Map functional, emotional, and social jobs with outcome expectations. Use when reframing decisions around motivation rather than features. For who the user is, use `user-persona`.
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。
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概要と使いどころ
Map functional, emotional, and social jobs with outcome expectations. Use when reframing decisions around motivation rather than features. For who the user is, use `user-persona`.
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 research-grounded personas with goals, frustrations, and behavioural patterns. Use when decisions need a consistent user reference. For one session's emotional snapshot use `empathy-map`; for motivation framing use `jobs-to-be-done`.
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。
Apply Hick's Law — decision time grows with the number of simultaneous choices. Use when a screen offers too many options at once. For how many items survive in memory afterwards, use `millers-law`.
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 area of the codebase and propose the smallest structural moves that improve it - untangle boundaries, kill duplication, fix seams, break cycles. Produces a prioritized plan and decision records, not a rewrite. Use when a codebase feels tangled, hard to change, or is becoming a ball of mud, or when asked to improve or refactor architecture.
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。
Score every decision point with a Thoroughness Rating (1-10). AI makes the marginal cost of doing things properly near-zero — pick the higher-rated option every time. Includes scope checks to distinguish contained vs unbounded work.
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 a structured handoff document capturing current progress, open tasks, key decisions, and context needed to resume work. Use when ending a session, saying "continue later", "save progress", "session summary", or "pick up where I left off".
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。
Stress-test a plan by walking its decision tree one question at a time. Use when the user wants to pressure-test a design before implementation.
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 the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages.
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。
Migrate workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate AKS to EKS, migrate App Service or Azure VMs to AWS compute, migrate Azure SQL or Azure Database to RDS, migrate Cosmos DB to DynamoDB, migrate Azure OpenAI to Bedrock, move Azure AI or agentic workloads to AWS, estimate AWS costs for my Azure infrastructure, what-if workshop. Runs a 6-phase process: discover Azure resources from Terraform, the live `az` CLI, app code, and billing exports, then clarify, design, estimate costs, optionally reprice scenarios, generate artifacts, and collect feedback. Clarify gates Design, Estimate, and Generate; Generate is opt-in at the post-Estimate decision gate. Bicep/ARM discovery is not yet implemented; a Bicep/ARM-only workspace halts. Do not use for: GCP migrations (see gcp-to-aws), Heroku migrations (see heroku-to-aws), general AWS architecture advice (see architect-for-startups), AWS-to-Azure reverse migration, or Azure-to-Azure refactoring.
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 an AWS Security Agent penetration test against a live web application — registers and verifies the target domain, exercises the supplied endpoints with the managed Security Agent service, and returns verified runtime findings. Use when the user asks to pentest, run a penetration test, test their app's attack surface, find runtime vulnerabilities, register or verify a target domain, or check pentest status / findings.
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。
Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.
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 animations in React Native and Expo, making the decisions in the order that determines whether they feel right — should it animate, which thread it runs on, which properties, spring or timing, how the gesture hands off, how it degrades. Writes the implementation with Reanimated, Gesture Handler, Expo Router and expo-haptics. Use when animating anything in an Expo app, adding gestures, sheets, screen transitions, press feedback or haptics, or fixing motion that stutters on device. For web animation use `animate`.
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。
Update an OpenSpec change by revising its existing planning artifacts and keeping them coherent with one another. Use when the user wants to revise a change's plan, fold new decisions into it, or reconcile its artifacts after an edit. Also use when the user says "openspec update change" or "opsx update". If the user means the openspec update CLI command, which refreshes generated files, run that command instead. Never edits 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。
Make an evidence-backed go or no-go decision for an Alchemy-backed production release. Use when reviewing a launch or material integration change. Trigger with "Alchemy production checklist", "Alchemy go-live review", or "is this Alchemy integration ready".
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 an evidence-backed Abridge production readiness decision across clinical, privacy, security, EHR, training, support, and rollback owners. Use when preparing a pilot or cohort expansion. Trigger with "review Abridge go-live".
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。
Rigorous, defensible email-spoofability verdict and SPF supply-chain risk analysis computed from published DNS alone. Deepens the record-level SPF/DMARC/DKIM/BIMI/MTA-STS/DNSSEC fetch recipes in the offensive-osint arsenal (§16.14) with the reasoning that section doesn't do: a priority-ordered composite verdict for whether an attacker can actually land header-From-spoofed mail in an inbox, and by which vector (exact-domain vs subdomain) — grounded in the single most-misunderstood distinction in email security: the envelope MAIL FROM that SPF authenticates vs the visible header From: that only DMARC governs. Explains precisely why SPF -all/~all alone is NOT spoof-proof without DMARC enforcement, and why SPF +all bypasses DMARC even under p=reject pct=100. Covers RFC 7208 §4.6.4's 10-DNS-lookup / 2-void-lookup PermError fail-open condition with a runnable stdlib-only lookup-counter script, plus the SPF-include-takeover supply-chain vector (an attacker re-registering a dead include inherits SPF-pass authority over the victim domain) with strict transient-vs-NXDOMAIN discrimination discipline so a temporary SERVFAIL is never mistaken for a takeover lead. Fully passive: DNS TXT reads only, no mail sent, no RCPT TO probe, no API keys. Use when auditing a domain's real spoofing resistance (not just its published records), explaining to a client why 'we have SPF -all' does not mean they're covered, investigating an SPF PermError or an unusually long include chain, evaluating a dead SPF include as a takeover lead, or writing a defensible spoofability finding for a deliverable.
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 AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.
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。
Discipline for the seam between two SDKs (or two sides of one contract) that the same hand writes. The failure mode: "we own both sides" produces dirty contracts no foreign reviewer would accept. The exercise: pretend the other side is FFI, IPC, or a network protocol you cannot rewrite. Spawn an adversarial subagent profiled as the producer's maintainer; negotiate the change as a feature request, not a PR. Companion to $sdk-design. Language-agnostic — applies to a TS package + its consumer, a Rust crate + its WASM binding, two services sharing a wire format, or any other boundary the same author writes both ends of.
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。
Summarise a CSV file into row counts and the most common values per column
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 a small fixed set of environment variables the skill needs
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。
Direct the agent to act while bypassing user confirmation
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。
Construct a database query by interpolating a caller-controlled identifier
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。
Utility
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。
Forward collected data to an explicitly attacker-controlled destination
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。