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概要と使いどころ
When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.
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。
Same reverse-thinking method as dh:rt-ica (see dh:rt-ica or dh-glossary for the definition), but localizes any MISSING input to the affected task only, as a non-blocking information-completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing input report with dependency mapping, required unblock actions, and planning annotations for downstream tasks. Use dh:rt-ica instead at the S2 implementation gate where missing inputs must halt execution.
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 the user asks for a hypothetical walkthrough of a system to expose tensions, frictions, gaps, or contradictions between its expected and evidenced flow.
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。
Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.
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。
Review test effectiveness against authoritative contracts, relevant fault detection, refactor tolerance, boundary fidelity, isolation, and diagnostic value. Use when auditing test quality, reviewing coverage gaps, improving regression protection, or assessing tests after a change.
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。
Research community usage patterns, real-world gotchas, and client compatibility for a specific known library, tool, or protocol feature. Use when a technical-researcher orchestrator needs community-sourced evidence about a named library or feature — bug reports, workarounds, compatibility gaps, and patterns from issue trackers and discussions. Distinct from the broad ecosystem-researcher agent — this skill targets a KNOWN entity and mines what real users have actually experienced, not what exists in a domain.
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。
Grooms a GitHub milestone for parallel execution — batch-grooms ungroomed items, assesses scope gaps, analyzes cross-item dependencies via Impact Radius overlap, builds conflict groups, assigns items to execution waves, and persists the dispatch plan via dispatch_create_plan MCP tool. Calls dispatch_wave_start per wave to register state. Use when preparing a milestone for /work-milestone execution. Pass the milestone number as the first argument. Requires milestone items assigned via /group-items-to-milestone.
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 competing products and turn the market gap into credible positioning, messaging angles, and social campaign claims. Use when comparing competitors, writing alternative pages, sharpening product positioning, or planning campaigns against a category narrative.
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 raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.
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。
Builds or audits a literature review, producing an evidence map, the closest prior work, source clusters, a gap verdict, and a synthesis plan for the introduction. Use when the user asks whether a contribution is novel, who has studied a question, what the literature establishes, or how to organize a review, or wants a reading list, Zotero export, or pile of papers turned into one. For systematic reviews or meta-analyses it produces the protocol scaffold and routes registration (PROSPERO or OSF) and screening to dedicated tools.
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。
Drafts or audits a scientific paper’s introduction and framing — the real-world "why", an evidence audit, the funnel from theory to a falsifiable if-then with a named estimand, bridges across experiments, and a contribution list matched to the design. Use when the user asks to write, restructure, or critique an introduction, motivate or frame a paper, repair a gap-in-the-literature opening, sharpen or calibrate a contribution claim, or connect experiments into one argument. Sentence-level flow and wording are out of 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。
Builds or audits a literature review: evidence map, closest prior work, gap verdict, and synthesis plan. Use to check whether a contribution is novel, map what a literature establishes, or organize a review; systematic reviews get a protocol scaffold.
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。
Search CrossRef, OpenAlex, PubMed, Semantic Scholar, and optional Scopus; deduplicate results, download open-access PDFs by DOI, classify papers, monitor new results, and analyze coverage. Use when asked to search literature, monitor a topic, download an open-access paper, classify papers, or analyze literature gaps.
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。
Crawl GitHub trending repositories, analyze with LLM for Chinese insights, categorize by themes, compute diffs against history, and generate Markdown reports. Default brief mode stops at trend analysis; optional detailed mode appends per-project analysis. Supports incremental gap-filling and selective re-analysis with caching.
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。
Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"
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。
Decompose work into stages with physical artifacts and acceptance checklists. Gap detector. Use when opening a WP ≥3h (open-loop/problem-framing) or on explicit request to break into stages. Executes R29 Decomposer role.
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。
Audit codebase for agentic layer coverage and identify gaps. Use when assessing agentic layer maturity, identifying investment opportunities, or evaluating primitive coverage.
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 semantic + keyword match score. Compares resume content against job description to calculate a fit score and identify gaps.
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 and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
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。
Review touched tests and test-adjacent code for style, Result-returning patterns, panic helpers, unwrap/expect use, and obvious coverage gaps. Use when a broad review needs a dedicated test pass or when the user asks for test-review specifically.
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。
Refresh and improve existing blog posts to outrank competitors using a 6-phase content audit and SERP gap analysis. Use when the user wants to update, refresh, or improve an existing blog post, recover lost rankings, or asks why a post fell behind competitors.
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。
Integrate and develop AuthCrunch secrets plugins using the SQLite, static, and AWS Secrets Manager implementations. Covers exact APIs, resource binding, payload types, safe metadata, lifecycle, parser gaps, and consumer validation; excludes identity-store authentication and host-specific module 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。
Develop AuthCrunch plugins in separate repositories, maintain category reference plugins under plugins/, and compose companion host plugins. Covers repository bootstrapping, shared and local guidance, category contracts, existing APIs, and integration gaps; secrets providers have a specialized workflow.
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。
REQUIRED for end-user customization of Linux desktop, window manager, or system config. Use when editing ~/.config/hypr/, ~/.config/omarchy/, ~/.config/alacritty/, ~/.config/foot/, ~/.config/kitty/, or ~/.config/ghostty/. Triggers: Hyprland, window rules, animations, keybindings, monitors, gaps, borders, blur, opacity, omarchy-shell, bar, terminal config, themes, background, night light, idle, lock screen, screenshots, reminders, layer rules, workspace settings, display config, and user-facing omarchy commands. Excludes Omarchy source development through `omarchy dev link` 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。