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Found 403 Skills
When the user wants to optimize for entity recognition, Knowledge Graph, or entity-based SEO. Also use when the user mentions "entity SEO," "entity optimization," "Knowledge Graph," "Knowledge Panel," "entity signals," "brand entity," "entity linking," "entity relationships," or "entity-first content."
People.ai (now Backstory) platform help — automatic activity capture, deal intelligence, pipeline health, revenue forecasting, MCP integration, Salesforce/Dynamics/Oracle CRM sync. Use when reps aren't logging activities and CRM data is stale, deals are slipping without warning and you need early risk signals, forecast accuracy is poor because it's based on gut not data, evaluating People.ai vs Gong vs Clari vs Revenue.io for revenue intelligence, activity data isn't tying back to pipeline or revenue outcomes, or you want to connect People.ai to AI agents via MCP. Do NOT use for conversation intelligence with call recording and transcription (use /sales-gong or /sales-note-taker), building outbound sequences (use /sales-cadence), or general CRM data cleanup strategy (use /sales-data-hygiene).
Analyze articles for AI-generated content indicators and rewrite to pass WeChat's 3.27 non-human automated content creation detection. Checks for template phrases, transition word density, sentence uniformity, paragraph pattern repetition, and other signals that WeChat uses to flag AI content. Outputs a risk report and an optional humanized rewrite. Use when the user wants to check if an article looks AI-generated, make an article more human-like, bypass WeChat AI detection, or humanize AI-written content. Also trigger when the user mentions "去AI痕迹", "人性化润色", "微信AI检测", "anti-ai-check", "humanize article", "公众号发文检查".
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Assess chemical and drug toxicity via adverse outcome pathways, real-world adverse event signals, and toxicogenomic evidence. Integrates AOPWiki (AOPWiki_list_aops, AOPWiki_get_aop) for mechanism- level pathway tracing, FAERS for post-market adverse event quantification, OpenFDA for label mining, and CTD for chemical-gene-disease evidence. Produces structured toxicity reports with evidence grading (T1-T4). Use when asked about toxicity mechanisms, adverse outcome pathways, AOP mapping, FAERS signal detection, or chemical-disease relationships for drugs or environmental chemicals.
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
When the user wants to improve their ability to recognize buying signals, ask for the sale, and confidently move deals to commitment. Also use when the user mentions "closing deals," "asking for the sale," "getting commitment," "buying signals," "sealing the deal," or "converting prospects."
Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart money signals, and an AI rating based purely on token structure. Use when user asks about holder analysis, 筹码分析, 持仓分析, chip structure, who is holding, or whether a token is safe to buy based on its holder composition.
Analyze articles for AI-generated content indicators and rewrite to pass WeChat's 3.27 non-human automated content creation detection. Checks for template phrases, transition word density, sentence uniformity, paragraph pattern repetition, and other signals that WeChat uses to flag AI content. Outputs a risk report and an optional humanized rewrite. Use when the user wants to check if an article looks AI-generated, make an article more human-like, bypass WeChat AI detection, or humanize AI-written content. Also trigger when the user mentions "去AI痕迹", "人性化润色", "微信AI检测", "anti-ai-check", "humanize article", "公众号发文检查".
Generate, refine, research, and validate startup ideas through an interactive founder-specific idea machine. Use when Codex needs to ask a few focused questions, search current public web signals, rapidly brainstorm startup or SaaS ideas, learn from love/maybe/no reactions, produce additional idea rounds, compare finalists, investigate competitors and existing workarounds, select a promising opportunity, or create an evidence-linked Markdown startup-idea report.
Install and operate Hermes Tweet, a Hermes Agent plugin for X/Twitter research, timeline reading, tweet analysis, and approval-gated tweet actions. Use this skill when installing Hermes Tweet, researching X/Twitter accounts, monitoring launch signals, investigating mentions, auditing giveaways, or preparing guarded tweet actions. Use proactively when a Hermes Agent workflow needs current X/Twitter context. Requires XQUIK_API_KEY for read and action tools.
Detects entropy signals in a codebase: stale TODOs, disabled tests, lint suppressions, commented-out code, dead imports, empty catch blocks, and deprecated API usage. Designed for daily runs to catch quality erosion early. Do NOT use for feature work, refactoring planning, or security audits.