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Found 2,054 Skills
Use to write the hook — the opening that earns attention — for any social content: a caption's first line, a video's first three seconds, a carousel cover slide, a thread opener, a YouTube title, or an email subject. Run when the user says "write a hook," "hook for this," "opening line," "first three seconds," "cover slide," "make this scroll-stopping," or when good content keeps getting ignored. Reads brand-profile and voice first so hooks sound like the brand, not viral-bait templates. Hooks must be TRUE to the content that follows — this skill extracts the hook from the post's strongest element and never overpromises. For full captions use caption-writer; for full video scripts use the video skills. This writes the opening itself.
Social media analytics and reporting — read native platform data honestly and turn it into next actions. Use when someone wants to "check my analytics," "see how my posts are doing," "build a social media report," "which content is working," "what metrics/KPIs should I track," or to turn performance data into next steps. Measures goal-mapped SIGNAL metrics (saves, shares, watch time/retention, engagement-rate-by-reach, follower-growth-rate, CTR, conversions) — not vanity (followers/impressions/likes) — and closes the loop. Uses the METER framework. Reads brand-profile + social-strategy (goals) first. WoopSocial has NO analytics surface, so this reads NATIVE platform dashboards (+ GA4/UTM) and interprets numbers the human provides; it NEVER fabricates a metric. Feeds content-recycling, experimentation, competitor-analysis, and every growth skill. Distinct from goals-and-kpis (sets targets) and experimentation (runs tests).
Shopee（虾皮）Public 公共模块（与 linkfox-shopee-store-auth 同系列），经 /shopee/developerProxy 转发 Shopee Open API Public 模块全部 6 个接口：get_shops_by_partner、get_merchants_by_partner、get_access_token、refresh_access_token、get_token_by_resend_code、get_shopee_ip_ranges。当用户提到 Shopee Public API、Partner 店铺列表、get_shops_by_partner、OAuth token 交换、refresh_access_token、Shopee IP 白名单 时触发。日常授权流程优先 linkfox-shopee-store-auth；需直接调用 v2.public.* 开放接口时触发本 skill。
Shopee（虾皮）联盟营销 AMS Affiliate Marketing Solutions（与 linkfox-shopee-store-auth 同系列），经 /shopee/developerProxy 转发 Shopee Open API AMS 模块全部 36 个接口：get_open_campaign_added_product、batch_add_products_to_open_campaign、create_new_targeted_campaign、get_affiliate_performance、get_shop_performance 等。当用户提到 Shopee 联盟营销、AMS、达人带货、affiliate、Open Campaign、Targeted Campaign、佣金率、达人推广、get_open_campaign_added_product 时触发。即使未明确提及"联盟"，只要涉及已授权 Shopee 店铺的联盟推广或达人 campaign 管理，也应触发。
Shopee（虾皮）套装优惠 Bundle Deal（与 linkfox-shopee-store-auth 同系列），经 /shopee/developerProxy 转发 Shopee Open API Bundle Deal 模块全部 10 个接口：add_bundle_deal、get_bundle_deal_list、add_bundle_deal_item、update_bundle_deal、end_bundle_deal 等。当用户提到 Shopee 套装优惠、Bundle Deal、组合促销、add_bundle_deal、bundle_deal_id、满件优惠 时触发。即使未明确提及"套装"，只要涉及已授权 Shopee 店铺的 Bundle Deal 活动管理，也应触发。
Take one or many customer-interview files you already have and extract the AJTBD structure from them — segments by Core Jobs, personas, Consideration Set, existing Solutions and Problems, value hypotheses — using Ivan Zamesin's AJTBD / Next Move Theory methodology (distinct from generic Christensen JTBD). Input — a folder or list of files: deep-interview transcripts, interview notes, sales-call or demo transcripts, support/chat logs, survey open-ends. The interviews may be AJTBD or not, well- or poorly-conducted, one file or dozens. The skill first asks which business task you're solving (and helps you choose if you can't name one), then reads each interview in its own subagent (a fan-out so it never overflows context, no matter how many large transcripts), extracts the Core Jobs with an honest per-interview confidence (a clean extraction vs. a weak hypothesis), gives per- interview feedback (what was pulled, what's missing, whether this interview can even serve your business task), clusters the extractions into segments by similar Core Jobs + similar success criteria + similar priority order, and computes each segment's confidence from the supporting interviews' confidence. Output — one report: a data-quality summary, segments by Core Jobs with personas and confidence, structured existing Solutions and Problems, a Consideration Set per segment, value-creation hypotheses, and a gap list of what to interview next. Use when the user says "analyze my interviews", "extract jobs from these transcripts", "I have customer interviews — find the segments", "what jobs are in these calls", "synthesize my interviews", or has interview/transcript files and wants the methodology pulled out of them. The post-fieldwork counterpart to /nmt-interview-guide. Two modes — Quick (default, no internet) and Deep (subagents + web to enrich competitors and the Consideration Set). Plain language; defaults to English.
OpenTelemetry in Java — Javaagent zero-code instrumentation, Spring Boot Starter, manual autoconfigure SDK, declarative YAML configuration, BOM dependency management, sensitive-data capture and redaction (url.query, headers, request parameters, SQL sanitization). Use when adding, reviewing, or configuring OpenTelemetry in a Java service. Triggers on "setup otel in java", "java telemetry", "javaagent", "Spring Boot otel", "GlobalOpenTelemetry", "AutoConfiguredOpenTelemetrySdk", "TracerProvider java", "url.query redaction", "capture request headers", or any Java-related OTel question.
Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions.
Build well-crafted production procedural meshes in Three.js. Use for complete hard-surface assemblies and humanoid robots, profile extrusion, parameter-curve and spine lofts, pillow panels, exact polygon cuts, inset, revolve, sweep, solidify, bevels and fillets, shell thickness, direct-topology apertures, semantic mesh writers, or diagnosing primitive-built forms, coplanar flicker, loose/non-manifold geometry, detached parts, interpenetration, support, clearance, and swept-envelope defects.
Diagnose and interpret AE/TE A/B experiments from configuration and report evidence through a defensible decision. Use when the user asks what an experiment means, whether it can roll out, why a result is not significant, why group sizes or exposure are wrong, why treatment results conflict, whether the report is trustworthy, or what to do next. Covers SRM, duration sufficiency, novelty effects, metric conflicts, missing or anomalous data, design reasonableness, data reliability, metric interpretation, trend and segment analysis, root-cause hypotheses, and rollout recommendations. All platform discovery and reads must use ae-cli.
Benchmarks a trained Physical AI Studio policy in a simulation gym and reports success metrics. Use when running physicalai benchmark, editing configs under library/configs/benchmark, adding or changing a Benchmark class in physicalai.benchmark, tuning rollout/episode/env settings, recording rollout videos, or interpreting results.json / results.csv.
Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).