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Found 1,943 Skills
Analyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.
Consult Gemini AI for architecture alternatives, design trade-offs, and brainstorming. Use when seeking different perspectives on design, evaluating architectural approaches, comparing solutions, or generating creative ideas.
Retrieve analysts' price target summary for any stock using Octagon MCP. Use when evaluating analyst sentiment, upside/downside potential, consensus expectations, and tracking target trends over time.
Retrieve stock price change statistics across multiple time periods using Octagon MCP. Use when analyzing short-term and long-term returns, comparing performance across timeframes, and evaluating momentum and historical growth.
Prevents sycophantic reasoning through checklist-based analysis, categorical integrity, and evidence-following to uncomfortable conclusions. Red Flags: "I agree that", "You are right", "Great point", "Absolutely", "That is a fair point", "I can see why", agreement without validation, softening conclusions, hedging without evidence, retracting under pressure. Use when analyzing conflicts, evaluating ethical claims, or noticing sycophantic thought patterns. Do not use for routine implementation or simple factual questions. CRITICAL: This skill overrides conversational tendencies toward politeness. Agreement requires validity and accuracy.
Analyze arguments, detect biases, evaluate claims, and improve reasoning. Use when asked to fact-check, identify logical fallacies, evaluate arguments, analyze predictions, find root causes, or think adversarially about plans. Triggers include "evaluate this argument", "logical fallacies", "fact check", "analyze the claims", "identify biases", "devil's advocate", "red team this", "root cause".
Orchestrate security finding validation through graduated exploitation. 4-phase pipeline: recon (SAST/DAST), analysis (code review), validation (exploit proof), report (No Exploit, No Report gate). Eliminates false positives by proving exploitability.
Write titles for blog posts, deep dives, and hub articles. 15 proven formulas + 10 Commandments evaluation. Generate 10+ options, select best through systematic criteria.
Score assistant responses for relevance on a strict 1-5 scale, then return strict JSON only with score, rationale, and improvement suggestions. Use when the user asks to evaluate relevance, grade relevance, or critique topical alignment.
Conduct multi-dimensional comparative analysis based on user-input technical options or project requirements, and output structured technology selection reports. Applicable scenarios: front-end framework selection, back-end technology comparison, database selection, deployment solution evaluation
Score how well a creator fits a brand's niche on a 1-10 scale with detailed written rationale. This skill should be used when evaluating creator-brand fit, scoring niche alignment, checking if an influencer matches a brand, assessing creator relevance, rating a creator's fit for a campaign, vetting a creator for niche match, deciding whether a creator is right for a brand, comparing creators by brand fit, or reviewing an influencer's profile against campaign requirements. For full creator vetting beyond niche fit (brand safety, rates, compliance), see creator-vetting-scorecard. For writing outreach to creators who pass vetting, see outreach-writer.
Fetch, organize, and analyze LangSmith traces for debugging and evaluation. Use when you need to: query traces/runs by project, metadata, status, or time window; download traces to JSON; organize outcomes into passed/failed/error buckets; analyze token/message/tool-call patterns; compare passed vs failed behavior; or investigate benchmark and production failures.