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概要と使いどころ
Generate a Privacy Impact Assessment in house format for a new feature, product, or processing activity, using the structure learned from your seed PIA. Use when the user says "write a PIA", "privacy impact assessment for", "do we need a PIA for this", "privacy review this feature", or describes a new data processing activity.
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 effects of a close-combat energy skill as one visual language in Three.js. Lightning that reads as lightning, dynamic but never in your face: trees of strips (trunk, forks of forks, hair-fine branchlets) with a fine white core in a violet-blue glow, each channel reaching out as a stepped leader, flaring as it connects, holding its shape and fading through an afterglow, one discharge at a time; a storm-cloud orb (a lit billow surface that churns in place, 10-15 plasma-globe arcs from a small white core to its inner wall, crawlers on the rim, a hairline fresnel); ground strikes that flash when the leader arrives, crawl along the stone's own cracks and reflect in wet stone (the demo's floor is a scanned CC0 texture); a torn black shadow with dry-brush edges that hangs off the orb and streams behind it, air bursts felt as refraction, orange sparks, fractured rocks that land or float in a storm domain, and impact frames that hold, invert in two-tone ink with tapered speed lines, shake and bend the lens, limited to three flashes a second. Use for anime or dark-fantasy skill effects, lightning or thunder fists, plasma or energy balls, charge-ups, dashes with shadow trails, ground strikes, storm or domain rings, ultimates, hit-stop, impact frames, negative flashes, screen shake, "black lightning", "thunderstorm fist", "realistic lightning", "afterimage" or "skill effects only". Includes a reusable module and a five-beat demo with failure-mode switches.
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。
Amplify safe or boring designs to make them more visually interesting and stimulating. Increases impact while maintaining usability. Use when the user says the design looks bland, generic, too safe, lacks personality, or wants more visual impact and character.
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。
Post-access cloud exploitation for AWS, GCP, and Azure — what to do AFTER you obtain credentials or reach a metadata endpoint. Covers IAM enumeration and privilege escalation (iam:PassRole, CreatePolicyVersion, AssumeRole chains, GCP service-account impersonation, Azure managed-identity abuse), IMDSv1/v2 metadata credential theft, STS token abuse, S3/GCS/Blob bucket takeover and object exfil, Lambda/Cloud Functions env-var secrets, cross-account and cross-service pivoting, and turning leaked keys into demonstrated impact (read PII, assume admin, exfil data). Assumes authorized scope only. Use when you have AWS/GCP/Azure keys, an SSRF hitting 169.254.169.254 / metadata.google.internal, a leaked service-account JSON, or a token, and need to escalate and prove impact. For finding buckets/origins first, use cloud-recon; for the SSRF entry point, see web2-vuln-classes. 中文触发词：云渗透、AWS提权、IAM枚举、元数据凭证、云安全审计
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。
Bug bounty report writing for H1/Bugcrowd/Intigriti/Immunefi — report templates, human tone guidelines, impact-first writing, CVSS 3.1 scoring, title formula, impact statement formula, severity decision guide, downgrade counters, pre-submit checklist. Use after validating a finding and before submitting. Never use "could potentially" — prove it or don't report.
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。
Bug bounty report writing for H1/Bugcrowd/Intigriti/Immunefi — report templates, human tone guidelines, impact-first writing, CVSS 3.1 scoring, title formula, impact statement formula, severity decision guide, downgrade counters, pre-submit checklist. Validation gates and the submittability/always-rejected decision are owned by triage-validation; this skill owns the written report itself (templates, tone, formulas). Use after validating a finding and before submitting. Never use "could potentially" — prove it or don't report.
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 to translate a classifier's in-place verdict into a precise, page-by-page work plan for the docs-sync panel. Activate after docs-impact-classifier returns verdict in_place; reads the candidate page list, fetches the actual page contents, narrows scope to specific sections within each page, and emits the per-page task brief the panel fans out against.
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 when the docs-impact-classifier returns a structural verdict, signalling that the documentation TOC must change to accommodate the PR. Proposes TOC deltas (new pages, moves, merges) and emits new-page outline stubs that the doc-sync panel later fleshes out. Holds the 3-promise narrative (consume / produce / govern) and the persona ramps as hard constraints.
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 to classify the documentation impact of a pull request diff, returning one of three verdicts -- no-change, in-place edit, or structural change -- with bounded LLM cost. Activate as a sibling skill of docs-sync; the orchestrator calls this first, before any panel spawn, to keep cost floor at 1 LLM call when no docs work is needed. Reads .apm/docs-index.yml as the corpus map; never reads the full corpus.
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。
Problem-solving strategies for compactness in topology
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。
Check the live status of the Strait of Hormuz from the Hormuz Strait Monitor dashboard: open, restricted, or closed status, tanker transits vs normal, stranded vessels, Brent price impact, war-risk insurance levels, cargo throughput, tanker freight rates, diplomatic developments, and global trade impact. Use this skill whenever the user asks about the Strait of Hormuz or the Persian Gulf in the context of oil, shipping, or geopolitical risk — whether Hormuz is open, chokepoint or transit disruption, Gulf tanker traffic, war-risk premiums, Middle East shipping routes, or energy supply-chain risk.
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。
Plans for operating through disruption — impact analysis, recovery objectives, continuity plans, and the exercises that prove they work. Use this to run a business impact analysis, set RTO and RPO, write or test a continuity plan, prepare for a supplier or site failure, or answer a customer's resilience questionnaire.
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。
建议在逻辑间隔进行手动上下文压缩（Context Compaction），以便在任务阶段中保留上下文，而不是依赖任意的自动压缩。
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。
建议在逻辑间隔处进行手动压缩（Manual Compaction），以在整个任务阶段保持上下文（Context），而非随意的自动压缩。
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。
AI DevKit · Compact a historical AI coding session with Jev when context is long, work is handed off or resumed, or durable continuation facts and memory candidates need extraction.
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。
Configures and verifies an expanded, model-aware context window for OpenAI Codex CLI and Desktop. Use when Codex shows ~258K context, you want 500K or 1M tokens, auto-compaction fires too often, or model_context_window/model_auto_compact_token_limit needs repair. Not for changing the selected model or other Codex settings.
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。
Continues interrupted Codex work only after read-codex-history verifies rollout identity and fork/compaction lineage — restores the original outcome, unfulfilled requests, and the next action. Use when given a Codex Session ID, asked to pick up a prior Codex run, told Codex was interrupted, fused, compacted or stuck, or asked to take over without `codex resume`. Not for a conversation Codex already natively resumed.
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 asked 个人信息保护影响评估怎么做, 写一份 PIA, 处理敏感个人信息要评估吗, 自动化决策评估, PIPL 第五十五条, or assess a processing activity under China's Personal Information Protection Law. Produces a personal information protection impact assessment (个人信息保护影响评估) covering the PIPL Article 55 triggers, the Article 56 questions (lawful, legitimate and necessary; impact and risk; adequacy of protection), a risk register with mitigations and owners, and a record kept for at least three years.
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。
Draft a defensible construction change order with entitlement basis, scope delta, itemised pricing, and schedule impact. Use when asked to write a change order, price extra work, draft a CO or COR/PCO, respond to a directive for changed work, or paper a field change. Produces a complete change order request with contract-clause entitlement, labour/material/equipment/OH&P breakdown, time impact statement, and reservation of rights.
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。
Write changelogs and release notes readers actually benefit from — changes translated to so-whats, grouped by reader impact (breaking first, gifts second, plumbing last), with the upgrade path stated and the marketing kept honest. Use when asked write the release notes, turn this commit list into a changelog, announce this update to users, or why does nobody read our changelogs. Produces the impact-grouped changelog, the so-what translations, the breaking-changes block with migration steps, and the two-audience split when 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。
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
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 fast, high-impact 15-30 second brand and product marketing videos from an official company website, verified brand sources, or user-uploaded product imagery. Use for launch films, website motion videos, AI/SaaS demos, product promos, and Vibe Motion/Remotion productions that require exact logos, authentic imagery, English-first copy, product-feature storytelling, strong visual impact, smooth transitions, and deterministic frame-based animation.
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。
Context engineering: compaction, progressive disclosure, structured notes via AgentDB. Triggers: tokens, compaction, memory, handoff, summarize, context window.
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。