「insight」の検索結果

Builds a product strategy and stress-tests your moat, then writes a one-page strategy doc (diagnosis, where to play, how to win, guiding policy, coherent actions, moat verdicts). Use for 'we need a product strategy', 'is our moat real', 'do we have a competitive advantage', 'where should we play', 'defensibility', 'should we expand into X', 'our roadmap has no strategy', or 'write a product vision'. Draws on 909 insights from 236 Lenny's Podcast guests incl. Roger Martin, Richard Rumelt and Hamilton Helmer.

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。

Writes (or rewrites) a spec, PRD, PR-FAQ or shaped pitch and grades it against what the best product teams actually do. Use when you ask 'write a PRD', 'how do I spec this', 'PR/FAQ', 'working backwards doc', 'shape this project', 'do we even need a PRD', or 'engineering says the spec isn't detailed enough'. Draws on 56 insights from 38 Lenny's Podcast guests incl. Ryan Singer, Bill Carr and Lane Shackleton.

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。

Turns a hard product or career decision into a one-page decision memo with the eigenquestion, options, pre-mortem with kill criteria, reversibility rating, a named decider and a review date. Use when you ask 'how should we decide', 'should we build or kill this', 'one-way door or two-way door', 'run a pre-mortem', 'we keep going in circles', 'quit or pivot', or want to grade a decision doc. Draws on 396 insights from 177 Lenny's Podcast guests, led by Annie Duke, Shreyas Doshi and Shishir Mehrotra.

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。

Diagnose where your growth is actually leaking (acquisition, activation, retention, or loops) and produce a growth plan with a named loop, an activation definition, a 90-day experiment portfolio and channel guardrails. Use it when someone says growth stalled, signups don't activate, retention is flat, how do we build a growth loop, should we go PLG, onboarding redesign, churn, referral program, or which channel should we bet on. Draws on 672 insights from 143 Lenny's Podcast guests, led by Elena Verna, Casey Winters and Lauryn Isford.

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。

Builds a pricing model, packaging/tier structure and willingness-to-pay research plan, then grades your existing pricing page or plan. Use when you ask 'how much should we charge', 'what should our pricing model be', 'seat vs usage vs outcome pricing', 'good-better-best tiers', 'what goes in the free plan', 'should we raise prices', 'freemium vs free trial', or 'how do we price our AI product'. Draws on 199 insights from 79 Lenny's Podcast guests including Madhavan Ramanujam, Naomi Ionita and Patrick Campbell.

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。

Designs, sanity-checks and reads out product experiments (A/B tests, holdouts, pre/post, geo and incrementality tests) and produces an experiment brief plus a readout with a ship / hold / replicate call. Use when you ask 'should we A/B test this', 'how long should the test run', 'is this result real', 'sample size', 'stat sig', 'SRM', 'holdout', 'our win rate is low', or need to grade an existing test plan or readout. Draws on 191 insights from 71 Lenny's Podcast guests, led by Ronny Kohavi, Ramesh Johari and Archie Abrams.

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。

Diagnoses product-market fit from survey, usage and pipeline data and returns a scored verdict with the next moves. Use when you ask 'do we have product-market fit', 'is our PMF survey good enough', 'what does 35% very disappointed mean', 'should we scale yet', 'why is growth stalling', or need a Sean Ellis survey, retention check, or PMF level read. Draws on 137 insights from 66 Lenny's Podcast guests incl. Sean Ellis, Todd Jackson and Rahul Vohra.

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。

Builds your north star metric, a metric tree with owned input metrics and guardrails, and a review cadence, as a one-page metrics spec you can paste into a doc. Use it when you ask 'what should our north star be', 'which metric should this team goal on', 'our dashboard is a mess', 'how do we measure AI productivity', 'activation metric', 'OKR metrics', 'vanity metrics', or 'metrics review cadence'. Draws on 254 insights from 118 Lenny's Podcast guests incl. Sarah Tavel, Crystal Widjaja and Jessica Lachs.

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。

Rewrite an exec update, memo, strategy doc, deck outline, Slack post or talk so it lands with senior people, and get a graded score plus the top 3 fixes. Use when you say 'rewrite this for my VP', 'exec update', 'how do I present to the CEO', 'my memo is too long', 'prep me for this review', 'all-hands talk' or 'I get nervous presenting'. Draws on 296 insights from 90 Lenny's Podcast guests incl. Wes Kao, Nancy Duarte and Matt Abrahams.

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。

Builds a launch plan, launch story and press/distribution plan, then drafts the pitch emails and announcement copy. Use when you say 'launch plan', 'how do I get press', 'pitch a reporter', 'announce funding', 'go-to-market for a feature', 'Product Hunt', 'press release', 'launch messaging' or 'we shipped and nobody noticed'. Draws on 394 insights from 124 Lenny's Podcast guests, led by Emilie Gerber, Lulu Cheng Meservey and Jason Feifer.

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。

【深度调研专用】当需要系统性调研、多产品对比、深度分析、技术选型、趋势分析时必须使用本技能。触发关键词：调研、对比、分析、评估、选型、趋势、综合报告、多源验证。通过 3 层深度搜索（阅读多个网页）+ 多源交叉验证，提供比普通 WebSearch 更全面、更准确的研究结果和深度洞察。禁止用普通搜索工具替代深度调研任务。

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。

Conduct comprehensive deep research on any topic using Dify-powered workflow - searches documentation, academic papers, tutorials, APIs, best practices, and returns structured analysis with insights.

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。

Extract and persist insights from the current conversation to the knowledge base

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。

Enable continuous learning mode for automatic insight extraction

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。

Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past sessions", "extract lessons from all sessions", "save learnings", "update CLAUDE.md with what we learned", "capture session insights", "remember this for next time", "extract lessons", "update skills from session", or "what did we learn".

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。

Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past sessions", "extract lessons from all sessions", "save learnings", "update AGENTS.md with what we learned", "capture session insights", "remember this for next time", "extract lessons", "update skills from session", or "what did we learn".

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。

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

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。

Read your Instagram niche and profile from real data via Apify, no login. Scan a hashtag for the posts traveling now (likes, comments, owner) to see the format and hook that works. Pull profile stats for any handle, yours or a competitor's: followers, posts, bio, category. Instagram hides who liked or commented on other accounts, so this is discovery plus profiles, not engagers. Triggers on "what works in my niche", "scan the hashtag", "competitor stats". Not for writing captions (use ig-caption-writer).

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 operating Inngest API resources from the terminal with `npx inngest-cli@latest api`: Cloud/local run debugging, event-run lookup, function traces, function invocation, app syncs, webhooks, environments, keys, account checks, and Insights queries. Provides prescriptive command routing for agents: which CLI command to run for a run ID, event ID, app ID, function ID, Cloud environment, API key, missing ID, or potentially mutating operation. Use `inngest-cli` for dev server setup/general CLI workflows and `inngest-api` only when raw REST API v2 docs or OpenAPI fallback are needed.

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。

Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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 needs to analyze, categorize, or extract actionable insights from customer feedback across multiple sources, especially feature requests.

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 has raw customer interview transcripts, survey responses, support tickets, or other qualitative data and needs to extract actionable insights.

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。

Analyze large-scale construction datasets. Process thousands of projects for patterns, benchmarks, and predictive insights.

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。

Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.

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。