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概要と使いどころ
Conduct comprehensive deep research on any topic using Dify-powered workflow - searches documentation, academic papers, tutorials, APIs, best practices, and returns structured analysis with insights.
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 should be used when performing maintenance or diagnostics on remote Linux systems. Triggers on "remote server", "Linux maintenance", "Ubuntu cleanup", "Debian", "disk space", "apt cleanup", "journal vacuum", "snap cleanup", "system diagnostics". Provides structured three-phase checklists with quantification.
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 ready to document findings, generate a report, or summarize binary analysis results. Compiles analysis findings into structured reports - correlates facts from triage/static/dynamic phases, validates hypotheses, generates documentation with evidence chains. Keywords - "summarize findings", "generate report", "document analysis", "what did we find", "write up results", "export findings"
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 needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.
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 competitive analysis with feature matrices, SWOT, positioning maps, and UX review. Covers research frameworks, pricing comparison, review mining, and visual deliverables. Use for: market research, competitive intelligence, investor decks, product strategy, sales enablement. Triggers: competitor analysis, competitive analysis, competitor teardown, market research, competitive intelligence, swot analysis, competitor comparison, market landscape, competitor review, competitive landscape, feature comparison, market positioning
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。
Provides a structured workflow for planning and executing code reviews like a senior engineer. Use when asked to review code, PRs, or plan a code review task.
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。
Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter 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。
Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.
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。
Decision rubric for promoting a prose prompt into a typed DSPy Signature. This is an ENHANCE overlay on top of the [[dspy]] library skill: it does NOT teach DSPy syntax — it answers the coder-agent decision "when do I stop hand-writing a prompt string and declare it as a `dspy.Signature`, and how do I name/describe its fields so the optimizer and the calling code both get a clean contract." Activate when: a prompt string grows past ~50 lines; the LM output is consumed by code (parsed, branched on, stored) rather than read by a human; the same prompt is reused across >1 call site; or a teammate asks "should this be a Signature?". Do NOT activate for one-shot throwaway prompts, or for HOW-TO questions about DSPy modules /optimizers/compile — defer those to the [[dspy]] skill and the [[agentsop-dspy]] workflow skill. Search keywords: typed prompt, structured prompt, DSPy Signature, prompt as a function, prompt contract, when to formalize a prompt.
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。
Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.
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。
Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.
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。
Connect to a hospital's FHIR R4 server (Epic, Oracle Health/Cerner, MEDITECH, athenahealth, or any SMART-on-FHIR endpoint), pull a patient's clinical data and notes, and extract structured findings. Use when users say "connect to the EHR", "connect to Epic/Cerner", "pull notes for patient X", "what do the last 6 months of notes say about Y", or any task that starts from a live EHR rather than pasted text.
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 structured reconnaissance and attack surface enumeration for authorized penetration tests, CTF challenges, and bug bounty programs. Use when the user mentions 'recon,' 'reconnaissance,' 'enumerate,' 'attack surface,' 'subdomain enumeration,' 'port scan,' 'fingerprint,' 'asset discovery,' or needs to map a target's external footprint.
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。
Run a structured threat-modeling session for a new feature, system, or architecture — STRIDE, attack trees, data flow diagrams, abuse cases. Use when the user mentions 'threat model,' 'threat modeling,' 'STRIDE,' 'attack tree,' 'abuse case,' 'data flow diagram,' 'DFD,' 'security architecture review,' 'security review,' 'design review,' 'pre-implementation security,' 'shift left,' 'what could go wrong,' or needs strategic security thinking before code is written.
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 fact-checking workflow. Use when verifying claims for publication, rating claims, or setting pre-publication checks.
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 trade logging, performance review, behavioral pattern detection, and strategy attribution for systematic improvement
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。
Survey the codebase for analogous features, reusable utilities, and existing patterns relevant to a proposed change, reading any source the task names outside this repo. Returns structured findings without writing code. Use when the user asks to "survey patterns", "find existing patterns", "look for analogous features", "check how similar things are done", "find prior art for this change", or needs pattern context before planning 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。
Analyze a codebase and produce a structured threat model at .turbo/threat-model.md covering assets, trust boundaries, attack surfaces with existing mitigations, attacker stories, and calibrated severity. Use when the user asks to "create a threat model", "threat model", "threat model this codebase", "security analysis", "analyze the attack surface", "what are the threats", or "identify security risks".
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 what changed and generate a structured test plan at .turbo/test-plans/<slug>.md covering four escalating levels: basic functionality, complex operations, adversarial testing, and cross-cutting scenarios. Use when the user asks to "create a test plan", "plan tests", "what should I test", "generate test scenarios", "test plan for this PR", or "what are the test cases".
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 package managers and CI action pins, then discover outdated or vulnerable dependencies. Returns structured findings without upgrading. Use when the user asks to "review dependencies", "check for outdated packages", "check dependencies", "scan dependencies", "dependency review", "check for outdated GitHub Actions", or "are my workflow actions up to date".
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 code review through the shared OpenClaw agent-skills installation.
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 for social science academic paper drafting, outlining, diagnosis, review, and revision. Trigger for topic diagnosis, research question refinement, literature review planning, CNKI/Google Scholar literature search-to-Zotero workflows, structured paper reading notes, theoretical framework design, research design critique, draft review, section or paragraph revision, citation and evidence risk checking, causal-claim checking, and pre-submission review. Do not use for purely creative writing or non-academic editing.
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 structured planning documentation for web projects with context-safe phases, verification criteria, and exit conditions. Creates IMPLEMENTATION_PHASES.md plus conditional docs (DATABASE_SCHEMA, API_ENDPOINTS, UI_COMPONENTS, CRITICAL_WORKFLOWS). Use when: starting new Cloudflare Workers/React projects, adding major features to existing apps, breaking large work into manageable phases, or need verified planning before coding begins.
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。
Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.
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。