“regression”的搜索结果

Culture assessment and monitoring with three modes — Diagnostic for a deep session surfacing the gap between declared culture and actual culture, Review for periodic check-ins on whether behavior is shifting, and Alert Triggers for setting tripwires around culture-strategy misalignment, regression, and integration risk. Not a culture change programme — a mirror. Updates company-context.md after every run. Triggers on questions about culture, values, team dynamics, how decisions really get made, why change efforts stall, merger integration, or whether the organisation can actually execute its strategy. Based on practice by Eterdis (eterdis.com).

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Design, review, or report a measured performance comparison. Use for benchmark methodology, speedup or regression claims, and performance-based choices; not generic performance discussion, ordinary dependency bumps, local renames, or prose edits.

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Review the product engineer's post-implementation testing notes for how thoroughly they cover the card's requirements and risk, and whether regression-worthy cases are automated at the right level of the test hierarchy

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Review code changes on this card for likely bugs, regressions, and missed edges

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Playwright-powered browser automation and E2E testing for SolanaOS Hub. Covers test configuration, agent-driven test generation (planner/generator/healer), NanoHub route testing, Convex API validation, wallet flow testing, IPFS Hub verification, and CI/CD integration. Use when asked about E2E tests, browser automation, Playwright setup, NanoHub testing, visual regression, or Hub route verification.

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Review someone else's pull request by measuring which of its own guards its own test suite actually catches, via a small mutation matrix. Use when reviewing a PR, checking whether a regression test is real, or asked 'is this change actually tested?', 'does this test do anything', 'mutation test this'.

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Test-driven development with red-green-refactor and behavior-focused tests. Use when the user explicitly wants test-first work, a regression test before a fix, or integration tests that drive a feature through a public interface.

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Disciplined diagnosis loop for hard bugs, regressions, flaky failures, and performance problems with unclear cause. Use for root-cause debugging after a concrete symptom exists; do not use for routine implementation or speculative cleanup.

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Post-fix verification — re-scan the repo, gate on `--exploits weaponized --severity high`, recheck the specific CVE against the new installed version, write the verdict to `.vulnetix/memory.yaml`. Use when confirming a fix landed, validating a version bump did not introduce regressions, or producing a clean-scan attestation for compliance.

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Load when asked to review a local diff/change set for correctness, regressions, blast radius, affected flows, and missing tests using the knowledge graph; use `review-pr` for intent/checklist/merge-gate evidence.

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Load when diagnosing a bug, exception, regression, wrong result, failing flow, or unknown root cause in repository code using callers/callees, execution flows, recent changes, and impact analysis; not for implementing a known fix without investigation.

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Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

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Comprehensive results analysis for empirical research: generate publication-quality descriptive statistics and balance tables, interpret regression coefficients with economic magnitude and effect sizes, assess identification assumption diagnostics, and produce structured results memos. Use when asked to create summary statistics, Table 1, balance tests, interpret results, assess economic significance, or write results narratives.

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Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

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Called by /plot to upgrade regression and summary tables to top-journal standards.

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Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.

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Use when a website needs rendered screenshot review, Playwright visual regression, multi-viewport checks, horizontal overflow detection, console-error checks, focus/state inspection, or a repeatable visual QA evidence set.

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Use after substantial web projects or repeated design defects to convert evidence into reusable skill improvements through root-cause analysis, regression scenarios, and controlled proposals without silently changing the core OS.

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Open-format data ingestion for NV oOS Pro toolkits — the reusable pattern behind the F&B data-ingestion track (Proposal 078). Covers structural (header) matching over filename mapping, the configurable table_map + header-matcher + name-guess resolution order and the slug-first regression, the multi-format loader (CSV/TSV/JSON/JSONL/XLSX/ODS/PDF/photo), the Observation + terminology-map layer (vitals/FHIR analogue) with raw preservation, dedupe/retention/out-of-band banding, complete-by-default/resumable (offset) imports, and the read-only ingest-review dashboard anatomy (edit_posts review vs manage_options write, dry-run-first). Use when adding or extending data ingestion to a toolkit, ingesting heterogeneous owner files, wiring XLSX/PDF/photo imports, building an ingest/review dashboard, or debugging partial/truncated imports.

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Design and review API contract tests across providers and consumers. Use for schema changes, compatibility regressions and client integration failures.

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Build AI-focused SRE incident response practices for LLM outages, degraded quality, runaway cost events, and safety regressions.

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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.

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Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.

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Run or interpret bounded multi-page Unlighthouse audits for local Lighthouse evidence. Use for site-wide performance, accessibility, best-practice, and SEO regression checks when field data or API quotas are insufficient.

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