1.9천건 · 관련도순
Analyzes current state and user query to answer BMad questions or recommend the next skill(s) to use. Use when user asks for help, bmad help, what to do next, or what to start with in BMad.
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Meta-skill — browse and discover all installed stellar.new skills. Use when a user says "what skills do I have", "show me available skills", "what can I do", "find a skill for X", "which skill should I use", "help me navigate", "what does stellar.new do", or "where do I start". Lists skills by journey phase, surfaces the SKILL_ROUTER trigger phrases, and recommends the next skill given current context.
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Review Stellar Community Fund (SCF) Build Award applications. Use when evaluating SCF submissions, covering project fit assessment, technical architecture evaluation, team readiness, traction validation, budget analysis, and funding recommendations.
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Resize a product photo to Shopify's recommended 2048×2048px square format, sharpen, and convert to WebP for fast storefront load times.
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Smart crop a product photo to Etsy's recommended 2000×2000px square format and export as JPEG.
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Generate a formatted PDF compliance audit document with findings, risk ratings, remediation recommendations, and sign-off sections.
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Unity 6 UI development guide. Use when building user interfaces, menus, HUD, buttons, or any UI elements. Covers UI Toolkit (recommended for new projects — USS, UXML, UI Builder, data binding), uGUI/Canvas (legacy runtime UI), and IMGUI. Based on Unity 6.3 LTS documentation.
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Put two or more Japanese job offers on one basis so they can be read side by side, separating fixed overtime allowance and unguaranteed bonus from the stated annual figure, converting each offer to guaranteed annual pay and to an hourly rate that includes deemed overtime hours, and listing non-monetary terms without scoring them. Use when a user holds more than one offer, wants to know what a stated 年収 actually contains, or wants to see where the ranking changes once the components are separated; do not use to recommend which offer to accept, to score or rank offers, to estimate take-home pay as fact, or to reply to an employer.
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Turn vague preferences about employers into criteria that can actually be used to choose between them: for each one, what would have to be seen to confirm it, whether it comes from firsthand experience or from an untested assumption, whether it is a must, a nice-to-have, or something to avoid, and which pairs of criteria pull against each other while both being treated as required, then test the set against real candidates. Use when a new graduate is working out what to select employers on, when someone who has decided to move needs criteria for where to apply, or when a stated axis like "a place I can grow" needs to be made checkable; do not use to recommend an industry, job, or employer, to score or rank candidates, or to judge someone's stated preferences.
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Work out the order and the loose ends before turning down an offer or withdrawing from a selection process: whether the offer being kept is actually settled in writing, whether each decline is mid-selection, post-offer, or after acceptance, who besides the employer has to be told when the application came through an agent, a school recommendation, or a referral, which reply deadlines have passed, and what documents, expenses, or borrowed items are still outstanding. Use when a user holds more than one offer and needs to decline the rest, wants to withdraw from a process they are no longer pursuing, or has been asked to turn down other employers as a condition; do not use to decide whether to decline, to rule on what declining after acceptance means legally, or to send any message.
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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM; Liu 2022 *NAR Genom Bioinform* 4:lqac046), MeTPeak (HMM over sliding windows; Cui 2016 *Bioinformatics* 32:i378), MACS3 / MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment confirmation via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output formats, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union; cross-caller IDR-equivalent), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody-based methods cannot resolve, and orthogonal-validation routes (miCLIP / GLORI / m6A-SAC-seq / m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set as a whole, reconciling differing peak sets across tools, validating MeRIP peaks against orthogonal single-base methods, interpreting peaks at the 5' end where m6Am contamination is possible, or recommending a multi-tool consensus strategy.
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Map the repository and recommend the next CodeConductor slash command. Read-only.
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Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer
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Weekly sweep of ALL transcript-bearing spoken events (earnings calls, conference firesides, CMDs, investor days, AGMs, investor updates) for every active thesis, via the Quartr watchlist. Produces a triage digest in Daily Intel/ flagging thesis-touching mentions and recommending deep reads. Use when user says "event sweep", "what did my companies say this week", "conference sweep", or on the weekly schedule. Also `--setup` (build/rebuild the Quartr watchlist from active theses) and `[N]` (window override in days, default 8).
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Generate a backward-looking retrospective of vault activity (addressed callouts + Log entries) over a time window (1w / 1m / 1q), overlay with newsflow + earnings transcripts + price action, and rank trade ideas by the gap between official narrative and market reaction. Output is an immutable Research note. Use when user says "retro", "retrospective", "what did I do this week/month/quarter", "review activity against market", or "recommend trades from recent research".
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Use this skill when deriving EcoSIM plant trait parameter recommendations for a named plant from web and online literature evidence, using a read-only `plant_trait.*.desc` file and the `ndlf43` tree block or `gr3s43` grass block as the starting archetype. Never edit or replace the `.desc` file.
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Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.
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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
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Chinese-market SaaS pricing page template — soft sky-blue glass-morphism, 4-tier card grid with one elevated "recommended" tier, monthly/annual toggle, FAQ accordion, ¥ pricing with Inter numerals, careful CJK linebreaking, ICP filing footer.
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Use when a user asks for QA planning, execution, review, release assessment, reporting, status, or test-tool recommendations and the correct PWDEV QA workflow must be selected safely.
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Recommend context-appropriate QA tools while preserving observed availability, constraints, and safer alternatives.
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Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations. Use when asked to "review the architecture", "engineering review", or "lock in the plan". Proactively suggest when the user has a plan or design doc and is about to start coding — to catch architecture issues before implementation. (gstack) Voice triggers (speech-to-text aliases): "tech review", "technical review", "plan engineering review".
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