「did」の検索結果

Use when the user asks to "map what our buyers believe", "capture the objections we keep hearing", or "find the switching forces that move the beachhead"; produces a belief map of the beachhead — held beliefs and mental models, the recurring objections and their reframes, and the JTBD four forces (push of the problem, pull of the new, anxiety of switching, habit of the present) — each item sourced from interviews or win-loss notes (User-provided) and labeled Measured / User-provided / Estimated, with any unverified quote or comparative claim marked "[needs source]" and routed to the claims candidates, never adjudicated here. Not for demographic or persona profiling — use audience-mapper; not for the positioning canvas — use positioning-truth-tracer. 受众信念/异议地图/切换四力/流失语言

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 "analyze backlinks", "analyze my off-site signals", or "track AI traffic / ChatGPT / Perplexity referrals"; profiles referring domains, anchor-text mix, toxic links, and disavow candidates (backlinks mode) or isolates AI-assistant referral sessions in GA4/GSC/logs and reports their trend, landing pages, and AI-vs-organic conversion (ai-referrals mode). Not for internal links — use site-structure-optimizer; not for keyword positions — use rank-tracker; not for multi-metric stakeholder reports — use performance-monitor. 外链分析/反向链接/AI流量/AI引荐/ChatGPT流量/AI转化

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 "read back" a paid campaign change, "did this ad change work", or "compare ROAS/CPA before and after"; reads ROAS/CPA against a control over a fixed readback window and returns a Promote / Keep-testing / Rollback / Unproven readback decision with the math delegated to roi-calculator. Not for RQS scoring or veto adjudication — use ad-account-auditor; not for the ROI ratio math — use roi-calculator; not for cross-channel rollups — use performance-analyzer. 付费广告复盘/ROAS回看/投放效果归因

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 "track where my emails are actually landing after I send", "read my seed-list inbox vs spam vs promotions results", "trend my Gmail Postmaster / Microsoft SNDS reputation", or "did placement drop after my last send"; produces a per-provider inbox/spam/promotions placement read, a domain/IP reputation trend from Postmaster + SNDS, a send-over-send delta with named regressions, and a reusable SEND-S placement snapshot on your own exported telemetry. Not for the pre-send SPF/DKIM/DMARC auth pre-flight — use deliverability-qa; not for computing the EQS or running the vetoes — use email-quality-auditor. 邮件收件箱落点监测/收件箱vs垃圾邮件/Postmaster声誉趋势

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 "check pacing", "am I over/under-spending", "is this campaign on track to hit budget", or "why did spend spike/stall mid-flight"; returns a spend-vs-target-curve read, learning-phase status, an over/under-delivery call, and a reallocation trigger. Not for initial budget allocation — use budget-optimizer; not for choosing the bid strategy — use bid-strategy-planner; not for the RQS gate — use ad-account-auditor. 付费广告预算节奏监控/跑量过快过慢/在途配速

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 "what did we pay this creator last time" or to "update the creator roster"; curates creator identity, rate, rights, exclusivity, compliance-event, and performance facts through the append-only creators event stream. Not for scoring fit — use fit-scorer; not for reviewing content — use creator-content-auditor. 创作者档案/达人名册

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Read funnels, retention curves, and event data as a designer — separating a design problem from a tracking artefact. Use when handed product data you did not design and asked why people drop off. For choosing what to measure, use `metrics-definition` (ux-strategy); for running a controlled test, use `a-b-test-design` (prototyping-testing).

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Use when THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits. Covers payment setup, policy, session budgets, and troubleshooting. Triggers on: "my agent hit a 402 while calling an API", "a tool call returned 402 Payment Required", "my agent needs to pay for x402-protected content", "let the agent pay for content, capped at $5 per session", "set a spend limit for the agent", "ProcessPayment failed", or "why did my agent refuse to pay". Not for BUILDING payment capability for end users, including wallets and framework middleware; use agents-build and references/payments.md. For non-paid APIs via Gateway use agents-connect. For inbound auth use agents-harden. For project scaffolding use agents-get-started.

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。

Pull AWS Security Agent findings (penetration tests and code reviews) and drive remediation. Use this whenever the user mentions Security Agent, security findings, pentest or penetration test results, code review findings, vulnerabilities found in their AWS account, "what did the security scan find", remediating or triaging security risks, or wants to start fixing reported vulnerabilities — even if they don't name the service explicitly. Trigger it for phrases like "get my security findings", "what vulnerabilities do we have", "let's fix the pentest results", or "triage the security report". The skill discovers scans, exports findings to a gitignored local directory (so sensitive exploit detail is never committed), produces a prioritized triage summary, and offers to start fixing the highest-risk 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。

Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).

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。

Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.

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。

Submit feedback on an Expo skill—or Expo itself—and control bundled anonymous usage telemetry (off by default / opt-in). Submit feedback with: npx --yes submit-expo-feedback@latest "ACTIONABLE_FEEDBACK". Optionally add either or both: --category "CATEGORY" and --subject "SUBJECT". Replace the uppercase placeholders before running. Use when a skill was useful, confusing, broken, missing context, or worth improving; when Expo, Expo CLI, EAS CLI, docs, or MCP worked well or fell short; when an AI agent repeatedly failed, got stuck, or needed the user to take over an Expo task (report it as an eval candidate); or when the user explicitly asks to enable or disable telemetry (tracking), check its status, or understand what it collects.

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。

FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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。

Org-grade attack-surface mapping: given a company's legal identity, discover its ENTIRE owned internet footprint — corporate family -> owned domains -> owned netblocks/ASN -> live assets — with attribution discipline, not just DNS breadth. The org-first attribution pyramid (legal entity -> LEI/registration -> corporate family -> owned domains -> owned netblocks/ASN -> live assets). Corporate-identity resolution via the GLEIF LEI API (legal name -> LEI, exact-LEI direct-children expansion, downward-only depth-capped BFS, NEVER a name re-resolution), SEC-EDGAR full-text search + Exhibit-21 subsidiary entity names, OpenCorporates entity corroboration, Wikidata SPARQL corporate graph (P856/P355/P749/P1830). Domain attribution via reverse-WHOIS (WhoisXML preview-then-purchase quota guard, SecurityTrails associated-domains — both paid), crt.sh O= certificate-transparency organization pivot (keyless), infrastructure correlation (shared NS/MX/SaaS-TXT, netblock membership, reverse-DNS PTR), and the independent-evidence combiner (1-prod(1-w_i), rule of three, OwnerTier NONE/WEAK/MODERATE/STRONG/CONFIRMED) — discover-only `related:` candidates are NEVER auto-scanned. Netblock/ASN attribution via org-first RIR queries (ARIN Whois-RWS org-handle search, RIPE DB organisation + inverse-org search) that recover 'dark netblocks' with no DNS link to the seed, ASN discovery (RIPEstat + BGPView union), the HYPERSCALER-SCOPE GUARD (never attribute a whole AWS/GCP/Azure/Cloudflare announced range to a tenant — keep only the seed-containing block, tag shared_hosting_cdn), and org-identity-seeded internet-scan-index queries (Shodan/Censys/ZoomEye/FOFA/BinaryEdge org: filters + an always-on keyless crt.sh fallback). Promote-to-scan triage ranks forgotten discover-only netblocks by remote-access exposure (gateway-vendor/KEV/control-plane/datastore port scoring) into an operator queue. Attribution confidence rubric + anti-patterns (namesake grafting via GLEIF name re-resolution, single-signal ownership, hyperscaler over-attribution, privacy-WHOIS pivot poisoning, RIR org-name collisions). Passive/keyless-first OSINT only — every paid-key dependency is enrichment on top of a keyless core, never a hard requirement. Use when mapping an organization's full corporate-family internet footprint, resolving a legal entity to its LEI/subsidiaries, discovering domains/netblocks/ASNs an org owns beyond its one seed domain, auditing M&A/shadow-IT sprawl, or scoping an engagement that starts from a company NAME rather than a domain.

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。

Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, standalone image-to-video clips (i2v, animate this still, 그록 영상, 이매진 비디오, 스틸 움직여줘, first/last frame, reference-to-video, 영상 이어붙이기, 영상 편집, extend/edit a clip), clips from a video MCP on the agent (ZCRE, 지크 MCP), chroma removal, animation atlases, video loops, 큐레이션뷰, image candidates, 팔레트 스왑, palette swap, recolor, rig layers, engine exports, repeating backgrounds, projected shadows, motion/contact inspection and optional scene composition from existing assets.

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。

Fill out job application form fields with context-aware, tailored answers drawn from the candidate's CV and the job description

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。

Interactive daily standup/meeting update generator. Use when user says 'daily', 'standup', 'scrum update', 'status update', 'what did I do yesterday', 'prepare for meeting', 'morning update', or 'team sync'. Pulls activity from GitHub, Jira, and Claude Code session history. Conducts 4-question interview (yesterday, today, blockers, discussion topics) and generates formatted Markdown update.

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 recent code changes via git history and extract software engineering lessons. Use when the user asks 'what is the lesson here?', 'what can I learn from this?', 'engineering takeaway', 'what did I just learn?', 'reflect on this code', or wants to extract principles from recent 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。

Names and rebrands an app clone so it is the user's own: name candidates with the trademark, domain, store and handle checks to run, a new palette checked for contrast, a logo brief, a voice guide, and a sweep tool that finds anything of the original left in the codebase. Use when the user says "name my app", "rebrand the clone", "make it mine", "pick a name and colours", "logo brief", "brand voice", "check for leftovers", or after /replica-entrepreneur. Always runs before /replica-launch.

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。

Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.

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。

Guided journey from a live website that underperforms to a prioritized, evidence-backed backlog of conversion, usability, message, and speed fixes - each shipped as a testable experiment. Orchestrates eight skills phase by phase - cro-methodology, ux-heuristics, refactoring-ui, web-typography, storybrand-messaging, high-perf-browser, made-to-stick, design-everyday-things - asking the user questions at every decision point and recording results in the project docs/ folder (WEBSITE.md, DESIGN.md, IMPROVE-WEBSITE-PLAN.md) so the journey resumes across sessions. Use when the user wants to fix a landing page that isn't converting, diagnose why visitors leave, audit for clarity and usability, or says 'the homepage feels off but a redesign didn't help'. No site yet, create-website; converts but needs traffic, grow-website; if the friction is in a product app, not the marketing site, improve-app; if one specific flow leaks, conversion-optimization. For one framework in isolation, invoke that skill directly.

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 or improve OpenChatCut custom skills. Use whenever the user asks to create a skill, capture a workflow as a skill, write a SKILL.md, add a reusable workflow, 创建技能, 把流程做成技能, 写一个技能, or improve an existing custom skill. Pushy: treat any workflow the user repeats as a skill candidate, even if they do not say "skill".

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。

Answer questions about what the user recently did on their computer — who contacted them, what they were working on, where they left off — by reading the local Computer History summaries and raw event streams.

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。