「assignment」の検索結果

Guides experiment state transitions: launching, pausing, resuming, ending, shipping variants, archiving, resetting, duplicating, and copying to another project. Covers preconditions, implications for variant assignment and analysis, and the decision framework for when to use each action. TRIGGER when: user asks to launch, pause, resume, end, ship, archive, reset, duplicate, or copy an experiment to another project. DO NOT TRIGGER when: user is creating an experiment (use creating-experiments), configuring rollout (use configuring-experiment-rollout), or setting up metrics (use configuring-experiment-analytics).

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。

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.

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。

Design what an employer will have a candidate do during an Otameshi Tenshoku trial: turn a business problem into a bounded paid assignment or an unpaid learning visit, with scope, completion criteria, candidate hours, company staff hours, duration, compensation and change conditions, and check the planned cost against the budget. Use when a hiring manager, recruiter or small-business owner wants to decide what work to offer, how much time it takes, what it costs, or how to adjust a plan when materials are late or the scope grows; do not use to write the public listing (trial-listing-draft), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post, contact or contract on the user's behalf.

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, from the employer's side, what has been put in writing before a paid Otameshi Tenshoku assignment starts under gyomu-itaku: item by item whether the deliverable, delivery date, fee and how it is calculated, payment due date, who pays, out-of-scope work, revision limits, company support, rights, confidentiality and termination are stated in a contract, purchase order or email rather than in chat or by word of mouth, count the days from delivery to payment, and find pay tied to satisfaction or hiring, unpaid or unlimited revisions and one-sided termination. Use when a hiring manager, recruiter or small-business owner is about to offer terms to a candidate, has been asked by a candidate what is still unwritten, or needs the conditions written for a platform contract screen; do not use to judge whether the terms are lawful or the fee fair, to compute withholding or consumption tax, for employment contracts, or to sign, send or conclude the contract on the user's behalf.

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。

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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。

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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。

Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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。

Translate an approved experiment spec into a launch runbook — platform binding (PostHog primary), feature flag setup, assignment unit, exposure event definition, instrumentation QA, dashboard wiring, ramp plan, monitoring, and rollback procedure. Platform-agnostic core with one strong PostHog adapter shipped; GrowthBook, Statsig, LaunchDarkly, Optimizely, and Eppo documented as a single mapping table the user adapts. Load when a spec is approved and ready to launch, or when the user says "set up the experiment", "wire this up in PostHog", "implement the test", "create the runbook", "launch checklist for this test", or when the experimentation orchestrator routes here.

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。

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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。

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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。

Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

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 user asks about driver fitness-for-duty programs — pre-trip readiness assessment, recognizing impairment from drugs/alcohol/medication/fatigue/mental health, refusing assignments when unfit, supervisor training on fitness assessment, FMCSA recommendations on duty status determination, and how to handle a driver who appears unfit. Reference 49 CFR 392.3 + 392.4.

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。

Pattern Patrols system of recurring, certified sweeps. Use when you are a patrol run or an assignment names a P# id, you spot a registered-pattern violation off-mission (log a sighting), a mistake class keeps recurring, or you touch PATROL_REGISTRY or PATROL_SIGHTINGS. NOT for one-off bug fixes.

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。

(core) route header and body-height conformance via PageHeader. Use when fixing a (core) page's header, title bar, breadcrumbs, sub-route nav, body height (h-page, calc(100dvh…)), mobile header, or buttons hidden behind the avatar, or on a header-conformance assignment. NOT for /administration/* routes.

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。

Locate CEconItemView::operator= in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CEconItemView::operator=" (symbol CEconItemView_operator=). Copy-assignment operator. Shortlist via call sites in weapon-give/refresh paths that copy an existing CEconItemView into another (this-pointer copy of definition index, seed, wear, attribute list and item ID fields — a field-by-field copy, not a memcpy). Trigger: CEconItemView_operator=, CEconItemView::operator=

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。

Find and identify the CBasePlayerController_SetPawn and CBasePlayerController_GetPawn functions in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll / libserver.so to locate the pawn-assignment routine by scanning for its distinctive multi-register-push prologue directly (no pre-resolved anchor needed), confirming uniqueness and byte-level match against a known reference signature. Trigger: CBasePlayerController_SetPawn, CBasePlayerController_GetPawn

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。

GRASP principles knowledge base for PHP 8.5 projects. Provides quick reference for 9 responsibility assignment patterns (Information Expert, Creator, Controller, Low Coupling, High Cohesion, Polymorphism, Pure Fabrication, Indirection, Protected Variations). Use for architecture audits and design decisions.

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。

When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.

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。

When the user needs to integrate multiple data sources (Canvas API, user memory, file systems) to create comprehensive academic reports. This skill combines course information, assignment details, submission status, and user context to generate actionable insights. Triggers include requests that involve cross-referencing multiple data sources or creating consolidated academic reports from disparate systems.

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。

Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.

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。

Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5: debugging, refactoring, changes across 2+ files, or unfamiliar code; and instruct a subagent on Opus/Sonnet/Haiku to read this skill with a non-trivial assignment. Triggers include "think like Fable", "fable-mode", "turn on discipline", "work carefully", dispatching a subagent on a weaker model, and repeated mistakes or rework in the session. NOT needed for trivial edits (rename, typo, one-liner) or purely conversational answers.

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。

Choose Beads issue priority (P0–P4, numeric, or named) from urgency, impact, and risk when creating or triaging 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。

Apply bottom-up and top-down role mining techniques to discover optimal RBAC roles from existing user-permission assignments, reducing role explosion and enforcing least privilege.

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。