「pip」の検索結果

Design document processing pipelines for extraction and routing. TRIGGERS - Use when user needs help with ai-document-processor related tasks.

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 AI audio processing pipelines. TRIGGERS - Use when user needs help with ai-audio-processor related tasks.

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 invoice data and generate a formatted PDF summary in a single pipeline.

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。

Resize an image, add a branded text watermark, and convert to WebP in a two-step pipeline.

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 a document to markdown and classify it with an LLM in a single pipeline.

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 data from multiple invoice PDFs in one call, then pipe the results directly into an XLSX accounts payable tracker.

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 a document to clean markdown suitable for chunking and embedding in a RAG pipeline.

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 a resume PDF to clean markdown for LLM parsing or candidate pipelines.

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 PDF, DOCX, HTML, or image documents to clean, structured Markdown.

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。

Resize, sharpen, and compress an image to fit email platform size limits in a single pipeline.

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 a PDF invoice to clean markdown for LLM processing or document pipelines.

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 line-level charges from multiple carrier invoices, then generate an XLSX freight cost tracker in a single pipeline.

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Runs standard-format scenarios through ScenarioRunner — ASAM OpenSCENARIO 1.x (.xosc, via --openscenario, with catalogs and ParameterDeclaration overrides) and OpenSCENARIO 2.0 (.osc, via --openscenario2, the ANTLR/osc2 pipeline). Validates a file against the schema before running, lists the bundled examples, and reports which OSC features ScenarioRunner actually implements. Use when the user asks to "run an xosc", "run an OpenSCENARIO file", "use the OpenSCENARIO standard", "run a .osc / OSC2 scenario", or hands over a scenario file from another tool.

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Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types. Use when creating agents in source control, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship'. NOT for Apex-based agent actions (use custom-agent-actions-apex). NOT for UI-based agent creation (use agentforce-agent-creation).

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age file encryption reference — the modern, simple alternative to GPG. Covers key generation, X25519 encryption, SSH key support, passphrase mode, pipe patterns, SOPS integration, YubiKey plugins, and security considerations. By Filippo Valsorda.

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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

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Use when working with access policies, field access, authorization middleware, gates, route access control, AccessResult semantics, permission handlers, or files in packages/access/, packages/routing/src/AccessChecker.php, packages/user/src/Middleware/. Covers entity-level access (deny-by-default via isAllowed), field-level access (open-by-default via !isForbidden), the Gate system, route options (_public, _permission, _role, _gate), and the SessionMiddleware/AuthorizationMiddleware pipeline in public/index.php.

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Security-Scanner Agent fuer fabrikIQ und andere Projekte. Fuehrt umfassende Sicherheitspruefungen durch. Trigger bei: (1) Pre-commit Validation, (2) PR-Creation, (3) Weekly Security Audits, (4) Nach Dependency-Updates, (5) Vor Production Deployments. <example> Context: User moechte vor einem Commit sicherstellen, dass keine Secrets committed werden. user: "Ich will gleich committen. Pruefe mal auf Secrets." assistant: "Ich starte den security-scanner Agent, um den Code auf API Keys, Tokens und andere Secrets zu pruefen." <Task tool call to security-scanner agent> </example> <example> Context: Weekly Security Audit des fabrikIQ-Projekts. user: "Fuehre den woechentlichen Security-Scan durch." assistant: "Ich fuehre einen vollstaendigen Security-Scan durch: Secret Detection, Dependency Audit, OWASP Patterns, Security Headers und Rate Limiting." <Task tool call to security-scanner agent> </example> <example> Context: User hat Dependencies aktualisiert und will Vulnerabilities pruefen. user: "Ich habe npm update und pip install -U gemacht. Gibt es Sicherheitsprobleme?" assistant: "Ich scanne die aktualisierten Dependencies auf bekannte Vulnerabilities mit dem security-scanner Agent." <Task tool call to security-scanner agent> </example> <example> Context: PR-Review mit Security-Fokus. user: "Review den PR #42 auf Security-Probleme." assistant: "Ich analysiere die geaenderten Dateien im PR auf Security-Patterns: SQL Injection, XSS, Hardcoded Credentials und fehlende Input-Validation." <Task tool call to security-scanner agent> </example>

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Full pipeline: evaluation -> spec -> implementation -> review -> QA

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Official HeyGen plugin for OpenClaw — adds HeyGen as a first-class provider for the built-in video_generate tool. Identity-first avatar videos: pick an avatar_id, a voice, a script, and get an on-brand presenter video back. Use when: (1) generating a video where a specific avatar/person speaks specific lines, (2) "make a video of me / our brand presenter", (3) outreach, announcements, pitch decks, product walkthroughs, or any presenter-led video, (4) needing landscape or portrait output with deterministic aspect ratios, (5) integrating HeyGen into an OpenClaw agent through the standard provider interface (no bespoke REST glue required). Covers: HeyGen v3 Video Agent (api.heygen.com), avatar-presenter video, identity-preserving video, talking-head AI video, scripted avatar narration, brand spokesperson video. NOT for: cinematic b-roll (use Veo / Runway / Kling), video translation, TTS-only, streaming avatars, or photo-to-talking-head v2 pipelines.

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Build kokoro-js-jp and run its real-browser e2e suite (Worker + WASM openjtalkjs g2p + ONNX kokoro-js synthesis, English and Japanese). Use when asked to verify this package actually works in a browser, to check whether a change broke synthesis, or to add browser-level regression coverage for a bug in the g2p/synthesis pipeline that vitest's pure-function tests can't catch. Not for typecheck/lint/unit-test requests — use npm run typecheck / npm test for those.

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Gate a build on browser fingerprint regressions with liarjs - save a baseline scan as JSON, diff later runs against it, and fail the job when the consistency score falls below a floor. Use when asked to add a fingerprint or headless-detection check to GitHub Actions, GitLab CI or another pipeline, to catch a regression in a Chromium build or scraping harness before it ships, or to track how a fingerprint score changes across commits.

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Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.

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。