「structured」の検索結果

Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.

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Use when: a general codebase assessment, pre-migration/pre-rewrite review, or explicit security-focused request needs a structured, repeatable security audit pass covering hardcoded secrets, injection-prone constructs, auth/debug backdoors, and insecure config defaults. Complements (does not replace) the built-in /security-review command. Not a mandatory gate on every ticket.

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Use when: 'intake this repo/skill', 'evaluate external skill', 'should we adopt X', a candidate repo URL plus an adoption question, or any external skill/workflow/tool surfaces as a possible fit for Agistra. Structured front-door evaluation of external skills, workflows, and similar capability artifacts before any install, promotion, or adaptation decision. Ends in exactly one classification: Install candidate, Adapt concept, or Reject. Never install first — intake first.

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When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.

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Structured task planning with clear breakdowns, dependencies, and verification criteria. Use when implementing features, refactoring, or any multi-step work.

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 the user asks to "plan a penetration test", "create a security assessment checklist", "prepare for penetration testing", "define pentest scope", "follow security testing best practices", or needs a structured methodology for penetration testing engagements.

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 skill when a design or idea requires higher confidence, risk reduction, or formal review. This skill orchestrates a structured, sequential multi-agent design review where each agent has a strict, non-overlapping role. It prevents blind spots, false confidence, and premature convergence.

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。

CLI tool for AI-powered web scraping, data extraction, search, and crawling via ScrapeGraph AI. Use when the user needs to scrape websites, extract structured data from URLs, convert pages to markdown, crawl multi-page sites, search the web for information, automate browser interactions (login, click, fill forms), get raw HTML, discover sitemaps, or generate JSON schemas. Triggers on tasks involving: (1) extracting data from websites, (2) web scraping or crawling, (3) converting webpages to markdown, (4) AI-powered web search with extraction, (5) browser automation, (6) generating output schemas for scraping. The CLI is just-scrape (npm package just-scrape).

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。

Semantic search, similar content discovery, and structured research using Exa API

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 implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

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 guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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。

Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. 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。

Extract text, tables, and structured data from documents using prebuilt and custom models.

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 AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.

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.

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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.

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 guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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 human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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 LLM outputs validated with Pydantic.

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Uses Postman to perform structured API security testing by building collections that test for OWASP API Security Top 10 vulnerabilities including authentication bypass, authorization flaws, injection, and data exposure. The tester creates environments with multiple user roles, writes test scripts for automated security validation, and integrates Postman with OWASP ZAP and Newman for CI/CD security testing. Activates for requests involving Postman security testing, API security collection, aut...

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Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...

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。

Guide authors through a structured self-review before requesting peer review.

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。

Debug Python code using tracebacks, pdb, and structured logging.

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。