「capture」の検索結果

Capture architectural decisions made during Claude Code sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.

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Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

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When the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For signup/registration flows, see signup. For post-signup activation, see onboarding. For popups/modals, see popups.

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 narrated video of a feature running in the local dev app, for a PR or a stakeholder. Drives a headless Chrome over the DevTools Protocol so clicks and keystrokes are real, captures frames, and encodes an mp4 with no extra tooling installed. Use when the user asks to "record a video", "make a demo", "show this working", or wants a screen recording of a change.

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 analytics with PostHog in a TypeScript app — capturing events, identifying users, adding feature flags, tracking errors, or wiring API lifecycle telemetry. Triggers on edits to analytics integration files, or mentions of "PostHog", "analytics", "feature flag", "event tracking", "capture", "identify", "A/B test", or "experiment".

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Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.

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Give an AI agent its own real browser over MCP tool calls - launch, navigate, click, fill, screenshot, extract text, run JS - with a kernel-level real-device fingerprint and a persistent profile, so the session stays logged in between runs and pages see one coherent device instead of a headless build. No Playwright or SDK code to write. Use when an agent should operate a site itself, when a computer-use / browser-use setup needs a captured real fingerprint rather than a synthetic one, when agent sessions keep losing their login, or when comparing hosted agent-browser services. Also for 'MCP browser', 'browser MCP server', 'let my agent browse the web', 'agent browser control', 'browser-use MCP', 'computer use browser', 'Browserbase alternative', 'Steel browser alternative', 'headless browser detected'. Node (npx) or Python; Windows x64, macOS Intel + Apple Silicon, Linux x64 / arm64. SDK and REST reference is anti-detect-browser; account isolation is multi-account-isolation.

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Manage 4todo (4to.do) from chat. Capture tasks, prioritize with the Eisenhower Matrix, reorder, complete, and manage recurring tasks across workspaces.

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Capture meeting notes with clear structure: attendees, agenda, decisions, action items with owners and deadlines. Ensure accountability and alignment post-meeting.

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。

Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.

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。

Ask Claude, ChatGPT, and Gemini a question and capture the full trace behind the answer (the search queries each engine ran, the pages it retrieved, and the sources it cited, span by span). Use when asked to "trace an AI answer", "see what AI engines cite", "check AI citations", "which sources does ChatGPT/Claude/Gemini use", "why does the AI recommend X", or for any GEO (Generative Engine Optimization) measurement.

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 all IP-level testcases from tc_list.md through the bench Makefile, capture results, and write per-failure issue reports with no fix suggestions

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Search, read, create, edit, and maintain knowledge in a Memex KB via the `mx` CLI. Use when the user asks to search the KB, find documentation, check project or organizational docs, add or update knowledge-base entries, audit KB quality, or when reusable knowledge should be captured for future agents.

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Capture durable project or workflow knowledge discovered during work. Use when a mistake, hidden constraint, repeated question, or changed behavior reveals a documentation gap. To root-cause one specific mistake the agent just made and let the user pick fixes, use `learn` (`/learn`) instead.

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PyTorch Inductor pattern matcher 机制开发：扩展本仓 compile 能力（default/Inductor 后端）， 或扩展三方框架自带的类似能力——Pattern 创建/注册/调试、GraphPatternEntry 手动改图、 Copy 算子（InplaceCopy/ViewCopy）消减的机制与全生命周期，含 Phase 1–7 出口证据纪律。 当用户需要写/改/调试 pattern、处理融合不命中、做 Copy/InplaceCopy 消减或注册 replacement 时 使用；即使用户只说"pattern 不生效""copy 算子变多了""compile 没融合"而未说全称，也应触发 （触发词：pattern / pattern matcher / register_replacement / GraphPatternEntry / compile 融合 / Copy 消减）。 边界：批量下发（图 capture/replay）见 aclgraph-dev；算子本体开发见 operator-dev； 框架侧特性使能/补齐见 framework-integration。 由 dev-workflow 的 pattern/compile 开发场景路由触发，亦由 model-auto-optimization 的 S1 kernel 融合（接入/compile）替换场景指向（框架侧使能不支持、需新增/适配 pattern 时）。

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Drafts U.S. corporate board-of-directors meeting minutes into formal, defensible records documenting valid corporate action and governance process. Triggers on board minutes, directors meeting record, board resolutions, quorum validation, motion and vote capture, corporate secretary records, governance drafting. Requires source materials such as agenda, attendance, bylaws, notices, reports, prior minutes, and resolutions.

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Capture, gate, and query long-term project knowledge via the gated wiki subsystem: propose candidates, recall, or search; agents propose, humans approve. NOT for short-lived memory (mk:memory).

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JSON-canonical session memory in .meowkit/memory (Markdown is a generated view). Capture learnings, patterns, costs; loads at Orient, persists at Reflect. NOT for retrospectives (mk:retro).

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Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).

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Coordinates staged or parallel jobs across live-verified coding-agent runtimes (Claude Code, Codex, Cursor, and other installed CLIs) and in-session toolkit subagents. Routes each job by capability and risk tier, isolates parallel writers in git worktrees, captures redacted output, resumes interrupted runs, and blocks completion until an independent arbiter verifies the results. Use when work should be split across multiple runtimes or subagents, run as staged or parallel jobs, and reviewed before handoff. NOT for a single-agent task or the in-session 7-phase flow (use mk:cook / mk:workflow-orchestrator).

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Record Claude Code sessions with asciinema. TRIGGERS - record session, asciinema record, capture terminal, demo recording.

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Close out a session — summarise what changed, capture what was learned, and prepare the commit.

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Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

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Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.

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