3,550 件 ・ 関連度順
概要と使いどころ
End-to-end R data analysis pipeline — exploration → cleaning → regression → publication-ready tables and figures. Use when user says "analyze this dataset", "run a regression on X", "explore this CSV", "full analysis workflow", "get me summary stats and a regression", or points at a `.csv`/`.rds`/`.dta` and asks for empirical results. Produces numbered R scripts in `scripts/R/` and outputs to `output/`.
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。
Before and after changing anything shared — a function's return value, a signature, a schema, a label set, a config default, a constant, a file format — find every consumer and actually run them. Catches the change that looks purely additive but silently breaks a contract in a file you never opened. Use when editing shared code, adding a field/column/return element, renaming, changing units or defaults, or touching a pipeline that produces reported numbers.
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。
Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.
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。
提取微信公众号文章全文。Use when the user provides a mp.weixin.qq.com URL and asks for 提取文章, 抓取公众号, 读一下这篇, or when another skill (ra-洗稿, ra-选题, ra-video-wash-pipeline) needs to read a WeChat article before processing. Uses MicroMessenger UA spoofing to bypass WeChat's first-layer access control. Stdlib only, no API key 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。
实操长片策划工作流（B站/YouTube 新赛道，实操+真人出镜）。产出可直接照着录的策划稿：测试题组（含可粘贴 prompt）、 结构时间轴（全身出镜段 vs PiP 段）、出镜口播稿、录屏操作清单、悬念与彩蛋设计。 Use when the user says 做实操长片, 实操视频, 实测视频, 对比实测, xx做成实操, 出个实测策划, 这个选题拍实操, or ra-选题 routes a 实操长片 立项 here. 策划稿落 `01-内容生产/视频工作台/制作中/<日期-主题>/策划稿.md`，frontmatter status 是与制作端交接的唯一状态机。
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。
视频脚本洗稿和二创脚本工作流。Use when the user provides an existing transcript, oral draft, viral script, competitor video text, rough topic notes, or wants 洗稿, 二创改写, 爆款视频脚本重写, 逐字稿改成短视频脚本, rewrite with ra-人话, and queue the approved script into 01-内容生产/视频工作台/待制作/ as the handoff contract for video production. If the user provides a video URL or local video file and asks 视频洗稿、视频二创、洗稿并制作视频, use ra-video-wash-pipeline first.
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。
When throughput or latency is pipeline-limited, identify the single binding constraint and exploit, subordinate, elevate, then recheck—ignore non-constraints.
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。
Agent 1 of the video editing pipeline. Removes silences and dead air from a talking-head recording. Use when asked to cut silences, trim pauses, remove dead air / gaps, or tighten the pacing of a raw video, given a word-level transcript. Produces an edit list (EDL), a re-timed transcript for downstream agents, and optionally the cut video via ffmpeg. Does NOT cut mistakes, repeats, or false starts — that is the cut-mistakes agent.
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。
Agent 2 of the video editing pipeline. Finds and removes spoken mistakes — stutters, repeated words, false starts, and retakes (re-recorded lines) — from a talking-head recording. Use after cut-silences, when asked to cut mistakes, remove stutters / repeats / filler restarts, clean up flubs, or keep the best take. Works review-gated: it proposes every cut with context and a reason for approval, then renders only the approved cuts via ffmpeg. Requires a word-level transcript.
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 force-robust Stormbreaker loop — route to real agents, execute a verified pipeline to completion.
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 types /hep-storm, mentions @Hephaestus storm, or asks to force-robustly drive a loop-worthy goal (apps, sites, agents, automations, debugging, multi-step research, data/report generation) to a verified finish. Stormbreaker routes the goal to real Agentlas specialists, materializes a dependency-ordered pipeline fabric, and runs a verifier-first loop that does not stall, run away, or claim false success. Trivial questions are answered directly, not stormed.
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 types /hep-storm or /agentlas-storm, says @Hephaestus storm <goal>, or asks to drive a goal to verified completion through a force-robust Stormbreaker loop. Stormbreaker routes the goal to real Agentlas specialists, materializes a dependency-ordered pipeline fabric, and runs each work packet as a verifier-first hardened loop that does not stall, run away, or claim false success. Use it for loop-worthy work — apps, sites, agents, automations, debugging, multi-step research, data/report generation. Trivial questions are answered directly, not stormed.
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 force-robust Stormbreaker loop — route to real agents, execute a verified pipeline to completion.
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。
Publish or update a game on Steam with Steamworks and SteamPipe: configure depots and packages, upload builds with steamcmd, set a build live on a branch, and run the release checklists. Use for Steam publishing, app_build.vdf/steamcmd uploads, depots, beta branches, or a store page release.
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。
Write game shaders from cross-engine fundamentals — the vertex→fragment pipeline, coordinate spaces, UV math, and common 2D/3D effects (tint, UV scroll, dissolve, outline, fresnel rim, vignette) in GLSL with HLSL equivalents. Use when the user mentions shaders, fragment/pixel shader, vertex shader, UV, GLSL, HLSL, or effects like dissolve, outline, or rim light.
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。
Maintenance orchestrator for the brooks-lint plugin itself. Runs a sequential subagent pipeline — author → eval → QA → trigger-audit → release — to add or edit a skill, refresh the eval suite, keep the four manifests + all README translations + CHANGELOG + AGENTS/GEMINI in sync, audit trigger boundaries, and cut releases. Drives the five agents in .claude/agents/ (skill-author, eval-curator, consistency-qa, trigger-boundary-auditor, release-manager). Triggers when the maintainer asks to work ON brooks-lint itself: "add a new skill", "edit the brooks-debt guide", "update the eval suite", "fix the trigger descriptions", "make this change and validate it", "release brooks-lint", "bump and publish", and follow-ups: "re-run", "re-validate", "update that skill", "redo the audit", "do the X part again". Do NOT trigger for: USING the brooks-lint analysis skills on some target codebase (that's brooks-review / brooks-audit / brooks-debt / brooks-test / brooks-health / brooks-sweep); generic questions about brooks-lint that don't ask to change it; or maintenance of a different plugin.
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。
OWASP CI/CD Top 10 knowledge base for identifying, assessing, and remediating CI/CD pipeline 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。
Manage GitLab merge requests and pipelines with a Python CLI
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 customer-card PowerPoint content YAML from Design Thinking canonical artifacts and build using the shared PowerPoint skill 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。
Text-to-speech voice-over generation from YAML speaker notes using Azure Speech SDK with SSML pronunciation control, or an offline Piper engine that needs no credentials
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 all reference benchmarks under benchmarks/eval/ and refresh the reference-table cells in benchmark_*.py. Auto-detects host hardware (H200/H100/H800/...). For each row's matching hw entry: replace cells in place. For new hardware or a new workload tag: append a new row to the section's table. Local-Pipeline-Result tables are skipped. One slash command runs everything end-to-end and auto-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。
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-discovery, write-judge-prompt, or validate-evaluator instead).
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。
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
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。
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
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。