「eac」の検索結果

World-class level design expertise - spatial storytelling, player flow, blockout methodology, and the invisible hand that guides players without them knowingUse when "level design, blockout, graybox, whitebox, player flow, level layout, combat arena, exploration space, map design, multiplayer map, pacing, weenie, landmark, gating, metroidvania, open world design, linear level, environmental storytelling, teaching through design, playtesting, heatmap, metrics, sightlines, level-design, game-development, spatial-design, blockout, graybox, player-flow, pacing, environmental-storytelling, combat-design, multiplayer-maps, open-world, linear-design, metroidvania" mentioned.

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Use when implementing GDPR compliance, handling data subject requests, conducting DPIAs, managing consent, or responding to data breaches - covers all key GDPR requirementsUse when ", " mentioned.

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Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, landing in spam folders. This skill covers transactional email that works, marketing automation that converts, deliverability that reaches inboxes, and the infrastructure decisions that scale. Use when "keywords, file_patterns, code_patterns, " mentioned.

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Expert at building resilient applications through proper error handling. Covers Result types, error boundaries, try-catch patterns, typed errors, and graceful degradation. Use when "error handling, try catch, error boundary, Result type, exception, error-handling, typescript, react, resilience" mentioned.

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Expert in the business of online education - pricing strategies, launch playbooks, revenue models, positioning, and building sustainable education businesses. Covers everything from pricing a first course to scaling an education company. Use when "price my course, course launch, sell my course, course business, education business, course revenue, monetize teaching, " mentioned.

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Expert in building and scaling channel partner programs - resellers, agencies, system integrators, and technology partners that extend your reach and revenue. Covers partner program design, enablement, incentive structures, co-selling motions, and marketplace strategies. Knows when channel makes sense and when direct is better. Use when "channel partner, reseller, VAR, agency partner, partner program, co-sell, marketplace, distribution partner, " mentioned.

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Your most loyal users aren't just customers - they're potential advocates, contributors, and community members who can become the engine of your growth. Community-led growth isn't about broadcasting to an audience - it's about creating a space where your users connect with each other, share knowledge, and become invested in your success. This skill covers community strategy, platform selection, engagement tactics, ambassador programs, and the metrics that matter. Whether you're building a Discord for developers or a forum for power users, the goal is the same: creating genuine value that makes members want to stay and contribute. Use when "community, discord, forum, user group, ambassador, champions, developer relations, devrel, community engagement, community growth, power users, superfans, community, discord, engagement, devrel, ambassador, forum, user-community, growth, retention, champions" mentioned.

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CI/CD is the backbone of modern software delivery. Continuous Integration catches bugs early. Continuous Deployment gets features to users fast. But poorly designed pipelines can be slow, flaky, and a source of constant frustration. This skill covers GitHub Actions (the most popular), GitLab CI, and general pipeline design principles. The focus is on pipelines that are fast, reliable, and maintainable. 2025 reality: Your pipeline is infrastructure code. It deserves the same care as your application. A flaky test in CI is worse than no test - it teaches developers to ignore failures. Use when "github actions, gitlab ci, ci cd, pipeline, workflow, deploy automation, continuous integration, continuous deployment, build pipeline, yaml workflow, ci-cd, github-actions, gitlab-ci, devops, automation, deployment, pipelines" mentioned.

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The art and science of narrative-driven brand communication. This skill transforms brand messages into compelling stories that connect emotionally, build memory, and drive action. In a world of infinite content, story is the only thing that cuts through. Great brands don't sell products—they tell stories. Apple tells the story of creative rebels. Nike tells the story of human potential. Patagonia tells the story of environmental stewardship. This skill teaches you to find, craft, and tell your brand's story across every touchpoint. AI amplifies storytelling capability—generating variations, adapting across channels, maintaining consistency—but the human insight that identifies the true story remains essential. This skill bridges narrative craft with AI execution. Use when "story, storytelling, narrative, brand story, origin story, hero journey, emotional connection, customer story, testimonial, case study, brand film, about us, storytelling, narrative, brand-story, emotional, hero-journey, content" mentioned.

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The fusion of trend prediction and AI content generation: detecting emerging trends before they peak and generating content that rides—or creates—the wave. This is marketing alchemy, transmuting data signals into viral gold. Traditional trend marketing is reactive: see trend, create content, hope you're not too late. AI Trend Alchemy is predictive: detect signal, generate content, be there as the wave crests. The combination of AI trend detection and AI content generation creates a feedback loop that can outpace human competition. This skill combines data science intuition with creative generation, reading the digital tea leaves while simultaneously conjuring the content that captures the moment. It's part analyst, part creator, part prophet. Use when "trend, trending, viral, predict, emerging, what's next, cultural moment, before it peaks, early signal, catch the wave, trend prediction, going viral, trend, prediction, viral, cultural, emerging, timing, alchemy, detection" mentioned.

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The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.

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Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuningUse when "adding search to, algolia, instantsearch, search api, search functionality, typeahead, autocomplete search, faceted search, search index, search as you type, algolia, search, instantsearch, indexing, relevance, faceted-search, autocomplete" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Mastery of AI image generation across the full spectrum: Midjourney for aesthetic perfection, Flux for prompt adherence, DALL-E 3 for concept clarity, Stable Diffusion for control, and Imagen 3 for photorealism. This skill transforms text into visual reality at the speed of thought. We've moved beyond "AI can make pictures" to "AI is the fastest concept artist, product photographer, and visual designer ever created." The skill isn't just prompting—it's understanding how each model thinks, what makes images work, and how to systematically produce exactly what you envision. The best AI image generators have internalized composition, lighting, color theory, and style—not by studying art, but by generating thousands of images and learning what works. They're visual directors who happen to work in text. Use when "AI image, generate image, Midjourney, DALL-E, Flux, Stable Diffusion, Imagen, text to image, AI art, AI photo, AI illustration, AI visual, AI graphics, generate picture, ai-image, midjourney, dall-e, flux, stable-diffusion, imagen, generation, text-to-image, visual, art" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Angular is opinionated and comprehensive - it gives you everything: routing, forms, HTTP, dependency injection, testing. The learning curve is steep, but once you're in, you move fast. The structure it enforces is why enterprises love it. This skill covers Angular 17+, standalone components, signals, the new control flow syntax, and modern Angular patterns. Key insight: Angular's power is in its DI system and RxJS integration. Master those, and everything else follows. 2025 lesson: Standalone components are the future. NgModules aren't going away, but new projects should start standalone. Signals are Angular's answer to fine-grained reactivity - learn them. Use when "angular, angular component, angular service, angular routing, angular forms, rxjs, ngrx, angular signals, standalone component, angular ssr, angular, typescript, frontend, spa, enterprise, rxjs, signals, standalone" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Find local-business SEO prospects in a single command. Searches Google for "<niche> <city>", filters out aggregators (Yelp, Yellow Pages, Angi), enriches each remaining domain with DataForSEO Labs domain overview, On-Page technical audit, and Backlinks summary in parallel, then ranks the businesses by a closeability score (0-100) — highest scores are the easiest to land as SEO clients.

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On-demand rank checking via the DataForSEO SERP API. Looks up live Google organic positions for a domain across an arbitrary list of keywords. Reports each as winning / page-1 / close / not-ranking with one prioritized next action per keyword.

日本語の概要は準備中です。原文の説明を表示しています。

Draft a personalized cold email to a prospect based on the SEO audit findings. Reads the audit JSON for the given domain, picks the 2-3 most impactful issues, and writes an outreach email that references specific data (current ranking, traffic value, named issues) so it doesn't look like spam.

日本語の概要は準備中です。原文の説明を表示しています。

Show when you last had a 1:1 with each direct report / peer, sorted by recency. Flags people who haven't had one in N weeks. Powered by the OneOnOnes view on Meetings.base.

日本語の概要は準備中です。原文の説明を表示しています。

Batch-create Teams/People/@Name.md stubs from unresolved [[@*]] wikilinks across the vault. Each stub is minimal frontmatter; the user fills team/role/details later.

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Triage Meetings/_inbox/ — move each file to the correct subfolder by finding adjacent/similar notes via qmd, applying learned routing conventions from memory, and confirming with the user.

日本語の概要は準備中です。原文の説明を表示しています。

Synthesize the single strongest EVIDENCE-BOUND reviewer case to reject a paper, built ONLY from the evidence ledger (claims.json) + the other auditors' confirmed findings — never free-floating LLM critique. Two fresh cross-model codex threads: an attack writes the ~200-word rejection paragraph (every accusation tagged to an existing claim_id/finding_id), a defense decomposes it and rules each point against the anchored evidence. MEMO-ONLY: emits adversarial-case-builder.memo.md (fed to the adjudicator via --memo) and carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Honest-null allowed (the paper may survive). Run LAST. Detect-only. Adapted from ARIS kill-argument. Triggers: "adversarial case", "strongest objection", "rejection memo", "kill argument", "最强拒稿点".

日本語の概要は準備中です。原文の説明を表示しています。

Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation — verdict-bearing at L1 (the LaTeX source; PDF-extracted math is unreliable, so an L0 PDF-only run surfaces info only) — never asserts 'fabricated', only that the step shown does not hold. A fresh cross-model reviewer reads the theorem/proof + an extraction-only obligation scaffold and proposes per-obligation findings, each span-anchored to the evidence ledger (claims.json); reviewer≠adjudicator. Emits proof-derivation-forensics.findings.json; NEVER computes the verdict. dimension=proof, can be critical. Triggers: "proof forensics", "check this proof", "derivation integrity", "audit the math", "证明审计", "推导有没有漏洞".

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MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP fuzzy-title + boolean · WebSearch · WebFetch) from the paper's own title + contribution spans in the evidence ledger; TWO fresh cross-model codex reviewers (one per axis) LAY OUT the overlap side-by-side against each anchored contribution claim. It NEVER rules 'trivial' or 'duplicate' (that is a human judgment) and absence of a match is NOT evidence of originality. Emits novelty-duplication-advisory.memo.md + an info-only findings mirror; carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Detect-only. Adapted from ARIS novelty-check, reframed from 'is MY idea novel' to 'here is the overlap a reviewer should weigh'. Triggers: "novelty advisory", "duplication check", "prior-work overlap", "is this stapling", "缝合", "查重", "重复发表", "duplicate submission".

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Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent (HP-WEAK-BASELINE); 'outperforms' is asserted over overlapping error bars or with no variance/seeds (HP-SIG-OVERLAP); and a cross-row 'improves over baseline by X%' is arithmetically wrong (HP-DELTA-ERROR, cross-row form only). A versioned per-domain baseline profile + a live leaderboard/recency search are assembled by the EXECUTOR as structured facts; a fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per dimension) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Works at L0 (stated comparisons) and deepens at L2 (configs/result files). A completeness question it cannot settle internally becomes needs_external_check, never a guessed missing baseline. Emits baseline-comparison-audit.findings.json; computes NO verdict. Detect-only. Triggers: "baseline audit", "missing baselines", "is the comparison fair", "weak baseline", "baseline 误报", "SOTA earned?".

日本語の概要は準備中です。原文の説明を表示しています。