Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.
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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Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.
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 asks to explain a problem, issue, PR, or feature as an infographic. Create a step-by-step visual explanation starting from user code or actions, with a clear problem/solution boundary, syntax-highlighted snippets, and focused diagrams. Support standalone educational PDFs and Markdown-first PR explanations, with optional images only for complex diagrams. Also apply when explicitly invoked by the repository PR writer.
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。
iOS pentesting playbook. Use when testing iOS applications for keychain extraction, URL scheme hijacking, Universal Links exploitation, runtime manipulation, binary protection analysis, data storage issues, and transport security bypass during authorized mobile security assessments.
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。
Evaluate and improve interface usability using heuristic analysis. Use when the user mentions "usability audit", "users are confused", "form usability", "navigation problems", "Nielsen heuristics", "cognitive walkthrough", or "is this easy to use". Also trigger when reviewing a design for usability issues, improving form-completion rates, or evaluating information architecture and navigation. Covers Krug's laws, Nielsen's 10 heuristics, severity ratings, dark-pattern recognition, and accessibility. For visual design fixes, see refactoring-ui. For conversion-focused audits, see cro-methodology.
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。
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-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。
Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.
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。
按 tdesign-vue-next 规范编写、补充、迁移或审查 Vue 组件单元测试,并运行 Vitest 验证。适用于 packages/components 下的组件 API 测试、Issue 回归测试,以及相关 hooks、utils 测试。
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 the user wants to reduce churn, improve user engagement, or increase lifetime value. Also use when the user mentions "retention", "churn", "users leaving", "engagement", "DAU/MAU", "user activation", or "why are users uninstalling". For onboarding-specific issues, see app-launch. For monetization, see monetization-strategy.
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 the user wants to analyze, respond to, or improve their app reviews and ratings. Also use when the user mentions "reviews", "ratings", "negative reviews", "how to get more reviews", "review response", or "my rating is dropping". For broader ASO audit, see aso-audit. For retention issues causing bad reviews, see retention-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。
Run complete OpenClaw Windows Node issue and PR triage, including landing queues, proof pools, ownership audits, and release planning.
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 methodology for reproducing AND patching publicly disclosed, already-fixed vulnerabilities: sourcing the advisory and the fix commit, reading the patch diff, rebuilding a pinned offline lab, building a vulnerable-vs-patched contrast harness with proper controls, minimizing the trigger, designing and validating the fix, and writing it up as a blog post or whitepaper. Use this skill whenever the user wants to reproduce, demonstrate, understand, teach, or write about a known vulnerability or security incident: "reproduce this CVE", "build a repro for", "how did EchoLeak / Log4Shell / the postmark-mcp package actually work", "show me how this exploit worked", "write a blog post about this vulnerability", "set up a lab for CVE-XXXX", "pull the NVD/CVE/OSV details", or "make a PoC I can publish". Use it just as eagerly for the patching half: "how should this be fixed", "is this patch correct", "what would have caught this", "review this security fix", "write a regression test for this CVE", "how do I backport this", or "what's the right fix for this bug class" - even when the user never says the word "reproduce". Reach for it on vague asks like "help me understand this security advisory", "I want to write about this incident", or "can we test whether we were vulnerable" when a disclosed vulnerability is in play. This skill only handles ALREADY-PATCHED, publicly disclosed issues and refuses zero-day discovery, live targets, and weaponization.
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。
Builds and maintains the leveling framework and pay structure — level definitions, salary bands, benchmarking, pay equity, and how raises and promotions are decided. Use this to design or revise leveling, set or adjust salary bands, benchmark against market, handle a compensation request or counteroffer, run a review cycle, or diagnose pay compression and equity issues.
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。
Reviews and negotiates commercial agreements — MSAs, SOWs, order forms, NDAs, vendor and data-processing agreements — identifying material risk, proposing positions, and recommending a path rather than listing issues. Use this to review a contract before signature, prepare a negotiation position, build fallback positions and approval thresholds, or assess exposure in terms already agreed.
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。
Handles a dispute from the first sign of it — recognizing when preservation obligations attach, issuing and scoping a legal hold, suspending automatic deletion across every system that holds relevant material, preserving privilege, managing outside counsel and cost, and evaluating settlement against the real cost of continuing. Use this to respond to a claim or demand letter, issue a hold, work out which systems the hold has to reach, or decide whether to fight.
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。
Writes and edits newsletters and marketing emails people actually open — subject lines, opening, structure, voice, and the conversion turn where there is one. Use this to draft or rewrite a newsletter or email, sharpen weak email copy, plan a newsletter's structure and cadence, write a sequence, or repurpose an issue into other formats. For automated lifecycle and SMS programs, use `lifecycle-messaging`.
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。
Builds the loop from what customers say to what gets changed — collecting feedback, distinguishing signal from noise, routing it to owners, and closing the loop back to the customer. Use this to set up a feedback program, design or interpret CSAT/NPS, decide what customer feedback deserves action, get product to act on recurring issues, or diagnose why feedback is collected but nothing changes.
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。
Handles customer situations that have exceeded normal support — severity assessment, incident communication, executive escalation, and recovering a relationship after a failure. Use this when a customer issue is escalating or has gone to leadership, during a customer-affecting outage, when a major account is at risk, when a relationship needs repairing after a failure, or to design the escalation path itself.
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 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
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。
Reproduce triaged Slack bugs through a configured app-control adapter, verify existing fixes, and open a bounded draft pull request only after before-and-after proof. Use only from the configured Benny repro automation.
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。
Capture browser page screenshot (webpage content only, not desktop). When you need to show page state, document results, or debug issues. For desktop screenshots, use desktop_screenshot 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。
Inspect window UI element tree structure for debugging and understanding interface layout. When you need to debug UI automation issues, understand application structure, or find correct element identifiers.
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。
Automate GitHub repositories, issues, pull requests, branches, CI/CD, and permissions via Rube MCP (Composio). Manage code workflows, review PRs, search code, and handle deployments 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。
Establish a finding before you publish it, and correct it after — headlines that overstate what actually reproduces at the layer a user sees, reporting code that no entry point can reach or that is already dead, filing a caveat the project's own records already answered, re-verifying your prior notes against the tree instead of against the note, choosing the narrowest injection point that reproduces a failure without breaking the run first, capturing probe output a harness swallows, and withdrawing a published claim in the thread where you published it. Use when filing an issue, writing the body of a PR or code review that asserts a defect, triaging someone else's report, or deciding whether a suspicious observation is reportable at all.
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。
This skill is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integration with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment 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。