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概要と使いどころ
Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.
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An enterprise-infrastructure marketing canvas built around a near-black ground (#000000) and a system of per-product accent colors — Terraform purple, Vault yellow, Consul pink, Waypoint cyan, Vagrant blue — that act as identity tokens rather than decorative palette. Display type is hashicorpSans set in 600/700 with tight 1.17–1.21 line-heights; body type runs the same family at 500 weight with relaxed 1.50–1.71 line-heights. Cards live as charcoal surfaces with 1px translucent gray borders; product showcase cards lift into per-product chromatic gradients. The system reads as confident, technical, and intentionally multi-product — every section quietly signals which HashiCorp tool it represents.
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SEO optimization with keyword analysis, readability assessment, technical validation, content quality. Use for search rankings, blog posts, content audits, or encountering keyword density, readability scores, meta tags, schema markup errors.
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Code review practices with technical rigor and verification gates. Use for receiving feedback, requesting code-reviewer subagent reviews, or preventing false completion claims in pull requests.
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Use when an agent is asked to draft, rewrite, edit, review, polish, copyedit, simplify, humanize, or create written prose, including creative writing, essays, posts, scripts, speeches, emails, documentation, product copy, and other style-sensitive text. Apply George Orwell's six rules and ASD-STE100 Simplified Technical English as a plain-English discipline while preserving the user's intended meaning, audience, tone, and explicit constraints.
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Condense rebuttal prose into fewer words without changing the original meaning. Use when a response block, paragraph, or selected passage is too long but all technical content, citations, and commitments must stay intact. Supports academic English.
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 users want to practice or simulate Java, C++, Go, Golang, mixed-stack, or general backend technical interviews, including resume-based and job-description-based interview preparation.
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 high-level conceptual mapping from Active Directory attack techniques to the compliance controls they touch. Kerberoasting relates to authentication and logging, so it brushes ENS op.acc.5 / op.exp.8, NIS2 Art.21(2)(h), DORA RTS Art.9 / Art.21. Use this when a reader wants to understand which regulatory control an AD finding relates to, or to add an orientation note to a technical finding. This is a conceptual, orientative mapping only; it is NOT an auditor-defensible, curated, ID-by-ID control matrix. Covers ENS (op.acc.*, op.exp.*), NIS2 (Directive 2022/2555 Art.21), and DORA (RTS 2024/1774).
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。
Translate text between languages with cultural adaptation, terminology consistency, and support for multiple document types including technical docs, marketing copy, and UI strings. Use when the user requests translation or provides relevant inputs for this workflow.
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Optimize a webpage for search engines by analyzing on-page elements, improving technical performance, and implementing structured data for maximum visibility. Use when the user requests seo optimization or provides relevant inputs for this workflow.
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Think and work like an expert Molecular Biologist. Use when a task calls for Molecular Biologist judgment. Reasons from central-dogma sequence flow, binding affinity (Kd/Km/kcat), gene regulation, and biological-versus-technical replicate structure through MIQE-compliant RT-qPCR, ddPCR, Western/flow/microscopy, CRISPR editing with rescue, and IWGAV antibody validation while treating off-target reagent effects, batch effects, mycoplasma and cell-line misidentification, and toxicity-driven artifacts as first-class failure modes.
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Options trade execution, directional strategies long call covered call collar, spread strategies bull call bear put ratio ladder, volatility strategies straddle strangle strap strip, liquidity cost and implied volatility filters, integration with gold.technical_setups for options setup matching
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Author the research plan — fundamentals first, trade ideas last; Constitution Check evaluated twice; Deviation Register with mandatory Expiry; emits the four side artifacts (brief/entities/reproduce/contracts). Technical (market_data_stage: early) theses MUST emit the entities.md bars schema before implement (Q42).
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Trade execution using price action setups, major trend reversal trading top and bottom, MTR failure continuation, strong bull and bear breakout trading, strong and weak channel trading strategies, trading range strategies, opening range swings, integration with gold.technical_setups for setup matching and execution planning
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Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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。
Apply requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).
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Manage per-feature living documents that capture decisions, constraints, and reasoning across AI sessions during active development. Scoped to feature-level work — design, implementation, bugfix, refactor — not for codebase-wide assessments or product-wide specifications (those define their own document lifecycles). Handles creating new context documents, loading existing ones, and enriching them with new decisions. Use when starting a new feature, resuming work, making technical decisions, resolving questions, or when context needs to persist across sessions. Use this skill whenever the user mentions 'load context', 'update context', 'context doc', 'decisions', 'continue where we left off', 'what did we decide', or 'capture this 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。
Provider-independent media quality control and delivery direction for generated images, video, audio, ads, product content, social clips, broadcast and streaming assets, localization packages, and mixed-source edits. Use when an agent must define final export specs, inspect technical and perceptual quality, check captions/audio/color/accessibility/provenance/rights, create delivery sheets, version filenames, checksums, manifests, package deliverables, set acceptance or rejection criteria, and perform final delivery QA before handoff to a platform, broadcaster, streamer, client, or ad trafficker.
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。
Produce full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.
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Source technical and professional talent by combining LinkedIn profiles, GitHub contributions, and company intelligence. Identifies candidates with specific skills, assesses expertise depth, and provides outreach context for recruiting teams.
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Research GitHub user profiles including repositories, contributions, languages, stars, and activity patterns. Useful for developer talent sourcing, open-source community research, and technical due diligence on engineering teams.
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。
Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.
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Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
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Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
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。