「feed」の検索結果

Use when the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号

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 the user asks to "grow my email list", "design a lead magnet / signup incentive", "set up double opt-in", or "plan a referral / recommendation loop"; produces a list-growth plan — acquisition channels, lead-magnet / incentive concepts, a compliant double-opt-in capture-flow spec, referral-loop mechanics, and subscriber-growth / cost-per-opt-in targets (labeled Estimated) — that feeds SEND-S (consent quality captured at acquisition) and SEND-N (lifecycle entry). Not for the signup page/popup UX itself — use landing-optimizer; not for recording the opt-in — use consent-registry; not for the confirmation-email copy — use email-creative-builder. 邮件列表增长/lead magnet/双重确认/推荐环

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。

Plan and run product or feature launches from positioning and tiering through asset readiness, launch-day orchestration, feedback and momentum.

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 the user asks to "check if our surfaces have drifted from the canon", "watch for competitor repositioning", or "define when we should reposition"; produces a drift report — self-drift per flagship surface vs the narrative-registry canon over time (via wayback.py, change history Measured with as-of dates), competitor-repositioning alerts, an explicit repositioning-trigger condition set, and a D1/W1/M1 message-shift retro (intended vs actual pull-through, evidence-labeled) — feeding the TALE L drift-audit sub-items and the narrative-whiplash guardrail fact base. Not for the first-time consistency check before a surface ships — use narrative-cascade-planner; not for computing the TALE profile result or running the vetoes — use narrative-quality-auditor; not for echo-rate / AI-answer resonance measurement — use narrative-resonance-monitor. 自漂移监测/竞品重定位告警/重定位触发/叙事漂移复盘

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 the user asks to "review this influencer content" or "check if this post meets brand guidelines"; runs the typed STAR pre-publish gate, scores Trust and Appeal on the deliverable, folds in the creator Suitability read, computes the profile-weighted SQS, checks the disclosure/claim/brand-safety and fraud/fake-engagement vetoes, and writes constructive revision feedback. Not for drafting the brief — use brief-generator; not for partnership terms — use contract-helper. 达人内容审核/发布前质检

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。

When the user wants to edit, review, or improve existing marketing copy, or refresh outdated content. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' 'sharpen the messaging,' 'refresh this content,' 'update this page,' 'this content is outdated,' or 'content audit.' Use this when the user already has copy and wants it improved or refreshed rather than rewritten from scratch. For writing new copy, see copywriting.

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 the user asks to "design an early access program", "set up a waitlist and beta stages", or "define beta graduation criteria"; produces a waitlist→concept→alpha→beta→GA stage ladder with per-stage purpose and opt-in semantics, quantified graduation criteria per stage (labeled Estimated), a cohort-gating and invite-throttling plan, tester recruitment with launch-day social-proof prep, a feedback-loop spec where every status change notifies its subscribers, and a referral-loop mechanism spec (invite codes, anti-abuse). Not for waitlist acquisition strategy or the capture-flow spec — use list-growth-designer; not for the canonical stage record — use launch-registry. waitlist/内测阶梯/抢先体验/毕业标准/反馈闭环

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。

Riverbed Memory の suppression entry を活用するときの判断基準と CLI 操作を案内する。

Checks whether AI-assisted work defines review criteria, accessible context, explicit review loop, human judgment boundary, and feedback capture before delegating to an agent.

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。

Turn post-publish notes into pipeline improvements. Use after a video ships, or when the user says "retro", "here is my feedback", or "wrap up this project".

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。

AI-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA pipelines.

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。

Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or drafting proposed host automations for report-only review.

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。

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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。

Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.

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。

Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing...

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。

Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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。

Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.

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。

TanStack Virtual headless virtualization for React. Use when rendering large lists (100+ items), implementing virtual scroll, building infinite scroll feeds, virtualizing grids or tables, using window-level scrolling, or implementing masonry/lane layouts with @tanstack/react-virtual. Triggers on: useVirtualizer, useWindowVirtualizer, virtual list, virtual scroll, list virtualization.

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。

Set up and optimize repositories for AI coding agents. Creates minimal AGENTS.md, CLAUDE.md symlink, docs/REQUIREMENTS.md, docs/BUSINESS-RULES.md, feedback loops, and deterministic enforcement (Claude Code hooks, OpenCode plugins). Use when user wants to make a repo AI-friendly, set up AGENTS.md/CLAUDE.md, document requirements/business rules for AI, add pre-commit hooks for AI workflows, or optimize codebase structure for coding agents.

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。

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

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 with Codescene — codeScene behavioral code analysis including code health scoring, hotspot detection, change coupling analysis, and PR integration for code quality gates. Use when configuring CodeScene for codebase visualization, technical debt prioritization, and automated code review feedback.

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 with Coderabbit — codeRabbit AI code review management including automated review comments, auto-suggestions, review customization, and learning from feedback. Use when configuring CodeRabbit for AI-assisted code review, custom review rules, and automated suggestion workflows.

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 with Bloomreach — bloomreach commerce search and merchandising platform management including catalog feeds, search configuration, autosuggest, category pages, SEO, A/B testing, pixel tracking, and recommendation widgets. Covers search relevance, catalog health, widget performance, and content personalization 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。

Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.

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。