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Found 156 Skills
Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".
Challenge an outbound campaign copy by benchmarking it against the user's existing campaigns — what worked, what didn't, what the winners do differently — and return a concrete verdict plus prioritized fixes. Use whenever the user wants to know if a campaign or sequence is good, compare a draft to past campaigns, audit campaign copy against real performance, pressure-test a sequence before launch, validate a sequence before going live, or asks 'is this campaign as good as my best ones'. Triggers on: 'challenge this campaign', 'benchmark this sequence', 'is this campaign good', 'audit my copy', 'pressure-test before launch', 'compare to my best campaigns', 'should I launch this'. Pulls existing campaign performance from the La Growth Machine MCP when connected; otherwise works from stats and copy the user pastes; falls back to a best-practice baseline when there is no campaign history. For SDR, RevOps, Growth, Head of Sales/Marketing, founders launching outbound. Maintained by La Growth Machine.
Estimate fair market rates for creator partnerships based on platform, follower count, engagement rate, niche, and deliverable type. This skill should be used when estimating influencer rates, calculating creator pricing, building a rate card for a campaign, checking if a creator's rate is fair, comparing influencer costs across platforms, budgeting for a creator campaign, evaluating a creator's rate card, figuring out how much to pay an influencer, benchmarking creator rates against market data, or assessing whether a creator is overcharging. For negotiating rates after estimation, see rate-negotiation-playbook. For full creator vetting beyond pricing, see creator-vetting-scorecard.
Terminal-Bench integration for Mux agent benchmarking and failure analysis
Live Google Search Console analytics — fetches real SEO data (clicks, impressions, CTR, rankings) and delivers actionable insights with CTR benchmarking and opportunity detection. Zero dependencies. Use when the user asks about GSC, Google Search Console, SEO performance, search performance, keywords, rankings, organic traffic, top pages, top queries, "how is my site performing in Google", "check rankings", or "search console report".
Autonomous experiment loop that tries ideas, measures results, keeps what works, and discards what doesn't. Use when the user asks to optimize a metric, run an experiment loop, improve performance iteratively, or automate benchmarking.
Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when writing load tests, optimizing test execution speed, or setting up pytest infrastructure.
Customer feedback, NPS, CSAT, CES, Voice of Customer strategy across platforms — survey design, response rate optimization, closed-loop feedback, text analytics, benchmarking, program governance. Use when NPS scores are stagnant, survey response rates are low, feedback isn't driving action, unsure which CX metric to use, need to design a VoC program, comparing feedback tools (Medallia vs Qualtrics vs SurveyMonkey vs Typeform), or customers feel over-surveyed. Do NOT use for product review collection like Trustpilot or G2 (use /sales-customer-reviews) or in-app message surveys (use /sales-in-app-messaging).
Finds qualified candidates for a role by searching LinkedIn, Indeed, GitHub, and other professional platforms using Nimble Web Search Agents. Accepts a job description, role title, or freeform request and returns a ranked candidate list with profiles, skills, and contact signals. Use this skill when the user wants to find, source, or recruit candidates for a role. Common triggers: "find candidates for", "source engineers in", "who can I hire for", "find me a [role]", "recruiting for", "talent search", "find a [role] in [city]", "build a candidate list", "sourcing for [role]", "who's available for", "find potential hires". Also triggers on a pasted job description followed by a sourcing request. Do NOT use for job market research or salary benchmarking — use market-finder instead. Do NOT use for researching a single known person — use company-deep-dive or meeting-prep instead.
Use when you need to add or configure Maven plugins in your pom.xml — including quality tools (enforcer, surefire, failsafe, jacoco, pitest, spotbugs, pmd), security scanning (OWASP), code formatting (Spotless), version management, container image build (Jib), build information tracking, and benchmarking (JMH) — through a consultative, modular step-by-step approach that only adds what you actually need. This should trigger for requests such as Add Maven plugins in pom.xml; Improve Maven plugins in pom.xml. Part of cursor-rules-java project
Social listening and brand monitoring strategy — monitoring, Boolean queries, sentiment, competitive intel, crisis detection, AI visibility monitoring, LLM brand mentions. Platform comparison (Meltwater, Brandwatch, Talkwalker, Brand24, Sprout Social, Mention, Hootsuite, BrandJet, Influencity), monitoring setup (keywords, sources, alerts), sentiment analysis, competitive benchmarking (share of voice), crisis detection (real-time alerts, escalation), consumer insights, and reporting. Use when you don't know what people are saying about your brand, competitors are getting mentioned more than you, negative sentiment is spiking and you need to understand why, you're missing PR crises until it's too late, you can't tell if your brand shows up in AI/LLM answers, or you need to pick the right social listening tool. Do NOT use for platform-specific config (use /sales-meltwater), influencer discovery (use /sales-influencer-marketing), social media publishing/scheduling, or SEO keyword research (use /sales-semrush).
[Hyper] Optimize an existing Codex skill through baseline-first experiments, binary evals, optional guards, and one-mutation-at-a-time iteration. Use for skill autoresearch, measured trigger/workflow improvement, self-optimizing a skill, benchmarking skill changes, or resuming skill experiment artifacts.