2.2천건 · 관련도순
Use when user is part of a plural system — provides warm session check-in for who is fronting and maintains a simple JSON session log. No clinical language, no assumptions about system structure.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Curate Busabase training examples, create immutable dataset snapshots, run reproducible LoRA or QLoRA fine-tunes on a local Apple Silicon Mac with MLX-LM, compare the untouched baseline and adapter on locked evaluations, and register approved adapters. Use when the user wants to train, fine-tune, evaluate, compare, promote, or manage a local language model with Busabase as the workflow source of truth. Do not use for RAG-only knowledge retrieval, cloud training, or training a foundation model from scratch.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates the monthly revenue-accountable marketing report that B2B SaaS marketing leaders deliver to CEOs, CFOs, executive teams, and boards. Use when preparing the monthly CEO report, drafting the marketing section of a board deck, defending budget decisions, diagnosing pipeline gaps, or transitioning marketing from MQL/SQO reporting to revenue, CAC payback, and forward pipeline coverage. Designed for B2B SaaS demand generation leaders who own pipeline targets across segments, geos, and motions, and need to explain marketing's revenue contribution in executive language.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Builds multi-touch pipeline attribution models and generates stakeholder-ready narratives from campaign performance data. Use when analyzing channel contribution to pipeline, preparing monthly/quarterly revenue reports for leadership, diagnosing pipeline misses, modeling budget reallocation scenarios, or translating raw attribution data into board-deck language. Designed for B2B SaaS demand gen teams that report on pipeline and revenue, not leads or MQLs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Vercel AI SDK (TypeScript LLM ツールキット, ai / @ai-sdk/*, v7)。 generateText, streamText, Output, ToolLoopAgent, createMCPClient, useChat, UIMessage, embed, generateImage, wrapLanguageModel, WorkflowAgent, HarnessAgent, AI Gateway, @ai-sdk/rsc、 v4 から v7 への移行。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Configure AWS Verified Access to provide VPN-less zero trust network access to internal applications using identity and device posture verification with Cedar policy language.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing `experiment-list` (not feature-flag tools), with disambiguation when multiple experiments match. Use when the user names or quotes an experiment ("split test demo", "the File engagement boost experiment", "onboarding retention test", "landing page hero experiment", "pricing experiment"), describes it loosely ("the signup experiment", "my pricing test", "the one with the new checkout"), uses a relative reference ("latest", "most recent", "the one I created yesterday"), filters by status (running, draft, stopped, archived), or otherwise refers to an experiment by anything other than its concrete ID.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, or when another skill needs to maintain the domain model.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Authors portable, strongly-typed bioinformatics pipelines in the Common Workflow Language (CWL v1.2) as CommandLineTool/Workflow/ExpressionTool documents, validated with cwltool and run at scale on Toil/Arvados/Calrissian. Use when deciding CWL (portability/provenance/regulated) vs Nextflow/WDL/Snakemake; declaring secondaryFiles for indexed companions (.bai/.fai/.dict/.tbi and the caret rule); putting resources/containers under requirements (must-hold) vs hints (advisory) to avoid silent OOM; choosing scatterMethod (dotproduct vs flat_/nested_crossproduct); preferring $(...) parameter refs over ${...} JavaScript for portability; pinning DockerRequirement images; or emitting a CWLProv provenance object for audited/clinical settings.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan, implement, or improve an internationalization and localization strategy for UI content, formatting, and regional adaptation. Use when the user asks to add i18n, localize, translate, support multiple languages, handle regional formats, manage locale switching, or build a multilingual product.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manage MCP tools with natural language commands — list, enable, disable tools and tool groups
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extract key clauses from terms and conditions documents, then generate a plain-language PDF summary for client review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reads and sets the weather on a running CARLA server via get_weather/set_weather, and turns natural-language weather requests ("heavy rain at sunset", "light fog at night", "clear noon", "dust storm") into exact WeatherParameters — cloudiness, precipitation, puddles, wind, fog, sun position, wetness, dust. Use when the user asks to "set/change the weather", "make it rainy/foggy/sunny/night", "apply a weather preset", or describes any sky/lighting condition.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.