「qa」の検索結果

The default code-driven browser path for interactive browsing work: drives any browser from code with token-efficient a11y snapshots (57% smaller via compactSnapshot), ref-based clicks, coordinate control (CUA), viewport pinning, CAPTCHA solving (reCAPTCHA, Turnstile, hCaptcha, slider, OCR), Chrome MV3 APIs (tabs/bookmarks/history/downloads/topSites), network snoop (read API JSON instead of DOM), flight-recorder trace (jsonl + HAR + screenshots), OOPIF-aware snapshots, dialog policy, human handoff for login/OTP, device emulation, request routes, stealth via CloakBrowser as the default owned engine with zero exposed CDP ports, and an attached engine that drives the user's own signed-in browser through BrowserSkill (connectBrowserSkill, bskSnapshot, one-click onboarding via bskOnboard). MUST USE for interactive browser work: scraping, blocked/WAF/JS-rendered pages, logins, the user's logged-in sites and open tabs, extension popups, form filling, screenshots, web QA, CAPTCHAs. The ulw-research browsing lane runs on ultimate-browsing, not this skill.

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。

Read the network instead of the DOM. Snoop JSON API responses, wait for a request instead of sleeping, collect items while scrolling, record a QA flight trace (jsonl, HAR, screenshots), and intercept requests with 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。

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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。

Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM; Liu 2022 *NAR Genom Bioinform* 4:lqac046), MeTPeak (HMM over sliding windows; Cui 2016 *Bioinformatics* 32:i378), MACS3 / MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment confirmation via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output formats, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union; cross-caller IDR-equivalent), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody-based methods cannot resolve, and orthogonal-validation routes (miCLIP / GLORI / m6A-SAC-seq / m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set as a whole, reconciling differing peak sets across tools, validating MeRIP peaks against orthogonal single-base methods, interpreting peaks at the 5' end where m6Am contamination is possible, or recommending a multi-tool consensus strategy.

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。

Orchestrate multiple Antigravity skills through guided workflows for SaaS MVP delivery, security audits, AI agent builds, and browser QA.

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 verification of one SDD Composy task in verify_required: reproduce every claim with fresh commands, build the truth table, refute stale evidence, and produce the verdict that gates complete. Use when review is done and the task claims to be finished — 'verificar a TASK-003', 'a task está pronta mesmo?', 'is this task really complete'. Do NOT use for QA, code review, or a general 'verify my work' outside an SDD task.

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。

Run a bounded, provider-neutral SDD Composy correction loop on one approved task (EXECUTE, QA, EVIDENCE, REVIEW, VERIFY; at most 3 iterations) with explicit approval, safe-stop guards, and fresh verification. Use to continue an approved TASK autonomously — 'rodar o loop na TASK-003', 'continuar a task até passar', 'start a loop on task 4'. Do NOT use for the host's recurring loop runner, for fleets (sdd-fleet), or for a task without human approval.

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。

Discover, build, verify, and optionally export (HTML, optional PDF) the evidence manifest of one SDD Composy task in evidence_required, routing it toward review_required. Use when acceptance evidence must be assembled or checked — 'juntar as evidências da task', 'gerar o relatório de evidências', 'verify the evidence manifest'. Do NOT use to run tests (sdd-qa), review code (sdd-review), or inspect the trace (sdd-trace).

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。

Plan explicitly authorized read-only production observations and trace evidence-backed causes into preventive regression coverage without external effects.

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。

Triage defects with independent severity and priority, reproducible evidence, preserved attempt history, and explicit resolution retesting.

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。

Record a reproducible defect, independent triage, immutable attempts, and evidence-backed retest state.

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。

Select traceable regression coverage from change impact, affected risks, criteria, dependencies, and prior defects rather than execution convenience.

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 bounded performance profiles with explicit load authorization, contextual samples, percentiles, and agreed thresholds.

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 traceable data checks for reconciliation, integrity, transactions, and atomicity with separate observable oracles.

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。

Build an impact-based regression selection with explicit traceability, inclusions, exclusions, and limitations.

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。

Execute an explicitly authorized exploratory charter and record notes, findings, evidence, and follow-up without claiming false coverage.

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。

Produce an evidence-backed release readiness opinion while keeping the human release decision separate.

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 platform-specific Android and iOS checks with explicit SDK, driver, device, lifecycle, and evidence prerequisites.

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 deterministic CI quality gates that read the normalized manifest verdict independently from report-export process status.

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 authorized, bounded, reproducible security checks without turning scanner findings into penetration-test permission.

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 traceable API checks for contracts, authentication, authorization, errors, and idempotency with observable outcomes.

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 isolated repeatable automation, distinguish interactive browser exploration, and handle flaky results through evidence and reproduction.

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 traceable functional test conditions across positive, negative, boundary, and error behavior with observable expectations.

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。

Assess requirement clarity, testability, completeness, consistency, and traceability without inventing missing acceptance criteria.

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。