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概要と使いどころ
Portable multi-source research with cross-source validation and an offline evidence ledger. Use for fact-checking, comprehensive research, or any question requiring multiple independent perspectives; work with the host agent's search tools and optionally add SandBase Tavily, Exa, Scholar, and Cloudsway 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。
Research startups and emerging companies with funding data, team composition, product positioning, market traction, and news coverage. Combines Apollo firmographics, LinkedIn team data, and web intelligence for comprehensive startup due diligence.
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。
Aggregate news from Google News, Tavily, and Exa to get comprehensive coverage of any topic. Cross-references multiple news sources, identifies publication velocity, detects emerging stories, and provides citation-ready news intelligence.
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。
Track media coverage, press mentions, journalist activity, and industry narrative across news outlets, Twitter, and LinkedIn. Ideal for PR teams monitoring earned media, tracking campaign impact, and identifying journalist relationships.
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 comprehensive market research combining web search, academic sources, news coverage, and industry data. Produces market sizing signals, competitive landscape overviews, trend analysis, and evidence-backed market opportunity assessments.
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。
Research podcasts, episodes, and audio content by searching YouTube (where most podcasts crosspost), web sources, and transcript databases. Discover relevant shows, track guest appearances, and analyze topic coverage in the podcast ecosystem.
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。
Monitor brand mentions, sentiment, and reputation across Twitter, Reddit, news, and social platforms through SandBase. Use when asked for brand monitoring, reputation tracking, social listening, PR coverage analysis, or competitive brand intelligence.
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 any industry's competitive landscape using company databases, academic research, news coverage, LinkedIn activity, and job market signals. Produces industry overviews with key players, market segments, growth indicators, and emerging disruptors.
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。
Track real-time events, announcements, and breaking developments across Twitter, Reddit, news outlets, and web search. Monitors event progression, public reaction, key moments, and aftermath coverage for crisis response and opportunity detection.
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 and monitor news articles across Google News through SandBase. Use when asked for news monitoring, current events research, news coverage analysis, or media tracking.
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。
Analyze competitor content coverage and uncover differentiated content opportunities using web search, page extraction, and DataForSEO content analysis. Use when asked for competitor content research, editorial gap analysis, content brief inputs, or topic whitespace.
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。
Create a 30/60/90-day cash-flow forecast from AR, AP, opening cash, payment timing, and fixed-cost data. Use when asked about runway, payroll coverage, liquidity risks, or a near-term cash crunch.
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 differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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。
Per-CpG differential methylation testing from bisulfite sequencing count data or beta-value matrices. Covers beta and M-value computation, coverage filtering, statistical tests (Welch t-test, Mann-Whitney, limma, DSS beta-binomial), multiple testing correction, and effect size calculation. Use when comparing methylation at individual CpG sites between experimental groups from WGBS, RRBS, or targeted bisulfite sequencing.
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。
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
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。
Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu. Use when choosing between coverage-based, HMM-based, beta-binomial window-based, and crosslink-site-based peak callers; applying ENCODE eCLIP thresholds (log2 IP/SMInput >= 3, -log10 p >= 3); deciding when SMInput is mandatory; or reconciling peak-set discordance between callers for the same RBP.
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。
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
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。
Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.
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 working on Formax code changes and you need a disciplined dev loop: keep a single mainline task, avoid scope drift, run only targeted tests (no coverage), avoid partial staging (MM), run mandatory review before commit, include an incremental optimization check, and keep commits small and reviewable.
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 implementing features or fixing bugs with strict Test-Driven Development (TDD). Activate for coding tasks that require new functionality, refactoring, or comprehensive test coverage, especially when the user mentions TDD or Test Driven Development.
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。
Commercial Property Analysis — NOI, cap rate, expense ratio, tenant mix, vacancy, debt coverage, replacement cost, and lease analysis with Commercial Score (0-100)
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 AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-context.
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 production telemetry as INPUT to design new tests. Covers OpenTelemetry integration with tests, trace-based assertions, log-informed test creation, production-error analysis for coverage gaps, and telemetry-driven test prioritization. Use when: "trace-based testing," "design tests from logs," "OpenTelemetry assertions," "production errors point to test gaps," "telemetry-driven testing." Not for: safe rollout techniques (flags, canary) during release — use testing-in-production. Not for: scheduled post-deploy probes — use synthetic-monitoring. Not for: triaging CI failures — use ai-bug-triage. Related: testing-in-production, synthetic-monitoring, qa-metrics, ai-bug-triage.
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 a risk matrix or heatmap that quantifies what could break by business impact × probability, runs failure mode analysis on the top items, and maps test coverage to risk zones. Includes stakeholder interview frameworks and continuous reassessment. Run this BEFORE test-strategy or test-planning. Use when: "risk assessment," "risk matrix," "risk heatmap," "what could break," "critical paths," "failure modes," "where to focus testing." Not for: multi-quarter QA direction — use test-strategy. Not for: a single sprint/release test plan — use test-planning. Not for: hands-on session-based bug hunting — use exploratory-testing. Related: test-strategy, test-planning, release-readiness, qa-metrics.
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。