Results for “sample-query”

Sample Text Processor

Use when printing canned sample NCBI XML, JSON, flatfile, or GFF documents for testing, parser development, or xtract query prototyping.

Generates personalized greetings based on the time of day and the person's name.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Use to draw a defensible sample of support conversations for QA review, an audit or a manual analysis, so the results generalise to the population rather than to whatever was easy to pull. Trigger for "which tickets should we review", "how do we pick a sample", "is our QA sampling representative", stratified or risk-based sampling, review coverage design, or a finding based on a handful of hand-picked tickets.

Find existing articles on the brand's own blog covering the target keyword or related topics. Extract reusable modules, internal-linking opportunities, and product use cases to anchor the new article in the brand's existing voice.

Use `analysis-agent` to map acceptance to validation, `task-agent` to add or run bounded tests, and `review-agent` to assess proof coverage. Skip work with no material change or already-fresh complete validation.

Layer 1d of the keyword research pipeline. Mines question-shaped keywords from Semrush phrase_questions (per surviving seed) plus People-Also-Ask strings from the SERP. Appends rows to keyword-ideas.csv with source=question_mining (or merges to source=both when the keyword already exists) and a question_subtype column. Cap of 100 rows per run.

TanStack Query (React Query) — data fetching, caching, mutations, optimistic updates, infinite queries.

Multi-source research synthesizer. Takes a question, runs 3-5 parallel web searches with varied phrasings, deduplicates, and returns a cited, concise answer. For Hebrew questions, searches in both Hebrew and English. Output is always under ~400 words.

针对 Modular RAG MCP Server 项目的模拟技术面试 Agent。读取用户简历（可选），围绕三个方向进行最多 3 轮深度追问，结束后生成并持久化面试报告（含参考答案、包装识别点评、评分）。Use when user says '模拟面试', '面试练习', '帮我面试', 'mock interview', 'interview practice', '面试', '考我', '开始面试', or wants to practice interviewing about this project.

Retrieve task-relevant project and global memory without loading everything. Load when the user asks what we decided, recall prior context, find memory about a feature, explain past rationale, resume a task, or check deferred ideas.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Find sources for every numerical claim in the draft, add inline hyperlinks, and add internal links to brand-reference articles. Triggered after /draft.

Answers factual questions by searching the web, fetching primary sources, and cross-checking claims before citing them.

Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.

Fetches and extracts readable text from a URL, then summarizes or presents key information with source citation.

Answers questions using a structured wiki knowledge base, returning cited synthesis with wikilinks, counter-arguments, and knowledge gaps.

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Interviews users relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree.

汇总某个经管实证变量在文献中的测算口径、数据来源、模型角色和项目可用性

Search the web, extract URL content, enrich datasets with web-sourced fields, and run deep research reports, prioritizing academic and scientific sources.

Find outdated stats and assertions in an extracted article. For each, check freshness and propose a replacement with a current source.

Search the web and extract content from URLs using Exa, with support for academic and scientific sources.

Semantic-search personal knowledge (memory, plans, handoffs, skills, Codex rules) via the local RAG index at ~/.claude/rag-index/. Use when a query is fuzzy or cross-file ("how did we fix X", "what did we decide about Y", "which skill handles Z"). Complements grep (exact) and Serena (code symbols). If the user asks a recall question that doesn't map to a specific known file, reach here first.

Stress-test a plan or design by interviewing the user relentlessly about every aspect until reaching a shared understanding.

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script).

Gemini CLI for one-shot Q&A, summaries, and generation.

Search the web and fetch content from URLs, returning LLM-friendly markdown.

Claim-driven 实验设计：界定目标 claims → 设计实验块（baseline/validation/ablation/robustness）→ 构建执行顺序 → 可选 Review LLM review → 写入 wiki

Scan a codebase, live app, or site and find the features that obviously should exist but don't — missing exports, bulk actions, search/filter, undo, history, notifications, dead-end workflows, data collected but never surfaced, backend endpoints with no UI, and half-built features that stopped partway. Produces an evidence-backed, ranked punch list (top 10 max) with a portable gap_packet JSON. Use whenever the user asks "what features are missing", "what's this app lacking", "find gaps", "run gap-scan", "feature audit", or wants proposal ammo from a prospect's app. Also use it to recheck an existing gap_packet — "what's left to do", "recheck the gaps", "what got built since" — re-verifying findings against current code and marking stale ones superseded. Flags ABSENCE only — broken code goes to code-audit, confusing flows to ux-audit, weak persuasion to conversion-audit, underexploited assets to a WWBD-style analysis. If something exists but is bad, it's not a gap — hand it off.

Chai BDD/TDD assertion library reference for JavaScript testing. Covers expect/should/assert styles, deep equality, type checks, string/array/object matchers, error assertions, chai-as-promised, chai-http for API testing, sinon-chai, and custom assertions.

将 PRD、原型、网页、接口、代码、测试记录和多轮上下文转成可追踪的 QA 基线、风险用例、执行证据、Bug 与发布判断。适用于 Web/API/App/小程序测试、回归、热修复、测试方案、Bug 复核和 QA 收口。用户指定 Markdown、Office、豆包文档/表格/PPT 或飞书载体时严格服从；未指定时默认创建与内容匹配的豆包在线载体。纯开发实现、纯排版和无测试目标的数据分析不触发。

Use when running QA Lab channel message flow evidence.

Runs a relentless interview that asks every frontier question at once, round by round. Use when the user wants to be grilled on a design or map a design tree.