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Found 2,054 Skills
Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.
Build and execute brand marketing strategy for B2B companies — thought leadership, ABM brand layer, trust signals, LinkedIn presence, long sales cycle brand touchpoints, and enterprise credibility. Use when the user says "B2B marketing", "B2B brand", "business to business marketing", "selling to companies", "enterprise marketing", "we sell to businesses", "B2B brand strategy", "thought leadership strategy", "B2B content marketing", "account-based marketing brand", "B2B positioning", "how do we build trust with buyers", or is marketing a product or service sold to other businesses rather than consumers.
Read-only multi-agent review of a GitHub Pull Request, with the synthesized report posted back as a PR comment so the author is notified. Use when the user wants to review a GitHub PR (github.com or GitHub Enterprise) and post a structured review back to the PR conversation. Auto-detects the PR from the currently checked-out branch when no locator is supplied. Requires `gh`, `uuidgen`, `jq`, and `uv` or `python3` on PATH. Activates the `review-anvil` engine in read-only mode and orchestrates the shell helper for posting.
Use when the user asks to "build audience segments from my customer list", "make value-based / lookalike seed lists", "set up exclusion / suppression segments", or "map audiences to funnel stages across platforms"; turns the user's OWN customer/CRM/GA4 export into seed audiences, value-based lookalike SEED lists, exclusion/suppression segments, and a cross-platform funnel-stage targeting map, informing the ROAS A (Audience) dimension. Not for building account structure or match types — use campaign-architect; not for organic SERP intent — use keyword-research. 付费广告受众分群/种子人群/排除人群/相似人群种子
Probe a URL with escalating headless browser configurations to detect CDN bot protection (Akamai, Cloudflare, DataDome, AWS WAF) and produce a browser-recipe.json that downstream playwright-cli consumers use to bypass blocking. Runs an automated escalation ladder: default headless → stealth script injection → system Chrome (TLS fingerprint fix) → persistent profile. Use BEFORE any playwright-cli interaction with an untrusted domain. Triggers on: browser probe, site blocked, headless blocked, CDN blocking, bot detection, browser recipe, can't load page, 403 error page, access denied.
Shopee（虾皮）SBS 仓储服务（与 linkfox-shopee-store-auth 同系列），经 /shopee/developerProxy 转发 Shopee Open API SBS 模块全部 5 个接口：get_bound_whs_info、get_current_inventory、get_expiry_report、get_stock_aging、get_stock_movement。当用户提到 Shopee SBS、仓储库存、绑定仓库、get_bound_whs_info、库龄报表、效期报表、库存变动 时触发。即使未明确提及"SBS"，只要涉及已授权 Shopee 店铺的 SBS 仓储与库存数据查询，也应触发。
Shopee（虾皮）媒体上传 MediaSpace（与 linkfox-shopee-store-auth 同系列），经 /shopee/developerProxy 转发 Shopee Open API MediaSpace 模块全部 6 个接口：init_video_upload、upload_video_part、complete_video_upload、get_video_upload_result、cancel_video_upload、upload_image。当用户提到 Shopee 上传图片、上传视频、media_space、init_video_upload、upload_image、视频分片上传、获取Shopee图片URL 时触发。即使未明确提及"媒体"，只要涉及已授权 Shopee 店铺的图片/视频文件上传，也应触发。
Shopee（虾皮）店铺站内广告 Ads（与 linkfox-shopee-store-auth 同系列），经 /shopee/developerProxy 转发 Shopee Open API Ads 模块 23 个接口：get_total_balance、create_manual_product_ads、get_product_campaign_daily_performance、GMS 广告等。调用前须确认目标店已有 appType=ad 授权（ERP 授权不能代替）。当用户提到 Shopee 广告、Ads、广告余额、CPC、商品推广、手动广告、campaign、广告效果、ROI、get_total_balance、广告授权 时触发。即使未明确提及"广告"，只要涉及已授权 Shopee 店铺的广告账户、推广或效果查询，也应触发。
Read Hyperliquid (app.hyperliquid.xyz) perp + spot market data via opencli (read-only, public info API). Use whenever the user wants Hyperliquid perpetual or spot markets, mark/oracle/mid prices, 24h change, funding rates (hourly or annualized APR), open interest, volume, the L2 order book, OHLCV candles, historical funding, or a cross-venue funding comparison (Hyperliquid vs Binance vs Bybit) for funding arbitrage. Triggers: "Hyperliquid funding for BTC", "HL perp markets", "funding on BTC perp", "Hyperliquid order book", "HL open interest", "funding arb Hyperliquid vs Binance", "Hyperliquid candles for SOL", "Hyperliquid spot markets", "PURR price on Hyperliquid", "hyperliquid", "hyperliquid.xyz", "HL DEX". READ-ONLY market data — no account, order, or trade operations.
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).
Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.
Applies Neil Rackham's SPIN methodology (Situation/Problem/Implication/Need-payoff questions) to major B2B sales. Use for complex multi-call sales cycles, enterprise deals where the customer must justify the decision to others, when objections are mounting, when calls end in vague continuations instead of advances, when traditional closing techniques are backfiring on large deals, or when designing discovery-call structure. Triggers include 'my deal isn't closing', 'too many objections', 'B2B sales coaching', 'discovery call structure', 'stuck in the middle of the sale'. Not for transactional sub-$50 sales, pure consumer impulse, or PLG self-serve products.