「lora」の検索結果

Apply MCAF ML/AI delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. Use when the repo includes model training, inference, data science workflows, or ML-specific delivery planning.

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File a librarian query result back into the library as a new page with provenance tracking. Karpathy's "good answers can be filed back into the wiki as new pages" insight made concrete. The library compounds from explorations, not just from external sources.

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Prospective failure analysis using Gary Klein's pre-mortem technique. Assumes complete failure, works backward to identify risks, leading indicators, and circuit breakers. Counters optimism bias by forcing systematic exploration of failure modes before they materialize. Use for project plans, architecture decisions, technology adoption, business strategy, or feature launches. Triggers on "리스크", "위험", "실패하면", "swing-mortem", "뭐가 잘못될 수 있어", "risk", "what could go wrong", "걱정되는 점", "failure modes", "리스크 분석", "위험 분석".

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Generate probability-weighted alternative options that challenge default thinking. Forces unconventional alternatives and exposes hidden assumptions behind the "obvious" choice. For decision-point analysis, NOT full design exploration. Triggers on "대안", "alternatives", "옵션 뽑아", "options", "어떤 방법이", "아이디어", "다른 방법", "선택지".

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Trains the HUMAN user's critical thinking through four modes: drill (argument-analysis items with a single defensible answer, judge stance), scene (Socratic exploration of synthetic scenes or user-supplied material, no verdicts on interpretations; includes a configure track — design the information request and verification plan for a decision before any analysis), expedition (guided audit of impossible-tier problems from verified packs), and detective (a runtime-generated multi-layer case worked as an escape room, guide-and-judge stance). Use when the user wants to practice critical thinking, analyze arguments, hunt assumptions, examine bias, train reasoning, or decide what information a decision needs first. Triggers: critical thinking practice, train my thinking, drill, scene, byom, configure, information plan, what would I need to know, spot manipulation tactics, scam literacy, 批判思考練習, 話術辨識, detective, 查案, 破案, 偵探.

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Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.

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Diagnoses and fixes Claude behavioral issues in system prompts and Projects using tested countermeasure templates. Use when "Claude is too verbose," "keeps hedging," "agrees with everything," "claims it did something it didn't," "won't use a tool," "fix Claude's output," "tune Claude's behavior," "Claude ignores my preferences," "adds unsolicited disclaimers," "uses too many lists." Covers ten tendencies: agreeableness (output-content + persistent-preference), hedging, verbosity, list overuse, fabricated precision, over-exploration, tool miscalibration (over- and under-triggering), LaTeX defaulting, editorial drift, self-referential fabrication. Also use when auditing a system prompt for behavioral calibration or recalibrating a pre-4.7 prompt, and when users describe recurring output problems without naming a tendency. Do NOT use for scoring a prompt's overall quality — use rootnode-prompt-validation if available.

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Contribute to OpenClaw ecosystem projects (OpenClaw, NemoClaw, NanoClaw) through a structured 9-step workflow: target verification, codebase exploration, parallel audit, finding cross-reference, and pull request creation. Emphasizes false positive prevention and project convention adherence.

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Shared conventions for every SDLC development-phase architect agent: hard rules, code quality bar, workflow steps (superpowers invocation, spec reading, codebase exploration, verification), and the report/compact-summary contract. Architects load this skill first, then apply their stack-specific instructions on top.

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Use when the user requests a deep dive, exploratory analysis, or data analysis on BigQuery. Not for credential, publish, deploy, or irreversible changes.

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Use when an exact symbol, path, entrypoint, or line range can bound a focused code question or patch proposal under a fixed source budget. Not for source changes or broad repository exploration.

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This skill should be used when the user asks to "explore the app", "/qa", "auto-test the app", "find bugs in my app", "猴子测试", "自动探索", "随便点一下看看", "测一下全 app 看有没有崩", "smoke explore", or wants autonomous exploration of an Android or iOS app to surface crashes / ANRs / unexpected states. Drives the app via ui-mcp + mobile-mcp, tracks visited pages and clicked elements via report-mcp's state graph, captures crashes via log-mcp, and produces a Markdown report with bug list + coverage stats.

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This skill should be used when the user says "找 bug" / "看一下有没有 bug" / "smart-qa" / "/smart-qa" / "帮我测一下这个项目" / "智能 QA" / "推断业务流程测一下"，or asks for an autonomous bug-hunt where they DON'T already know what to test. Distinct from `qa` (blind exploration) and `devtest` (verifies a specific change). smart-qa READS the project source — prefers PRD/requirements docs, falls back to code (activities, routes, click handlers, API calls) — then proposes a focused test plan for confirmation before driving the app. v1 stops after the user confirms the plan and hands off to the existing `qa` skill for execution. v2 (planned) adds per-step assertions.

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End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

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Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.

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Generate an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects transcripts.

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Query Windsor.ai business data across marketing, sales, CRM, ecommerce, finance, and analytics connectors. Use when users need dashboards, reports, data visualization, schema exploration, or connector-backed test data from Windsor.ai.

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Handles NoSQL document operations and collection hierarchy exploration. Use for CRUD tasks and data retrieval. Provides flexible document manipulation and structured querying.

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 standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.

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Build, review, and browser-test interactive website page or module HTML from a confirmed UI Architecture Map and visual direction. Use for HTML First Drafts, restrained motion implementation, full-page or module-first prototypes, rendered design review, responsive and interaction QA, page composition, and implementation handoff. Do not use for design exploration, unconfirmed visual or motion direction, content strategy, Design Systems, Elementor controls, CMS templates, or framework components.

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Use when brainstorming ideas, generating raw material, or starting the creative phase of a writing project. Invoke for topic exploration, idea generation, angle discovery, or producing abundant raw content before organizing.

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Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

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Search and explore an Obsidian vault through the NoteBrain CLI (semantic search, tags, backlinks, connections, hidden links, boosted retrieval). Use it whenever the user mentions their notes, knowledge base, Obsidian vault, semantic search, finding connections or unlinked notes, or asks exploratory questions like "what do I know about X", "find notes related to Y", "what connects to Z", or "summarize my notes on W" — even when they never say NoteBrain, vector search, or ChromaDB.

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。