「matching」の検索結果

Use this skill for launching, supervising, debugging, OR platform lifecycle on a BlueField — BFB install, RShim/TMFIFO, host PF rebind, post-BFB recovery — taking a DOCA-linked binary to a healthy run directly on hardware (host x86 + BlueField NIC over PCIe, or BlueField Arm bare-metal). No container, no kubelet. Covers launch mode (direct, tmux, systemd), PCI/NUMA/ CPU/IRQ binding, co-tenant isolation (cgroup-v2/netns/numactl), a seven-layer error taxonomy, and a six-state BlueField lifecycle classifier. Trigger even when user does not say "bare-metal" — implicit phrasings include "binary exits 1 right after launch", "systemd keeps restarting it", "no matching device on the BF", "bfb-install exited 0 but DPU is dead", "ping 192.168.100.2 works but ssh fails", "host PFs aren't showing netdevs". Destructive firmware burn / mlxconfig set requires explicit confirmation via doca-hardware-safety; containers, library APIs, env prep, and build use other skills.

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Build and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware programming, read counters, match the Flow version to the installed DOCA release, and diagnose Flow API errors. Trigger on DOCA packet steering, classifier, representor, rule-matching, hairpin, or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Route plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.

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Search one AI agent's raw history for a specific topic, ingest only matching sessions, and return a synthesized answer. Use for targeted cross-agent recall; use wiki-history-ingest for bulk archival ingestion.

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Monitors task execution for skill improvement opportunities. Use during ANY multi-step task, agentic workflow, or work session. Captures patterns, user corrections and methodology worth preserving as reusable skills. It writes observation files to the workspace. Also triggers in post-task feedback discussions and when the user mentions skill observations, the observation log, or skill taxonomy. Also known as "One Skill to Rule Them All" — trigger on this phrase too. IMPORTANT: invoke this skill before the FIRST tool call of any session and before writing or proposing a plan — any turn that will involve a tool call counts. This sentence is the session-start trigger and the only activation layer that survives an unreachable config file; pair it with a CLAUDE.md instruction or a harness session-start hook (references/environments.md) — description matching alone is not enforceable. A subagent dispatched by a session already running it does not run it: it writes nothing and puts its findings in its report.

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Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility/inclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.

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Searches the Azure skills catalog and recommends installable agent skills by matching an Azure task to skill metadata and plugin installation guidance. WHEN: before starting any task that involves an Azure or Microsoft-cloud service, product, or data source, when no currently loaded skill or tool already covers it.

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Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.

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Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.

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Design to iOS and Android conventions — what each OS mandates, where they diverge, and when to unify. Use when shipping native apps. For breakpoint adaptation use `responsive-design`; for matching competitor patterns use `jakobs-law` (interaction-design).

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Turns one-shot external recon into a continuous monitoring program. Covers the scheduled re-scan-and-diff loop (baseline snapshot -> interval sleep -> re-scan -> asset/finding delta -> threshold-gated webhook alert), the scan-to-scan diff engine (new/removed/changed assets by a tracked-attribute table, new/resolved findings by a stable cross-scan fingerprint), adversary CTI / chatter monitoring across six public feeds (ransomwatch, ransomware.live, HackerNews Algolia search, Reddit security-subreddit RSS, GitHub Gist code-search, public Telegram channel scraping) with a source-kind-aware severity engine (leak-site/forum/telegram/paste tiers, CRITICAL through INFO), literal/glob/regex watchlist pattern matching, full-corpus capture with retroactive rescan on new watchlist entries, infrastructure-tracking-over-time discipline (certificate-transparency, passive-DNS, port/service, and typosquat re-enumeration cadence, and what a genuine 'perimeter drift' event looks like in the diff output), a five-state finding-lifecycle state machine (open/triaged/risk_accepted/resolved/false_positive) with per-severity SLA and fingerprint-based cross-scan dedup and auto-resolve/reopen rules, the alert-fatigue trap where a lifecycle-unaware rule re-fires on an already-accepted finding, a durable retry/backoff alert-outbox pattern ('queued is not delivered'), and copy-paste bash-cron plus PowerShell-Scheduled-Task recipes for a re-scan+diff loop with the Slack-compatible webhook payload shape. Passive OSINT and analysis only -- no new active-intrusion technique. Use when setting up ongoing monitoring for a retainer or MSSP engagement, tuning alert thresholds to avoid fatigue, triaging a finding's lifecycle status, investigating adversary chatter about a brand, building a 'what changed on the perimeter since last week' report, or deciding whether a persisting finding should re-alert.

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Generate OpenEnv environments from a concrete use case (for example, "generate an env for the library textarena"). Use when asked to design or implement a new environment under envs/ by researching a target library/API, selecting matching OpenEnv examples, asking key implementation questions, and building models/client/server/openenv.yaml. Do not use for model training or evaluation tasks.

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Generate prompts for AI image tools to produce modern ML/RL paper-style figures matching the aesthetic of recent ICLR/NeurIPS/ICML 2024-2025 publications. Key traits — pure white canvas, white panels with soft drop shadow, rounded friendly font (Nunito/Poppins), densely packed but uncluttered floating elements, small pastel token squares, rich inline illustrations, colored keyword text, pill-shaped concept labels. Trigger phrases — "pastel风格论文配图", "现代ML论文配图", "modern ML figure prompt".

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Builds questions and assessments that measure what they claim — matching item type to the thing being assessed, writing distractors that diagnose, and telling a question that measures learning from one that measures reading speed or test-taking. Use this to write or review assessment items, design a unit check or diagnostic, work out why scores do not match classroom performance, or decide how much evidence a claim about mastery actually needs.

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Cooking close-ups, mukbang and eating vlogs. They work when every beat shows one visible change in the food and one matching sound, and the dish stays the same dish from first frame to last. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Food close-ups and eating ASMR; 中文触发词: 美食特写与吃播 ASMR, 美食, 吃播, ASMR, 烹饪), or asks how published cases of this type were prompted. Read references/cases.md before drafting.

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Search file contents using regex pattern across directories. Cross-platform pure Python implementation (no external tools needed). Returns matching lines with file paths and line numbers.

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Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.

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Design a structured case-control study framework with explicit source population logic, control selection rules, matching decisions, exposure measurement planning, and bias-control checkpoints.

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This skill should be considered when you need to normalize testing engineers' written defect reasons following the provided product codebooks. This skill will correct the typos, misused abbreviations, ambiguous descriptions, mixed Chinese-English text or misleading text and provide explanations. This skill will do segmentation, semantic matching, confidence calibration and station validation.

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Guide for translating Python code to functional Scala style. Use when converting Python code involving higher-order functions, decorators, closures, generators, or when aiming for idiomatic functional Scala with pattern matching, Option handling, and monadic operations.

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Download flood stage thresholds from NWS (National Weather Service). Use when determining flood levels for USGS stations, accessing action/minor/moderate/major flood stages, or matching stations to their flood thresholds.

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AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.

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Methodology for clause-by-clause review of a contract against a structured deviation policy ("playbook"). Covers how to walk a playbook, locate the matching provision in the contract, apply rule types (max-value, must-be-present, must-be-absent, acceptable-set, must-have-feature), classify the result (ok / risk / reject), choose the prescribed action, and ground each finding in a verbatim excerpt. Use whenever reviewing any contract — NDA, MSA, vendor DD questionnaire, lease, DPA — against a structured rules-based playbook.

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Maintain and match against a library of classic dynamic programming patterns. Provides pattern matching, template code generation, variant detection, and problem-to-pattern mapping for DP problems.

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Minimal, pattern-matching code output. Write the least code that satisfies requirements. Match existing project patterns. Use Write/Edit tools only.

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