「pipeline」の検索結果

Use when testing file upload endpoints for unrestricted file upload, MIME type bypass, magic byte spoofing, polyglot files, SVG XSS, XXE via Office documents, ZIP slip, and path traversal in filenames. Trigger on: multipart/form-data endpoints, avatar/document upload flows, import-from-file features, profile image, CSV/Excel import, DOCX/XLSX parsing, image resizing pipelines, archive extraction, and any endpoint that stores or serves user-supplied files. Detects extension bypass (shell.php.jpg), null byte injection, double extension, ImageMagick exploits, and content-type confusion.

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Entry P1 category router for file access and upload workflows. Use when testing download endpoints, file paths, local file inclusion, upload flows, preview pipelines, archive extraction, or storage and sharing boundaries.

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。

Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.

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。

Command injection playbook. Use when user input may reach shell commands, process execution, converters, import pipelines, or blind out-of-band command sinks.

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Use this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.

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Task Breakdown Command. Breaks a feature into discrete, dependency-ordered implementation tasks from a PRD and tech spec. Creates a task list and individual task files with no approval prompt. When run inside the `/start-job` pipeline, return control to that orchestrator so it can invoke the implementation phase. When invoked standalone, stop after generating tasks and prompting for the next step. Use when the user says "break this down into tasks", "create tasks", "task breakdown", or after the tech spec is complete and the user wants to plan implementation.

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Top-level orchestrator for Flutter feature specification. Runs the full uninterrupted pipeline: prd → techspec → tasks in one shot. Use when the user says "spec a feature", "write a spec for", "start feature spec", "plan a feature", or wants to go from idea to implementation-ready tasks without typing three separate commands.

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。

Open the warm channel for a role the pipeline already assessed. Finds who the user already knows at the company, researches named targets from public professional presence when they know nobody, grades each path honestly, drafts the ask through the voice agent, and writes the contacts into the Referrals table in job_search.md.

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Read every application.yaml in the workspace and report the state of the search — the ranked queue, what is waiting on you, what has gone quiet, and the regression of the pipeline's own predictions against actual outcomes. Writes the calibration findings back into job_search.md and preferences.yaml.

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Redesign the resume and cover letter templates into your own house style. Changes typography, palette, and layout while holding the ATS constraints fixed, then proves the result still compiles and still extracts. Run any time; not a pipeline stage.

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Run a seven-agent adversarial pipeline against a resume and cover letter before submission. Produces channel-conditional verdicts anchored to the realistic applicant pool, separates materials quality from submission expected value, and ends with a contrarian pass that can overturn the verdict.

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Full instrumentation from day one. PostHog consolidates product analytics + feature flags + error tracking (one platform, one bill). GA4 via GTM (14-step automation, custom dimensions over events, server-side tagging). Sentry (deep error tracking + performance). Stripe (webhook-first with idempotent processing). Listmonk on Coolify (newsletters via Amazon SES SMTP relay). PLG 7-layer framework. Programmatic SEO (5 page types). Incident auto-remediation via Sentry→Inngest pipeline. AI search (GEO) awareness. Local business conversions (phone_click, direction_click, form_submit, booking_click) with CRO patterns for both SaaS and local.

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。

End-to-end AI website generation pipeline. Claude Opus 4.8 emits Bolt-style <boltArtifact> envelopes (multi-file, plan-first) that customize Vite+React+Tailwind templates from pre-researched business data. Pre-research via APIs, media acquisition, brand extraction, visual inspection via GPT Image 2 vision, R2 upload (per-file content-type by extension), D1 status updates. Supports all business types: SaaS, portfolio, non-profit, restaurant, salon, medical, legal, retail, tech.

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Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.

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 writing, reviewing, or changing build scripts, CI workflows, deploy pipelines, repo setup, or evaluating a new tool/dependency

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。

Frappe CRM guidance for leads, deals, contacts, organizations, pipelines, activities, assignment, dashboards, and sales workflows. Use when work touches Frappe CRM or CRM-style sales processes in the Frappe ecosystem.

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。

Design token architecture - primitive/semantic/component tiers, naming grammar, CSS custom property delivery, and DTCG build 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。

URQL GraphQL client patterns — the exchange pipeline, document and normalized caching, queries, mutations, subscriptions, and authentication

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。

Backend specification planning frameworks. Use when a spec touches API endpoints, database schema, middleware, or auth. Covers endpoint contracts with request/response shapes, error catalogs, auth per endpoint, schema design with constraints and indexes, migration strategy, and middleware pipeline ordering.

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。

Nx monorepo build system — workspace configuration, project graph, task pipelines, caching, generators, plugins, and release management

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。

AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, token budgets, retry/timeout handling, streaming, and key/PII exposure.

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。

GitHub Actions, pipelines, deployment

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。

Turborepo CI pipelines with remote caching and affected detection

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。

Upstash serverless Redis -- REST-based client, auto-serialization, pipelines, rate limiting, QStash, edge compatibility, global replication

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。