asked to analyze a PR, explain a contributor's design decisions, compare it with similar systems, or prepare a merge recommendation.
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 a Research Pilot project needs a read-only recommendation for the next workflow action.
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。
Takes a proofread or apsa-style report file (or any markdown file using **Original:** / **Recommended:** syntax) and rewrites the original source file with git merge conflict markers so the user can accept or reject each suggested edit using VS Code or Positron's built-in merge conflict UI. Branch names are "original" and "claude-edits". Use when asked to apply edits, insert conflict markers, or set up merge resolution for a copy-edit report. Supports an optional @sec-label argument to restrict markers to one section.
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。
Draft blog posts, social media, email newsletters, landing pages, press releases, and case studies with channel-specific formatting and SEO recommendations. Use when writing any marketing content, when you need headline or subject line options, or when adapting a message for a specific platform, audience, and brand voice.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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。
Auto-activates during requirements analysis to evaluate technical stack compatibility, recommend appropriate technologies, and assess performance implications.
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。
Auto-activates during requirements analysis to assess security risks using OWASP Top 10 framework, identify security requirements, and recommend mitigation strategies.
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。
Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.
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。
Condense long reports, documents, or data exports into structured summaries with key findings, implications, and recommended actions. Use when the work is distilling dense content for busy readers. Do not use for original analysis, strategy formulation, or when the source material needs expert interpretation beyond summarization.
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。
Calculate and interpret Earned Value Management (EVM) metrics per PMBOK 8 Measurement Performance Domain. Compute SPI, CPI, EAC, ETC, TCPI, and generate variance narratives with corrective action recommendations. Use during project execution to assess schedule and cost performance.
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 structured vendor/tool evaluation matrices with weighted scoring, TCO analysis, and recommendation memos. Covers criteria definition, deal-breaker identification, and negotiation points. Use when selecting SaaS tools, evaluating technology vendors, or making build-vs-buy decisions.
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。
Analyze survey and feedback data with statistical distributions, cross-tabulations, significance testing, and open-ended theme extraction. Produces executive-ready survey reports with segment comparisons and action recommendations. Use when processing NPS, CSAT, employee engagement, or market research surveys.
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。
Write one-page executive briefs and decision memos that turn messy inputs into a concise recommendation, options, risks, decision ask, and next steps. Use when leaders need a fast read before approving budget, policy, vendor, headcount, or strategy changes. Do not use for long-form reports, slide design, or generic summarization without a decision.
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。
Security Operations Center skill for alert triage, detection engineering, incident response, log analysis, threat hunting, SIEM queries, EDR investigation, timeline building, IOC handling, escalation notes, containment recommendations, and analyst-ready reporting. Use for blue-team operations, suspicious event analysis, and defensive cybersecurity workflows.
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 agent when you need to review UniProt annotation rules (ARBA, UniRule) for quality, biological accuracy, and GO annotation appropriateness. This agent performs comprehensive analysis of rule condition sets, evaluates literature support, assesses taxonomic scope, and recommends curation actions.
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。
Qdrant vector database -- collection management, point operations, payload filtering, named vectors, quantization, recommendations, snapshots
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。
Classify a proposed code change as Leaf Node or Core Code to decide how much AI usage and review rigor is appropriate. Use this skill whenever the user asks "is this leaf or core", "what kind of change is this", "should I vibe code this", "can AI write this", "how rigorous should the review be", or before starting any non-trivial change. Also useful as a sub-step inside other workflows (planning, PR prep, code review) when classification matters. Asks 6 diagnostic questions about propagation range, change frequency, tech-debt tolerance, examples, failure cost, and review intensity, then outputs a clear classification with the recommended approach. Trigger even when the user describes a change without explicitly asking for classification — the answer changes how they should proceed.
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。
Ask Daniel a blocking decision as options with a recommendation and honest pros/cons, rather than prose. Use whenever work is blocked on a judgment call, a product decision, an approval, or a choice between approaches — instead of writing "let me know which you prefer" at the end of a message.
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。
Query, recommend, and switch the Pulp JS engine backend (QuickJS, JavaScriptCore, V8). Handles "which JS engine", "switch to V8", "engine for Three.js".
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。
Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment 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。
Analyze a feature or sprint for scope creep by comparing current scope against the original plan. Flags additions, quantifies bloat, and recommends cuts. Use when user says 'any scope creep', 'scope review', 'are we staying in scope'.
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。
Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.
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。
Automatically analyze project state, detect stage, identify gaps, and recommend next steps based on existing artifacts. Use when user asks 'where are we in development', 'what stage are we in', 'full project audit'.
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。
Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.
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。