「thesis」の検索結果

Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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 and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

Hunt for adversary techniques in telemetry you are authorized to query. Hypothesis-driven, evidence-first, no live offensive operations.

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。

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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。

Conviction-trigger surveillance via Quartr keyword alerts — parse every active thesis's Conviction Triggers for named observables, propose keyword alerts scoped to the Vault Active watchlist (or market-wide for macro observables), create them ONLY on explicit user confirmation, and reconcile the alert set against the book as theses open/close. Use when user says "monitor", "set up alerts", "keyword alerts", "watch for X", or "reconcile alerts".

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。

Change conviction level or status on a single thesis. Use when user says "change conviction", "downgrade", "upgrade", "move to monitoring", "close [TICKER]", or "set [TICKER] to [level]".

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。

Extract a thesis's dependency fingerprint — the external variables its bull case actually rests on (key customers, suppliers/inputs, technology transitions, macro drivers, single points of failure). Use when user says "dependency map", "what does [TICKER] depend on", "dependency fingerprint", or "map exposures for [TICKER]".

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。

Audit whether a thesis's stated conviction matches its evidence, and whether any Conviction Trigger has silently fired without being actioned. Use when user says "conviction audit", "is my conviction right on [TICKER]", "did a trigger fire", or "should I re-rate [TICKER]".

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。

Tag a thesis's implicit macro bets — the macro variables its bull case is secretly levered to (AI capex, rates, FX, China decoupling, memory/HBM cycle, energy, regulation) with direction and magnitude. Use when user says "macro exposure", "what macro is [TICKER] betting on", "macro bets", or "rate exposure for [TICKER]".

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。

Pull an earnings call transcript from Financial Modeling Prep, extract qualitative signal (management commentary deltas, hedging shifts, Q&A tone), and produce a thesis-delta-first Research note. Use when user says "transcript", "pull earnings call", "transcript diff", or "ingest [TICKER] earnings".

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。

Map a thesis's position in its value chain — who it buys from, sells to, and competes with; where the bottleneck and pricing power sit; and whether it owns a critical layer. Use when user says "value chain", "supply chain position", "where does [TICKER] sit", or "who are [TICKER]'s suppliers/customers".

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。

Extract and maintain a cross-portfolio catalyst calendar from all thesis notes. Use when user says "catalyst", "calendar", "what's coming up", "upcoming events", or "what could move the portfolio".

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。

Restore a thesis, sector, or macro note from a snapshot in _Archive/Snapshots/. Use when user says "rollback", "restore", "revert", "undo sync", or "go back to the previous version".

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。

Pull an earnings call (or investor-event) transcript from Quartr (FMP fallback), extract qualitative signal (management commentary deltas, hedging shifts, Q&A tone), and produce a thesis-delta-first Research note. Use when user says "transcript", "pull earnings call", "transcript diff", or "ingest [TICKER] earnings".

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。

Adversarial triage of open review findings on a PR. Every finding (Codex, Claude bot, Gemini) is a hostile hypothesis to REFUTE — fix only what is proven real, reachable, in the PR's scope, and above the round's severity bar. The bar rises every review round. Everything else is declined or filed as an issue. Use for 'fix the PR findings', '/fix', '/fix 123', or before any fix commit in a review loop.

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 wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," or "how long should I run this test." Use this whenever someone is comparing two approaches and wants to measure which performs better. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.

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。

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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。

Orchestrator for the experimentation skill suite — turn assumptions and product questions into rigorous, well-instrumented experiments and decision-grade readouts. Routes through backlog → spec → runbook → readout based on user need and existing artefacts. Platform-agnostic with PostHog as the primary binding. Load when the user asks to design an experiment, A/B test something, set up an experiment, run a holdout, test a hypothesis, decide what to test next, read out experiment results, analyse a test, or says "should we A/B test this", "experiment on the landing page", "is this lift real", "ship or kill this test", "what should we test next", "build an experiment backlog", "test the pricing page", "validate this with an experiment".

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。

Capture the "why" behind technical choices to prevent architectural drift. Load when the user makes a major technical decision, chooses a library/framework, defines a system boundary, or changes a core architectural pattern. Also triggers on "record a decision", "write an ADR", "why did we do this", "document this architectural choice", or "architectural decision record". Supports `SYNTHESIS=true` mode for retrospective ADR backfill from observed repo state — used by `retroactive-project-setup` and any future backfill skill. Essential for long-term project maintainability and agent alignment.

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。

Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, ML options pricing, balanced news synthesis, and meme search via the Helium MCP server.

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。

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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。

Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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 and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 (Mathis 2023/2024) for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2 / PE3 / PE3b / PEmax / PEAR variants, MOSAIC in situ saturation mutagenesis (Hsu JY et al 2024 bioRxiv), the PRIME pooled-screen methodology (Erwood/Doman 2023 Nat Biotechnol 41:885; ~3,699 ClinVar variant screens), chromatin context as a primary determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

Comprehensive toolkit for writing high-quality computer science research papers (conference, journal, thesis). Provides narrative construction guidance, sentence-level clarity principles (Gopen & Swan), academic phrasebank, CS-specific conventions, and section-by-section quality checklists. Use when assisting with academic paper writing, revision, or structure planning across all stages from drafting to submission.

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。