「generating」の検索結果

Burn timed, styled captions into a finished video on the local machine, without re-rendering the source. This is the manual fallback to the API's captioning endpoint, POST /v1/captions, which stays the default path: come here when the style the user wants is outside the API's presets, when the wording has to be hand-corrected before it is burned into the pixels, or when the source was rendered with audio disabled and the API refuses it. Transcribes the audio with Whisper, groups words into reading phrases, renders the captions in a real browser via HyperFrames, and composites them over the source with ffmpeg using a real alpha channel. Use when the user has an MP4 with speech and asks to add captions, subtitles, burned-in text, TikTok-style captions or word-by-word text to it, and the API path has been ruled out. Runs out of band, so it costs no credits and works on any video file whoever generated it. Not for generating video (use the video skills) and not for a separate .srt/.vtt sidecar file.

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Publish finished creatives as live Meta (Facebook/Instagram) ads via the Meta Marketing API, plus research and ad-copy support. Uploads an image or video, builds a multi-variant creative (5 bodies / 5 titles / 3 descriptions in an asset feed), and creates a PAUSED ad in an existing ad set. Also pulls top-performing ads (ranked by ROAS) and competitor ads from the Ad Library to inform copy. Use when the user asks to deploy / publish / launch a creative as a Meta or Facebook ad, build a Meta ad, push a video or image into an ad set, pull their top ads, or research competitor ads. Not for generating creative (use the image/video skills) and not for writing ad-table rows.

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Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.

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。

Automates IDA Pro analysis with IDAPython by generating scripts that enumerate functions, decode strings, rename by signature, and export analysis results, and by documenting the headless idat batch invocation. Activates for requests to script IDA Pro, write IDAPython automation, or run IDA in batch/headless mode.

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。

Automates batch reverse engineering by generating Ghidra analyzeHeadless commands and post-analysis script skeletons to import, auto-analyze, and export results for many samples without the GUI. Activates for requests to script Ghidra headless analysis, batch-analyze samples with Ghidra, or generate an analyzeHeadless command and post-script.

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Uses capa to identify malware capabilities from a binary: running rule-based detection over disassembly, mapping matched capabilities to MITRE ATT&CK and MBC, and interpreting results to focus deeper analysis. Activates for requests to run capa, generate a capability report, or map a sample's behaviors to ATT&CK automatically.

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。

Inject juice and game feel into gameplay code using the project's chosen middleware. Use when adding screen shake, tweens, particles, audio feedback, haptics, hitstop, or any form of "juice" to gameplay events. Triggers on requests like "add juice", "make this feel better", "add screen shake", "polish this feature", "add feedback to this action", "make the hit feel impactful", "add particles when", "screen shake on damage", "tween this", "add camera shake", "make this snappy", or any game feel, polish, or feedback work. Always reads ProjectConfig.yaml -> feel_tools and docs/GFD.md Feedback Matrix before generating code.

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。

Generates networking boilerplate adapted to the project's chosen networking package (NGO, Mirror, Photon). Use when setting up multiplayer systems, syncing state, writing RPCs, building lobbies, or designing an authority matrix. Triggers on requests like "add multiplayer", "sync player health", "create an RPC", "set up lobbies", "make this networked", "add co-op", "replicate this across clients", "spawn networked objects", "handle player disconnect", "create a matchmaking system", or any task involving network state synchronization, remote procedure calls, or client-server architecture. Always reads ProjectConfig.yaml -> networking before generating code. If networking is "none", do NOT generate networking code.

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 generating or editing diagrams, charts, visual assets, posters, UI mockups, product images, infographics, academic figures, comics, avatars, storyboards, brand boards, or image-edit workflows, especially when exported PNGs need visual QA and bounded self-repair. Prefer deterministic Mermaid/SVG/HTML/canvas sources for text-heavy diagrams; use HTML technical diagrams for browser-ready system blueprints, architecture, deployment topology, agent runtime, memory flow, before-after architecture, workflow, sequence, data-flow, lifecycle, runbook, PII/data-lineage, and state-machine diagrams. Do not use for ordinary UI polish without generated imagery.

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Use when a task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search, choosing or building a genomics pipeline (aligner, caller, normalization method) for a specific data-generating process, or judging whether a computational-biology analysis's statistical design actually supports the biological claim being made. Distinct from a bioinformatics-technician (executes and QC-gates an established pipeline someone else designed) and a data-scientist (general product/experimentation judgment, not genomics-specific statistics) — this role owns which method and statistical model a genomics question requires, and why.

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Use when a task needs the judgment of a billing and posting clerk — generating an invoice against a rate/contract schedule, applying an incoming payment across multiple open invoices, processing a credit memo or adjustment, or investigating an aging-report discrepancy.

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。

Execute ENCODE RNA-seq pipeline from FASTQ to gene quantification and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing RNA-seq data with STAR alignment, RSEM/Kallisto quantification, or generating expression matrices. Trigger on: RNA-seq pipeline, gene expression, STAR alignment, RSEM quantification, transcript quantification, TPM, FPKM, RNA processing, run RNA-seq.

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Execute ENCODE Hi-C pipeline from FASTQ to contact matrices and loop calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing Hi-C data, generating contact matrices, or calling loops. Trigger on: Hi-C pipeline, chromatin conformation, contact matrix, loop calling, TAD detection, Juicer, HiCCUPS, 3D genome.

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Execute ENCODE Whole Genome Bisulfite Sequencing (WGBS) pipeline from FASTQ to methylation calls. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing WGBS/bisulfite-seq data, calling methylation levels, generating bedMethyl files. Trigger on: WGBS pipeline, bisulfite sequencing, methylation calling, DNA methylation pipeline, bismark, bwa-meth, bedMethyl.

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。

Analyzes git commit history and generates professional changelogs with semantic versioning, conventional commit support, and multiple output formats (Keep a Changelog, Conventional, GitHub). Use when editing CHANGELOG.md, CHANGELOG.txt, or HISTORY.md files, preparing release notes, creating releases, bumping versions, updating changelog, documenting changes, writing release notes, tracking changes, version bump, tag release, or when user mentions "changelog", "release notes", "version history", "release", "semantic versioning", or "conventional commits".

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。

Generates Stitch screens from authored prompt files using MCP tools. Reads prompt sections, sends each to Stitch for generation, and fetches resulting images and code. Use when user mentions "generate in stitch", "create stitch screens", "run prompts in stitch", "send prompts to stitch", "generate screens from prompts", "fetch stitch images", or has prompt-v*.md files ready for generation. Requires Stitch MCP server.

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 building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.

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。

EPUB ebook creation and processing — structure, metadata, validation — use when generating or parsing ebooks.

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。

Word document automation — generating, templating, and parsing .docx files — use when working with Word documents programmatically.

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 differential diagnosis — generating, prioritizing, and ruling out diagnostic hypotheses.

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, score, and shortlist brand names through a guided interview. Use when the user says "name my brand", "help me name", "come up with a name", "naming ideas", "what should I call this", "brand name", "product name", "company name", "rename", "rebrand", or is stuck choosing between names. Self-contained and brand-agnostic: it does not read knowledge/, so it works for naming a brand-new venture, product, service, feature, or rebrand from scratch, before any brand context exists. Runs on Alexandra Watkins' SMILE and SCRATCH tests to generate and filter, Marty Neumeier's 7 criteria to score, and the Lexicon Branding method for sound symbolism and strategy. Asks the user questions before generating. For the positioning the name has to carry, see positioning-doc. For the message architecture built on top of it, see messaging-framework.

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 interactive "living workflow" flowcharts in the visual style of https://statuesque-starship-49757d.netlify.app/revenue-engine - vanilla HTML/CSS/JS files with a horizontal step bar, a vertical trunk that grows per step, tool-to-tool columns with animated payload pills between brand-marked tool nodes, alternating left/right step descriptions, and per-step outputs. Use whenever the user says "build a flowchart", "interactive flowchart", "living workflow", "revenue engine flowchart", "demand gen flowchart", "GTM workflow diagram", "process diagram with animations", "Nebor-style flowchart", or asks to recreate the workflow visual from the Nebor site. Always asks which flowchart to build (Revenue Engine / Demand Gen / CRM & RevOps / Custom) and waits for explicit approval of the step + tool + edge table before generating any files.

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Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.

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Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).

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。