「bias」の検索結果

Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.

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。

Lattice-based cryptanalysis playbook. Use when attacking RSA via Coppersmith small roots, recovering DSA/ECDSA nonces from bias, solving knapsack problems, or applying LLL/BKZ reduction to cryptographic constructions.

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。

Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user-research questions, interpreting ambiguous feedback, or designing customer-discovery that avoids false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

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。

Applies decision science and cognitive bias research to marketing and product decisions — how people actually choose under uncertainty, and how framing, defaults, sequencing, and social context change behavior. Use this to diagnose why a well-argued offer is not converting, to structure choices and pricing presentation, to design an experience around how attention and memory work, or to pressure-test whether a persuasion tactic is legitimate or manipulative.

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。

Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.

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。

Draw ROB2 risk-of-bias plots, including a Traffic Light Plot and a Summary Bar Plot. Input is a CSV file with ROB2 assessments for each study; output are two PNG plot files.

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。

Perform quantitative meta-analysis with effect size calculation, forest plots, funnel plots, and heterogeneity assessment. Use when: user asks to combine results from multiple studies, calculate pooled effect sizes, assess publication bias, or create forest/funnel plots. NOT f...

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

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。

Explains why studies on the same biomedical topic reach different or opposing conclusions by auditing differences in population, endpoint definition, sample source, assay or platform, study design, statistical model, adjustment strategy, validation chain, and bias control. It separates true contradiction from apparent contradiction caused by framing or methods. Never fabricate references, PMIDs, DOIs, trial identifiers, dataset details, platform details, study features, or conflict explanations that are not supported by the input.

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。

Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as methodological rigor.

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。

Assesses whether study results are trustworthy by auditing design integrity, sample structure, statistical handling, bias control, validation chain, and claim discipline. It identifies where results are robust, fragile, overfit, under-validated, or overclaimed. Always separate reported findings from reliability judgment. Never fabricate references, PMIDs, DOIs, trial identifiers, study features, or validation claims.

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 when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.

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。

Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.

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。

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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 when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.

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。

Professional, ethical HR partner for hiring, onboarding/offboarding, PTO and leave, performance, compliant policies, and employee relations. Ask for jurisdiction and company context before advising; produce structured, bias-mitigated, lawful templates.

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。

Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per `write-code-eval`).

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。

Turn interview notes into a structured candidate scorecard and hire recommendation. Use when asked to write an interview scorecard, a candidate evaluation, an interview debrief, or to summarize feedback into a hire/no-hire call. Produces a per-competency assessment with evidence and ratings, an overall recommendation with confidence, and the open questions for the next round — evidence-based, bias-aware, and decision-ready.

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。

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

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。

Help users master the art of making high-stakes, irreversible decisions by using structured frameworks to reduce cognitive bias and maintain organizational momentum.

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 when running and reporting the statistical analysis for an Academy of Management Journal (AMJ) manuscript — measurement validity, common-method bias, the right estimator (HLM, SEM, panel, experiments), endogeneity, and robustness. Executes and reports the analysis; it does not design the study (amj-methods) or frame the contribution (amj-contribution-framing).

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。

Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.

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。

Detects cancer and infers tissue-of-origin from cfDNA methylation by choosing conversion chemistry (bisulfite vs EM-seq vs TAPS vs cfMeDIP), calling read-level methylation haplotypes rather than averaged beta values, and deconvolving a hematopoietic-dominated cfDNA mixture against a methylation atlas via NNLS/quadratic programming. Encodes the GRAIL/CCGA thesis that thousands of tissue-specific markers make methylation outperform sparse mutations for multi-cancer early detection (MCED) and localization, and that single concordantly-methylated fragments give ppm-level sensitivity. Uses MethylDackel for extraction (mbias-then-extract), MEDIPS/QSEA for enrichment data, scipy.optimize.nnls for deconvolution. Use when building an MCED or methylation-MRD assay, picking a conversion chemistry for low-input plasma, or deconvolving tissue-of-origin from cfDNA.

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。

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

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。