Results for “keyword-difficulty”

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Find keyword gaps, SERP opportunities, and low-competition high-intent keywords

Layer 1d of the keyword research pipeline. Mines question-shaped keywords from Semrush phrase_questions (per surviving seed) plus People-Also-Ask strings from the SERP. Appends rows to keyword-ideas.csv with source=question_mining (or merges to source=both when the keyword already exists) and a question_subtype column. Cap of 100 rows per run.

Gather keyword metrics, related terms, questions, full SERP benchmark, top-page extractions, and deep web research for a target keyword, then emit a beat spec the outline must satisfy. Triggered by /research <keyword> or as the first content stage of /blog-pipeline.

Analyzes keyword usage in provided content, calculates density, suggests semantic variations and LSI keywords based on the topic. Prevents over-optimization. Use PROACTIVELY for content optimization.

Layer 1b of the keyword research pipeline. Finds keyword opportunities by comparing the brand's blog against competitors AND by expanding seeds + modifiers via Semrush (phrase_fullsearch / phrase_related). Auto-discovers competitors via domain_organic_organic when none are provided, derives the keyword gap via domain_domains, tags every row with `gap_mode`, and outputs a candidate-keyword CSV ready for downstream BID/AIO vetting.

Reduces cognitive complexity of a specified method by extracting logic into focused helper methods, improving readability and maintainability.

Validate Pleasur.ai Stage 01 blog-keyword candidates with the Business potential, Intent, and Difficulty method using current Ahrefs evidence. Use before AIO vetting and prioritization to reject poor business fits, mismatched search intent, and SERPs the brand cannot realistically win.

Write exam questions with difficulty progression. TRIGGERS - Use when user needs help with exam-writer related tasks.

Optimize a LinkedIn profile — headline, about, experience, and keywords — for recruiter search and credibility.

A stakeholder asks to make search faster.

Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

Hard Call

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

Explain a hard concept at a target level using a concrete analogy and a check for understanding.

Use this skill when a user asks for SEO keyword research, keyword discovery, search volume analysis, keyword difficulty, search intent mapping, topic clusters, content opportunities, competitor keyword gaps, or a keyword strategy for a domain, URL, product, market, or seed topic. When a website is provided, crawl and interpret the site first, then use AIsa API access to DataForSEO keyword, SERP, trend, Labs, and OnPage endpoints plus AIsa LLM reasoning to find non-brand keyword opportunities. Use when: the user needs web search, research, source discovery, or content extraction.

Multi-phase dialectical stress-test for HIGH-STAKES decisions only (architecture choices, irreversible product calls, strategic bets). Heavy-cost skill — do NOT use for routine questions, brainstorming, or simple tradeoffs. User must explicitly invoke or describe a decision they call "high-stakes", "irreversible", or "needs stress-testing". Triggers: "stress test this decision", "dialectic on", "challenge this thesis", "should I really".

Manage software complexity through deep modules, information hiding, and strategic programming, with a scoring framework for evaluating design quality.

Generate routed vocabulary challenges for kaoyan English. Default is 12 words split 6→6; 27-word 6→9→12 is challenge-only. Script-routed selection, SM-2, 5-layer explanations.

Turn accepted search evidence into an intent, competitor, and information-gain brief.

Build and sharpen a project's domain model — a CONTEXT.md glossary and ubiquitous language. Use when pinning down terminology, or the agent "uses the wrong words". Repo decision-memory system (INDEX.md, rejected alternatives) → docs-adr.

Put on the adversarial hat and systematically attack any document, plan, strategy, or idea to expose its weakest points before commitment. Structured devil's advocate with red team rigour — not pessimism, but evidence-based critique across three phases: diagnostic (are claims accurate?), creative (is the problem artificially constrained?), challenge (are solutions robust?). Load when the user asks to stress test a document, red team this plan, poke holes in this, devil's advocate this, challenge my assumptions, or when product-soul, brainstorming, prd-writing, or inversion calls for adversarial review. Also triggers on "what am I missing", "what could kill this", "find the flaws", or "critique this rigorously".

Layer 0 of the keyword research pipeline. Builds a topic-graph snapshot for the brand's category before any seed work, approximated from Semrush phrase_related + phrase_questions on the category seeds plus the brand's own ranking footprint (domain_organic_unique). Idempotent on brand-config hash; never blocks the pipeline; cheap.

Generate 5–10 business idea candidates from a blank page or a founder's domain context — using pain mining, jobs-to-be-done, trend × capability mapping, constraint relaxation, adjacency search, and founder-market-fit prompts. Each candidate is a structured idea card (segment, JTBD, current alternative, why-now, distribution wedge, monetisation, "feels like"). Load when the user asks to generate business ideas, brainstorm startup ideas, find ideas to work on, says "what business should I start", "give me startup ideas", "I don't know what to build", "ideate ventures", "blank-page idea generation", "find me a startup idea", "explore business opportunities". Sub-skill of `venture-exploration`. Hard-bans "Uber for X" / "AI for X" with no specific JTBD, "everyone" segments, and idea cards missing any of the 7 required fields. Does NOT design or evaluate ideas generated — for that use `idea-evaluation`.

Post-draft pass that expands a drafted article to capture the FULL keyword family — keywords the page already or could rank for, plus the Ahrefs Content Gap (keywords competitors rank for but we don't) — by weaving the worthwhile ones in as natural added paragraphs/sections. NOT keyword stuffing. Triggered after /draft, before /quality-check.

Open-Vocabulary Object Detection Using Captions

Improve interface resilience through better error handling, i18n support, text overflow handling, and edge case management. Makes interfaces robust and production-ready.

Retrieve relevant information from a knowledge base using semantic, keyword, or hybrid search to ground a query. Use when the task starts with a corpus or index that must be searched; use context-ranking when candidate chunks already exist and only need ordering.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Tier vocabulary from a text or topic into everyday, academic, and technical categories with teaching priorities. Use when pre-teaching vocabulary or identifying language barriers in a text.

Analyzes multiple provided pages to identify keyword overlap and potential cannibalization issues. Suggests differentiation strategies. Use PROACTIVELY when reviewing similar content.

Analyzes algorithm time and space complexity, classifies problems by complexity classes, proves NP-completeness, and designs approximation algorithms.

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).