「hidden」の検索結果

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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Subagent flow for deep-reading GitHub issues + their linked/closing PRs to the bottom. Captures CURRENT state, maintainer status updates, reporter repro, blocking reviewer concerns, hidden second bugs, cross-repo duplicates. Invoked by weekly-report orchestrator; runs as a general-purpose subagent so the orchestrator's context stays small.

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This skill enables IP address masking and accessing hidden services.

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Use when ranking competing initiatives for a roadmap, breaking a tie between two features, or auditing a backlog for hidden low-value work via Reach × Impact × Confidence ÷ Effort.

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Use when the user shares a Sentry error, Jira bug ticket, or error description and wants root cause analysis. Also for proactive bug hunting and code audits for hidden bugs.

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First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

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。

Detect and defend against prompt injection hidden in documents, web pages, and images consumed by an agent.

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Systematically deobfuscate multi-layer PowerShell malware using AST analysis, dynamic tracing, and tools like PSDecode and PowerDecode to reveal hidden payloads and C2 infrastructure.

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Examine file system slack space, MFT entries, USN journal, and alternate data streams to recover hidden data and reconstruct file activity on NTFS volumes.

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Exploratory data-migration validation and QA ideation workflow. Use when you need to discover hidden risks, generate new validation angles, and converge mapping specs/validation reports into a prioritized QA backlog for CRM migrations. Version 2.0, updated 2025-12-28.

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XP-style plan review that identifies gaps, hidden assumptions, and critical misses. Triggers on 'review the plan', 'check the plan', 'audit the plan'. Reviews plans for YAGNI violations, missing feedback loops, and over-engineering.

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- Craft advanced search-engine queries and Google dorks to surface hidden files, documents and mentions. Use when building a Google dork, hunting a leaked document, or searching paste sites and document repositories for a name, email or selector

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- Enumerate an organisation's subdomains and sibling domains from Certificate Transparency logs and passive DNS, without sending traffic to the target. Use when looking for staging, dev, admin or VPN hosts, mapping the full hostname footprint of a domain, or spotting infrastructure a company forgot it had

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Synthesize WooCommerce coupons at runtime without creating shop_coupon posts in the DB — via the woocommerce_get_shop_coupon_data filter, the hidden-but-powerful virtual-coupon mechanism. Filter fires inside WC_Coupon::__construct, and a non-false return becomes a fully functional coupon read through read_manual_coupon. Plus custom discount types via woocommerce_coupon_discount_types and woocommerce_coupon_get_discount_amount, and the validation triple woocommerce_coupon_is_valid / _is_valid_for_cart / _is_valid_for_product. Use when implementing rule-driven coupons (auto-apply X% to logged-in users, code-pattern detection like LOYALTY-42, external CRM integration that hands out codes), without populating wp_posts. The AI-deficient pattern; most LLMs don't know dynamic coupons exist. Triggers on woocommerce_get_shop_coupon_data, woocommerce_coupon_discount_types, woocommerce_coupon_get_discount_amount, read_manual_coupon, virtual coupon, dynamic coupon in WC context.

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Configures the JetFormBuilder action events system — the named events (DEFAULT.PROCESS, DEFAULT.REQUIRED, GATEWAY.SUCCESS, GATEWAY.FAILED, BAD.REQUEST, ON.DYNAMIC_STATE, never) that decide WHEN a Form Action runs. Covers declaring supported / unsupported / required events on a custom action, the validation precedence inside Base_Event::is_valid_action(), the multi-event subscription model the action editor exposes, and how to register a brand-new event type via the 'jet-form-builder/event-types' filter using Base_Event, Base_Action_Event, or Base_Gateway_Event. Use when an action needs to run only on payment callbacks, only on validation failure, only on a specific conditional state, after another action's hidden flow, or when the plugin needs to define its own event class (e.g. for inbound webhooks). Triggers on mentions of "JFB events", "action events", "GATEWAY.SUCCESS", "DEFAULT.PROCESS", "supported_events", "unsupported_events", "get_required_events", "Base_Event", "Base_Gatewa...

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Apply when reviewing or shaping code that's hard to trace. Count layers between question and answer, and hidden state in the reader's head; collapse one-caller wrappers and shrink mutable scope.

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Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.

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MetaBackdoor trains the model to activate hidden behavior when the total token count crosses a speci

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Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.

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Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales

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。