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概要と使いどころ
Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.
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。
Keep a plugin/demo repo's screenshots in sync with its UX. When editor / UI / design-token code changes in an opted-in repo (one carrying .pulp/screenshots.toml), re-capture and refresh every place each shot is consumed — README images, web-demo Open Graph (og.png) images, and gallery thumbnails — consistently across the WAM and WCLAP demo sites. Two capture routes: native headless (render_to_png / pulp-screenshot, Skia) for README / native-editor shots, and live-web headless Chrome (playwright-core) for WAM/WCLAP demo OG + gallery images. Backfills demos missing OG images. This is a PORTING / consistency tool — it re-shoots a repo's OWN UI, it never clones another product's art.
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 HTML emails that render everywhere: table layouts, MJML, dark mode, testing, and deliverability basics. Use for transactional and marketing email.
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 LLM context windows deliberately — budgeting, prioritization, summarization, and retrieval instead of dumping everything in. Use when prompts grow long, costs rise, or the model loses track of key details.
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 and apply brand guidelines — voice, visual identity, and usage rules that keep every touchpoint consistent.
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。
Email marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup — plus the contact budget: a cross-channel frequency cap across every sender (marketing, sales sequences, customer success, in-app). Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate, BIMI, brand logo in inbox, VMC, Verified Mark Certificate, Common Mark Certificate, transactional email, password reset emails going to spam, DMARCbis, RFC 9989, DMARC pct tag, DMARC t=y test mode, DMARC rollout, move DMARC to reject, frequency cap, email fatigue, customers get too many emails, too many messages, sales and marketing both emailing the same person, communication limits, send frequency safeguard, global frequency capping, quiet period, message precedence.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM 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。
Convert Markdown (.md) files to a polished PDF with ALL images preserved and scaled to the page. Use whenever the user asks to "save this markdown as a PDF", "convert README.md to pdf", "export the .md as a pdf", "turn these notes/docs into a PDF", or wants a PDF deliverable of any GitHub-flavored Markdown document (README, design doc, report, notes) — especially when it contains images or diagrams (remote or local), tables, code blocks, or a linked table of contents. Pipeline: pandoc (GFM → standalone HTML with embedded resources) → headless Chrome print-to-pdf with a GitHub-like print stylesheet → pypdf verification that every referenced image is embedded. Do NOT use for .tex → PDF (use a LaTeX toolchain) or for .docx work (use word-docx / tex2docx).
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。
N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.
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。
Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib 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。
Use this skill whenever the user asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.
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。
Validates completeness and consistency of the project architecture against all GDDs. Builds a traceability matrix mapping every GDD technical requirement to ADRs, identifies coverage gaps, detects cross-ADR conflicts, verifies engine compatibility consistency across all decisions, and produces a PASS/CONCERNS/FAIL verdict. The architecture equivalent of /design-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。
Creates a structured bug report from a description, or analyzes code to identify potential bugs. Ensures every bug report has full reproduction steps, severity assessment, and context.
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。
Creates an Architecture Decision Record (ADR) documenting a significant technical decision, its context, alternatives considered, and consequences. Every major technical choice should have an ADR.
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。
Complete launch readiness validation covering every department: code, content, store, marketing, community, infrastructure, legal, and go/no-go sign-offs.
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 a task needs the judgment of an Electrical and Electronic Equipment Assembler — inspecting a solder joint against the specific IPC/J-STD workmanship standard criteria rather than personal visual judgment, following ESD precautions for every component handled since damage is invisible at the time it occurs, verifying a crimped connection with a pull test rather than visual inspection alone, or treating a single suspected cold joint as a potential complete failure point rather than a minor defect.
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 a task needs the judgment of a Conveyor Operator — diagnosing a mistracking belt before it becomes edge damage or spillage, verifying every lockout/tagout isolation point on a conveyor with a gravity take-up or incline before entering the nip zone, calculating each conveyor's actual delivered capacity in a multi-conveyor line to find the true bottleneck, deciding whether a mechanical or vulcanized splice fits a given tension rating, or judging whether a pinch-point guarding gap needs an immediate stop versus a scheduled fix.
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。
Flagship comprehensive SEO audit combining Ahrefs and GSC data in sequential waves with checkpoint saves. Use when user says "site audit pro", "full audit", "comprehensive audit", "audit with live data", "deep audit", "pro audit", "complete SEO audit", "run a full site audit", or "audit everything for this domain". Requires domain param. Optional: site param for GSC overlay.
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 Lighthouse audit against any URL via the local Lighthouse CLI or the PageSpeed Insights API fallback, parse category scores and failed audits, and map every failed audit ID to its official Chrome docs page plus a one-line fix. Use when user says "lighthouse", "lighthouse audit", "run lighthouse", "lighthouse score", or "pagespeed".
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。
List, select, and install Volcengine skills from the volcengine-skills marketplace. Use when a user asks which Volcengine skill or plugin handles a task, wants to browse every available skill, needs an optional skill that is not currently installed, or asks to install or check the installation status of a Volcengine skill. Also use to discover any core skill, including this finder. Use as a fallback during a Volcengine operation when the currently loaded or installed skills cannot cover the required product, tool, or workflow; inspect the full catalog, select the missing capability, and install it.
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 for this hackathon's channel template in apps/channel: CopilotKit Channels setup, thread context, tools, and native cards. Read before editing that app. This skill does not apply to the CopilotKit React web template or the React Native mobile app.
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 this after every code change in this Vue project, before committing, before opening a PR, or whenever the user says "verify", "check", "lint", "run tests", "build", "audit". Diff audit (router guards, store boundaries, UX, /ui consistency, error handling) → lint → tests + coverage gate → build. Coverage drops → add tests, never lower thresholds. Triggers on "is this ready to commit?", "any issues?", "tests passing?".
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。
Pin-to-net mapping auditor -- verifies that every IC pin in the schematic is connected to the correct net, matched against a pin map source of truth (CSV, datasheet table, or Python dict). Critical for BGA/QFN devices where swap errors are invisible to DRC/ERC.
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。
Full-stack app pipeline — build, test, review, and deploy across one or ALL repos. Scans CLAUDE.md, MEMORY.md, TODOs, open PRs, and issues for shippable work. Supports 'ship all' for multi-repo. Use when: 'build and ship', 'ship it', 'ship all', 'deploy everything', 'full pipeline', 'what needs shipping', 'end to end'.
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。