2.4천건 · 관련도순
PR and community reconnaissance — audit current press coverage, social presence, community health, and competitor PR. Use when asked to "audit our PR", "what's our community state", "how do we compare in press", or before planning a launch or community initiative.
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Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.
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Conducts a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.
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Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.
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Use when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.
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Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV export.
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Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.
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Compare explicit design/specification contracts with the implementation in a defined change scope. Use to verify signatures, types, schemas, invariants, and other stated contracts without expanding into general code quality review.
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Track and analyze US government shutdown liquidity impacts by monitoring TGA (Treasury General Account), bank reserves, EFFR, and SOFR data from FRED API. Use when user wants to (1) analyze current or past government shutdown effects on financial markets, (2) track liquidity conditions during fiscal policy disruptions, (3) assess "stealth tightening" effects, (4) compare shutdown episodes across different monetary policy regimes (QE vs QT), or (5) generate liquidity stress reports with historical context. Recommended usage frequency is weekly on Wednesdays after TGA/reserve data releases.
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An AI analyst for buying B2B software. Investigates what vendors claim, checks the evidence, surfaces contradictions and unanswered questions, and produces a shareable Decision Brief with the questions to ask before you buy. Quick Eval by default; Deep Eval on request. Use when asked to evaluate, compare, vet, or research B2B software vendors.
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Grill the user about a requirement, decision, or idea before implementation, then produce an actionable planning report without writing code. Use when the user wants to clarify requirements, stress-test an idea, compare approaches, or plan before coding.
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Replay recorded promotions through a tree's own rule without models or spend. Read which promotions the rule supports, what unsuccessful attempts missed by, their A/A controls, and hours and dollars per promotion. Compare a baseline and a candidate that changes one rule.
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Generate semantic + keyword match score. Compares resume content against job description to calculate a fit score and identify gaps.
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Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.
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Turn a YouTube video idea into a designer-ready thumbnail brief: focal subject, face and emotion, a text overlay of 4 words or fewer that the title does not repeat, contrast and composition, and 2 to 3 A/B concepts for Test and Compare. Built on 2026 principles (one focal point, legible at 120 pixels, complements the title). Outputs the brief plus the Publora attach step (the image upload itself is out of band). Use to plan a thumbnail. Not for title words (use yt-title-optimizer).
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Write high-CTR YouTube and YouTube Shorts titles under 100 characters using 2026 packaging formulas (curiosity-gap, number, how-I outcome, mistake/loss, transformation, versus). Balances curiosity with a real keyword, front-loads the click-deciding words for the 60-char mobile cutoff, and returns 3 to 5 A/B variants tagged by goal for Test and Compare. Use to title or rework a video. Not for the description (use yt-description-writer) or the thumbnail overlay (use yt-thumbnail-brief).
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Develops B2B SaaS positioning from your ICP, competitor and win-loss evidence. Maps what buyers compare you to, chooses between leading with a category or a problem, picks a primary and secondary anchor and up to three defensible differentiators, lays out 2 to 4 worked positioning options with a comparison canvas so you can decide, then locks the chosen one and tests it (clarity ladder, guarantee test). Triggers: "positioning", "how do we position", "what category are we in", "how do we differentiate from", "our homepage is confusing", "positioning options". Run before /messaging. Not for taglines or a messaging library (use /messaging).
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Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.
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Evaluate alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.
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Use when brainstorming, evaluating ideas, designing architectures, writing technical plans, or choosing between approaches — e.g. "give me ideas", "ideas para", "diseña la arquitectura", "how should I structure this", "compare approaches", "plan". Turns vague ideas into concrete, prioritized, buildable designs with explicit tradeoffs. Use before writing code, not after.
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Use when analyzing competitors, creating sales battlecards, building competitive positioning, preparing for competitive deals, or updating competitive intelligence. Triggers: 'battlecard for [competitor]', 'competitive analysis of [competitor]', 'how do we beat [competitor]', 'compare us to [competitor]', 'competitive intel', 'win against [competitor]'.
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Reverse-engineer a supplied video into a shot-by-shot generation specification, displayed evidence frames, and paired Chinese-English prompt packages. Use for requests such as 视频反推提示词, 视频转提示词, 从视频生成提示词, 视频逆向提示词, 逐镜头反推, 分镜拆解并生成提示词, 反推视频 prompt, or video-to-prompt; also use for Veo, Sora, Kling, Wan, generic prompt adaptation, and bounded generate-compare-optimize loops. Do not use for ordinary video summaries or basic transcription.
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Weekly marketing audit and operating review. Pulls the last 7 days of source-backed marketing, analytics, content, paid media, lead, watcher, and audit data; compares it to the prior 7 days; flags urgent issues; and produces a weekly scorecard plus action list. Use when "weekly audit", "weekly marketing review", "weekly check-in", "weekly scorecard", "what changed this week", "Friday marketing review", or any request for a recurring 7-day marketing audit.
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Daily ad performance check-in across platforms. Pulls live metrics from Meta, Google, and LinkedIn via deterministic scripts, compares against benchmarks and previous period, flags overspend/underperformers/policy issues, and outputs a quick daily summary with action items. Use when "daily ad review", "how are my ads doing today", "ad check-in", "morning ad report", "daily ad summary", "check ad performance", "ad dashboard", "daily ads", or any request for a recurring or quick-glance ad performance review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.