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概要と使いどころ
Use when designing, implementing, reviewing, debugging, testing, profiling, or porting concurrent, parallel, distributed, high-performance, network dataplane, kernel-bypass, or real-time software, including locks, semaphores, monitors, MPI, CSP rendezvous, DPDK, VPP, eBPF, XDP, AF_XDP, SR-IOV, RSS, RoCEv2/RDMA, RMS/EDF, priority inversion, and environment selection from silicon to edge.
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End-of-life detection for runtimes (Node, Python, Java, Go, .NET) and key packages — surfaces past-EOL items, items reaching EOL within 90 days, and EOL base images for containers. Use when planning a runtime upgrade, auditing for unsupported versions, gating a deploy against EOL deps, or producing a remediation roadmap.
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Signpost (not a knowledge pack) for maritime safety and class rules: SOLAS Consolidated Edition 2024 (IH110E) as the paywalled statutory core, ISM Code & Guidelines 2018 (ID117E), ISPS Code 2003 (K116E) and the 2021 guide (IB116E), and COLREGS 2026 (IC904E), with designation, edition, owner, status, and official URL only. Contains no source content. Free-path layers are US flag implementation (eCFR Title 46 and USCG circulars), the USCG Navigation Rules PDF, UK MCA notices under Open Government Licence v3.0, and class portals (DNV, ABS, Bureau Veritas). None of those layers is the IMO consolidated text. No in-repo pack covers these instruments.
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Obtenir et gérer un access token pour l'API Merci Facteur (envoi de courrier papier, recommandé, recommandé électronique). Utiliser dès qu'un appel à l'API Merci Facteur doit être authentifié, ou quand apparaît une erreur de signature, de timestamp, de restriction d'IP ou un token expiré. Couvre le calcul HMAC-SHA256 de la secret key, getToken, la durée de vie du token jusqu'à 365 jours (timeLimit, force) et le stockage des identifiants.
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Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.
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Knowledge base from the NASA Fault Management Handbook (NASA-HDBK-1002, Draft 2, April 2012). Use for the systems-engineering discipline of designing what a system does when it fails — the fault/failure/anomaly vocabulary, the five FM strategies (two prevention strategies — design-time fault avoidance and operational failure avoidance — plus three tolerance strategies — failure masking, failure recovery, and goal change), the six FM process activities across NASA mission phases Pre-A to E, response-latency-versus-time-to-criticality (TTC) design trades, fault/failure containment regions and FEPPs, the four redundancy approaches, top-down FM requirements development, FM verification vs. validation, the seven dedicated FM milestone reviews (FMCR/FMARR/FMPDR/FMCDR/FMTRR/FMLRR/FMCERR), FM organizational structure, and mined NASA Lessons Learned. Space-mission oriented (flight/ground/operations); spacecraft examples throughout. IMPORTANT: built from an UNAPPROVED DRAFT — several sections (detailed architecture building blocks, the Assessment & Analysis guidance, and Operations & Maintenance) are placeholders in the source and are thin here. Thin on detailed FDIR algorithms, terrestrial/IT fault management, and any approved-standard terminology that may have changed since Draft 2.
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Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.
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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.
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Prepares a DRAFT data incident impact brief from the incident notes and data inventory the user provides: the timeline as stated, systems involved matched to the inventory, data categories held and evidenced as involved, people and record counts potentially affected with the source and method behind every figure, third parties, notification questions for legal and the data protection officer, and every UNKNOWN with the evidence that would settle it. No legal determination: it never states whether the incident is a reportable breach, whether notice is due, to whom or by when. Use when the user asks to "prepare the impact brief for this incident", "what data was involved", "how many people are affected", "map the incident to our data inventory" or "pull together what legal needs on the incident". Do not use for the blameless postmortem or root-cause timeline, use incident-postmortem-drafter instead. Drafts for human review; never approves, authorises or signs off.
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Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.
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指定した起動ディレクトリと、親が確定した dotfiles root のスキル配置・エージェント定義を点検する。 判定の正は etc/audit-skill-agent-layout.py。実効 runtime の native 優先順、Cursor の name、 agent の model/role、生成物の状態を報告する。「overlay 点検」「スキル配置」 「エージェント定義は共通か」「layout audit」「/overlay-audit」で使う。
ユーザーが明示的に /chat と入力したときの深掘り対話。問いの文脈に合わせ、必要な最新確認・ローカル確認・比較・具体例を加えて回答する。通常の短い会話を自動で重い調査へ変えず、利用可能な実際のruntime操作だけを使う。
- Extract and interpret embedded file metadata with exiftool — EXIF GPS coordinates, camera make, model and serial, DateTimeOriginal and CreateDate timestamps, XMP and IPTC fields, and Office and PDF… Use when reading EXIF from a photo, checking who really wrote a document, dating a file, fingerprinting a camera or phone, or investigating provenance in JPEG, HEIC, RAW, MP4, DOCX, XLSX or PDF
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- Deep-dive a subject's social media presence — profile metadata, follower and mutual network, content analysis, and posting-time pattern of life across Instagram, Facebook, X/Twitter, TikTok, LinkedIn, Reddit, Telegram and… Use when profiling a social account, mapping someone's associates, inferring a subject's timezone or routine from their posts, or archiving a profile before it is deleted
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Use when migrating a CopilotKit v1 application to v2 -- updating package imports, replacing deprecated hooks and components, switching from GraphQL runtime to AG-UI protocol runtime, and resolving breaking API changes.
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Mutate WooCommerce variation prices via filters — pick the right layer (woocommerce_product_get_price for simple/non-variation products, woocommerce_product_variation_get_price for variations, the woocommerce_variation_prices_* family for the parent min/max aggregation that feeds the catalog "From X to Y" range), and the critical step of filtering woocommerce_get_variation_prices_hash whenever your price logic depends on context outside the default cache key (user role, custom toggle, time of day) — otherwise one user's filtered price gets cached and served to everyone. Use for role-based pricing, time-based discounts, or "filter variation prices but the min/max range doesn't update" debugging. Triggers on woocommerce_product_get_price, woocommerce_product_variation_get_price, woocommerce_variation_prices_*, woocommerce_get_variation_prices_hash, wc_var_prices.
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Register a custom Query type for JetEngine's Query Builder — extend \Jet_Engine\Query_Builder\Queries\Base_Query for the runtime query class, extend \Jet_Engine\Query_Builder\Query_Editor\Base_Query for the editor component, then hook BOTH register actions — jet-engine/query-builder/queries/register (factory) for the runtime, jet-engine/query-builder/query-editor/register for the editor UI. Five abstract methods on Base_Query — _get_items(), get_items_total_count(), get_items_page_count(), get_items_pages_count(), get_current_items_page(). Built-in cache via get_cached_data() / update_query_cache(). Custom queries participate in JE 3.8+ MCP tool exposure and the frontend query inspector automatically. Use when scaffolding a custom query type (HPOS WC orders, third-party API source, custom DB table) for JetEngine listings, dynamic widgets, or REST API endpoints.
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Use when the user asks to "track our share of voice", "what share of the conversation do we own vs competitors", or "trend our SOV this quarter"; computes SOV% = brand mentions ÷ (brand + competitor panel mentions) per platform per period on a LOCKED competitor panel — a panel switch invalidates the trend (restart the series and log the break; the ECHO O3 rule) — plus a sentiment-weighted SOV variant (sentiment labeled Estimated unless human-coded) and a Wikipedia-pageviews attention-share alternative; built from keyless listening connectors, gdelt.py news echo, and user exports — public counts only, closed platforms are never scraped. Not for backlink or offsite SEO signals — use offsite-signal-analyzer. 声量份额/竞品声量对比/提及份额/注意力份额
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Use when the user asks to "watch my list health over time", "flag decaying / unengaged subscribers on a schedule", "why is my open rate drifting down / bounces creeping up", or "build me a re-permission and prune worklist"; runs the scheduled SEND list-decay + suppression-drift watch — an engagement-recency cohort read (30/90/180/365-day), hard-bounce and spam-complaint trend vs benchmark, suppression-list growth/leakage check, and a segmented re-permission / sunset / prune worklist tied to SEND S (list hygiene) and E (engagement-decay) sub-items. Not for the one-time pre-send authentication pre-flight — use deliverability-qa; not for the consent/suppression record itself — use consent-registry; not for computing the EQS or enforcing vetoes — use email-quality-auditor. 邮件列表健康度监控/退订漂移/沉睡用户清理
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Use when the user asks to "pick a launch date", "plan the launch window", or "set the embargo and lift time"; produces a candidate-window comparison table (conflict / tailwind / risk per window) built from industry-event cycles and the competitor launch calendar, a launch-week vs rolling-release format call, store-review buffer padding (labeled Estimated), and an embargo window definition (lift moment + timezone) submitted to the launch registry as a candidate. Not for judging the cultural moment itself — use trend-spotter; not for launch-day execution — use launch-day-conductor. 发布择时/发布窗口/竞品日历/禁运期窗口/审核缓冲
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When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.
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Use when the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘
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Use when the user asks to "run a deliverability pre-flight before I send", "check my SPF/DKIM/DMARC/BIMI", "why am I landing in spam / promotions", or "score my sender reputation and list hygiene"; runs the ONE-TIME pre-send SEND S1 authentication pre-flight and builds the SEND S (Sender-integrity / Deliverability) evidence read — DNS + DMARC-RUA auth, domain/IP reputation, inbox placement, content/link/render, and point-in-time bounce/complaint hygiene — using Pass/Partial/Fail/Unknown/N/A states and scoring only at complete applicable coverage. Not for the recurring hygiene trend — use list-hygiene-monitor; not for final EQS or veto verdicts — use email-quality-auditor; not for segments/suppression lists — use list-segment-builder. 邮件送达率预检/SPF DKIM DMARC认证/发件域声誉
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Marketing project management and execution excellence for B2B SaaS campaigns. Use this skill when coordinating multi-channel campaigns, allocating team resources, planning agile marketing sprints, running QBRs with clients, managing project timelines, balancing marketing workload, or implementing marketing operations scrum. Also triggers on: campaign coordination, resource allocation, agile marketing, sprint planning, client success, QBR, project timeline, marketing ops scrum, workload balancing.
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。