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Found 6,614 Skills
Autonomous research agent that reads RESEARCH.md, infers what's needed, dynamically adjusts TODOs, and delegates to the right skill. Supports opt-in BFS mode for autonomous design space search. Respects a configurable supervision policy (presets: manual / checkpointed / autonomous / wild) governing notifications, approval gates, resource limits, and idea-change handling. Proactively surfaces gaps and asks before acting. Trigger phrases: "start research", "continue project", "what's next?", "explore design space", "autoresearch".
Prepare a day-one patch for a game launch. Scopes, prioritises, implements, and QA-gates a focused patch addressing known issues discovered after gold master but before or immediately after public launch. Treats the patch as a mini-sprint with its own QA gate and rollback plan.
When implementing or reviewing web interfaces, refer to this set of practical guidelines from the Traditional Chinese version of raunofreiberg/interfaces' *Web Interface Guidelines* to ensure that details such as interactivity, visuals, and accessibility meet the expectations of a 'good interface'.
Complete launch readiness validation covering every department: code, content, store, marketing, community, infrastructure, legal, and go/no-go sign-offs.
Decision frameworks for DatoCMS content modeling — schema shape, field choice, content reuse, taxonomies, content vs presentation, admin UI organization. Use for modeling *decisions*, not implementation: model vs block; single_block vs Modular Content vs Structured Text; references vs embedded blocks; taxonomy shape (flat/tree/faceted); refactoring page-shaped schemas to reusable content; fitting 300 KB / 500-block / 5-level record limits; model behaviour (singleton, draft mode, all_locales_required, sortable/tree/ordering_field, presentation_title_field, collection_appearance, inverse_relationships_enabled); field config (validator + appearance — enum + string_select, slug auto-fill, required_alt_title, structured_text allowlists, framed vs frameless single_block). Also schema review (reuse, editor ergonomics, omnichannel). *Creating* schema → `datocms-cli` or `datocms-cma`. Query/render → `datocms-cda` + `datocms-frontend-integrations`. Validators + cascade: `datocms-cma/references/schema.md`.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Testing strategy for Plate/Slate editor work — pure unit tests, plugin contract tests, golden serializer tests. Use when planning test layers, auditing a flaky suite, or deciding what to skip.
Generate Huawei Cloud Terraform configurations and execute deployment with user-guided approval. Use this skill when users want to create Huawei Cloud infrastructure as Terraform, whether they ask explicitly for Terraform or describe goals such as deploying a website, launching an application, or creating network, compute, database, load balancing, or storage resources. Trigger when users mention 创建/create、生成/generate、部署/deploy、配置/configure、使用/use、管理/manage、华为云/Huawei Cloud、Terraform、ECS、VPC、资源/resource、云服务器/ECS、虚拟机/VM、实例/instance、网络/network、负载均衡/ELB、数据库/database、RDS、存储/storage、OBS、桶/bucket、域名/domain、DNS、证书/certificate、SSL、监控/monitoring、日志/log、备份/backup、容器/container、CCE、函数工作流/FunctionGraph
Analyzes encryption algorithms, key management, and file encryption routines used by ransomware families to assess decryption feasibility, identify implementation weaknesses, and support recovery efforts. Covers AES, RSA, ChaCha20, and hybrid encryption schemes. Activates for requests involving ransomware cryptanalysis, encryption analysis, key recovery assessment, or ransomware decryption feasibility.
Full-pipeline iOS App Store opportunity research. Discovers underserved niches, analyzes competitor gaps, estimates revenue, produces scored top-3 opportunity reports, and writes MVP PRDs — all through browser and web research. Use when the user wants to find profitable iOS app ideas, research App Store charts, analyze competitor apps (ratings, reviews, revenue, gaps), generate opportunity reports, or write MVP PRDs. Triggers on "find app opportunities", "app store research", "what app should I build", "research this app category", "find a gap in the app store", "ios app ideas".
Find focused, runnable Deepgram recipes for a specific feature × language. Use whenever someone wants a minimal working code snippet for ONE feature (transcribe URL, diarize, smart-format, voice agent connect, etc.) rather than a full starter app. Recipes are under 50 lines, read DEEPGRAM_API_KEY from env, and ship with a runnable example_test. Covers Python, JavaScript, Go, .NET, Java, Rust, and the Deepgram CLI.
Use version control as a craft — atomic commits, buildable history, useful PRs, bisect-friendly main, recoverable mistakes. Use this skill whenever the task involves writing commits or PRs, choosing a branching model, deciding rebase vs. merge, recovering from a force-push or accidentally-committed secret, debugging a regression with `git bisect`, structuring a long change as a series of small reviewable steps, or judging whether a repo's history is readable. Use it especially when reviewing commit messages, PR descriptions, branching strategies, or merge policies. Built on Tim Pope and Chris Beams on commit messages, Paul Hammant on trunk-based development, Vincent Driessen on GitFlow (and his 2020 note retiring it for SaaS), Linus Torvalds on never rebasing public commits, and the Google Engineering Practices CL guide.