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概要と使いどころ
Email infrastructure security audit. Tests SPF, DKIM, DMARC configuration, open relay, email spoofing resilience, S/MIME, MTA-STS, TLS-RPT, and SMTP security. Uses swaks, dnsrecon, nmap SMTP scripts, smtp-user-enum, and dig. Pure skill markdown — no new infrastructure needed.
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。
Resend + React Email templates
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。
Edit the Prisma Next data contract — add models, fields, relations, indexes, enums, value objects (composite types), type aliases, namespaces (Postgres schemas), cross-contract foreign keys (cross-space FK), polymorphic types (`@@discriminator` / `@@base`), use extension namespaces (`pgvector.Vector(...)`, `cipherstash.EncryptedString(...)`), wire `prisma-next.config.ts` with `defineConfig` from the `@prisma-next/<target>/config` façade, and run `prisma-next contract emit`. Use for schema, models, fields, attributes, soft delete, paranoid, scopes, validations, callbacks, prisma schema, PSL, contract.prisma, contract.ts, contract.json, contract.d.ts, façade imports, `@prisma-next/postgres/config`, `@prisma-next/postgres/contract-builder`, `@prisma-next/postgres/control`, `@prisma-next/mongo/config`, `@prisma-next/mongo/contract-builder`, `extensions:`, `extensionPacks`, pgvector, cipherstash, postgis, paradedb, supabase, `@prisma-next/extension-supabase`, `@@control`, control policy, managed, tolerated, external, observed, PN-CLI-4002, PN-CLI-4003, PN-CLI-4011.
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 Rust-compiled malware by detecting the Rust toolchain signature, demangling Rust v0/legacy symbol names, and identifying crate dependencies from embedded paths. Activates for requests to analyze Rust malware, demangle Rust symbols, or identify a Rust binary build and its crates.
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。
Master professional email — inbox zero systems, high-impact writing, etiquette, and managing email at scale.
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 SEO operations for B2B SaaS organic visibility. Use this skill when you need keyword research, technical SEO audits, content optimization, link building strategy, international expansion, AI/AEO optimization, schema markup, Core Web Vitals improvements, and organic traffic growth planning. Also triggers on: SEO, keyword research, can we rank for this keyword, which keywords are actually worth it, qualify these keywords, check the SERPs, keyword shortlist, too many keywords, technical SEO, link building, content optimization, international SEO, hreflang, x-default, multilingual site, translated pages not ranking, our German site gets no traffic, language switcher, auto-redirect by country, machine translation SEO, content parity, ccTLD vs subfolder, AI search, AEO, GEO, Core Web Vitals, schema markup, organic traffic, G2 listing, Capterra, TrustRadius, review-platform category page, best software roundup, listicle placement, directory submissions, do we need a Google Business Profile in [country], local SEO with no office, Google Business Profile eligibility, virtual office address SEO, coworking address business profile, business profile suspended, NAP consistency, local citations for SaaS, LocalBusiness schema, server log analysis, log file analysis, what is Googlebot crawling, is Googlebot hitting errors, verify Googlebot, fake Googlebot, crawl budget, do we have a crawl budget problem, Crawl Stats report, new pages not getting crawled.
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。
Unity 6 Cinemachine camera system guide. Use when working with virtual cameras, camera blending, follow cameras, FreeLook, camera shake, or state-driven cameras. Covers Cinemachine 3.x API. Based on Unity 6.3 LTS documentation.
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。
Define Malli schemas for commands, events, and queries using defschemas
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 Crematory Operator — running a cremation cycle safely, verifying chain-of-custody and identification before charging a retort, handling implanted-device or oversized-decedent hazards, or documenting and troubleshooting an incident during processing.
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。
Analyze existing KiCad projects, schematics, PCB layouts, Gerbers, netlists, and PDF reference schematics. Use when files such as .kicad_sch, .kicad_pcb, .kicad_pro, or Gerbers are supplied and the user wants review, debugging, net tracing, ERC/DRC/DFM, schematic-to-PCB comparison, or pre-fabrication checks. Do not use to create a new design from requirements or for standalone sourcing and 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。
Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining 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。
Read and write Buttondown: list subscribers and emails, add subscribers, draft emails. Trigger phrases: buttondown, newsletter subscribers, email list.
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。
Close the loop on a Caveman learn report — review the ranked token sinks and apply cost-lowering fixes (trim config, offload recurring context to cavemem) with per-edit consent. Use when the user runs "caveman learn", asks to lower their agent's token cost, wants to trim a heavy CLAUDE.md, or wants to offload context they re-paste every session into cavemem.
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。
Inspect Caveman Cloud's eval-gated experiment lifecycle and block unsafe execution. Use when the user asks to start, approve, cancel, promote, or roll back a Caveman experiment, or asks what action an experiment's evidence supports. Read evidence first; do not execute lifecycle mutations until server-authoritative transition and evidence gates ship.
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。
Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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。
Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman-compress FILEPATH or "compress memory file"
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 configuring standard Salesforce Quotes, building quote templates for PDF generation, emailing quotes, syncing quotes to opportunity products, or setting up discount approvals on quotes. Triggers: 'create quote', 'quote template', 'quote PDF', 'email quote', 'quote sync', 'synced quote', 'discount approval', 'quote line items', 'QuoteSettings', 'enableQuote', 'QuoteStatus', 'allowEmail', 'SyncedQuoteId', 'IsSyncing', 'QuoteLineItem', 'QuoteDocument', 'GrandTotal', 'Pricebook2Id'. NOT for CPQ (SBQQ) quote templates — use admin/cpq-quote-templates. NOT for the end-to-end quote-to-cash process — use admin/quote-to-cash-requirements.
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 setting up, configuring, or troubleshooting Salesforce Sales Engagement (formerly High Velocity Sales) cadences, call scripts, email templates, work queue, or rep assignment. Triggers: 'cadence builder', 'HVS cadence', 'work queue steps', 'sales engagement setup', 'sequence steps not appearing', 'cadence not sending email', 'Sales Engagement permission set'. NOT for cadence enrollment from Apex — use apex/sales-engagement-api. NOT for MCAE/Pardot nurture — use admin/lead-nurture-journey-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。
Configure SEO for Experience Cloud sites: per-page titles and meta descriptions, sitemap.xml generation, robots.txt, and URL structure. Triggers: 'configure SEO for community site', 'Experience Cloud page titles meta descriptions', 'sitemap generation community portal', 'robots.txt Experience Cloud', 'noindex Experience Builder page'. NOT for creating the site, LWR vs Aura, or custom domain setup — use admin/experience-cloud-site-setup. NOT for pages a crawler cannot reach because the guest user lacks access — use admin/experience-cloud-guest-access.
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。
Configure Einstein Activity Capture (EAC) for an org: enable the feature, create Configuration profiles, set auth type (User-Level or Org-Level OAuth), define email and calendar sync scope, excluded domains and privacy settings, assign users, verify sync health. NOT for querying synced emails or events in SOQL, Apex or reports — use apex/einstein-activity-capture-api. NOT for Opportunity Scoring or Pipeline Inspection — use agentforce/einstein-copilot-for-sales.
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。
The model-generator shape for Skmtc: one definition per component schema, built by copying the shipped SKELETON package and filling its SLOT markers with the target library's syntax. Covers the edge cases every model generator must survive — refs, recursion, optional vs nullable, additionalProperties, enums, readOnly/writeOnly. Use when authoring or editing a generator that maps schemas to a validator/schema/type library ("write a gen-<lib>", "map OpenAPI models to <lib>"). Load ALONGSIDE skmtc-generator (engine rules) and skmtc-lang-typescript (TS layer).
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 the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure 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。