「candidates」の検索結果

Use the open-source free `QuickDup` clone detector for .NET repositories. Use when a repo needs duplicate C# code discovery, structural clone detection, DRY refactoring candidates, or repeatable duplication. USE FOR: the repo wants QuickDup; the team needs repeatable duplicate-code scans for C#; the user asks about DRY cleanup, copy-paste detection, clone detection, or duplicate. DO NOT USE FOR: formatting-only work; repos that intentionally use a different clone detector and do not want overlap. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Pseudo-mutation analysis ONLY: answer whether tests would catch a bug if production code changed, which meaningful changes would still pass, or which caller-visible mutations existing assertions would miss; verify candidates when requested, then optionally close verified gaps. Includes explicit read-only per-test composition for grading. Activate for behavioral blind spots tied to production behavior. Polyglot. DO NOT USE FOR: suite taxonomy, metadata, or distribution reports (test-tagging); .NET line-vs-branch or Cobertura interpretation, arithmetic, plateaus, project-wide coverage gaps, or coverage-backed test/CRAP priorities (coverage-analysis; use native coverage tooling outside .NET); named-target CRAP (crap-score); new suites (code-testing); assertion/smell audits; or mutation tools.

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Fetch and rank open GitHub issues by community engagement, present the top 3 candidates, and plan implementation for the selected issue. Use when the user asks to "pick next issue", "next issue", "which issue should I work on", "top issues", "most popular issues", "prioritize issues", or "what should I work on next".

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Assess project-wide structural technical debt: complexity hotspots, deprecated API usage, duplication clusters, architecture rot, and low-value tests. Ranks findings by impact and refactor effort into a report at .turbo/technical-debt.md. Use when the user asks to "assess technical debt", "find technical debt", "review technical debt", "what should we refactor", "find refactoring candidates", "where is the code rot", "what's our worst code", "find low-value tests", or "which tests can we delete". Analysis-only — does not modify code.

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Analyze an existing PostgreSQL database to identify tables that would benefit from conversion to TimescaleDB hypertables

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Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published GWAS. Query studies, associations, variants, traits, genes, summary stats. Build PRS candidates, analyze pleiotropy, fetch stats for Manhattan plots. No auth.

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Find roll options for existing short positions OR find best covered call/put to open against long stock. Use when user asks about rolling shorts, finding roll candidates, writing covered calls, or managing option positions. Requires TWS or IB Gateway running locally.

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Scan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, yield, trend direction, and earnings proximity. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies.

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Analyze PMCC (Poor Man's Covered Call / diagonal spread) positions from IB portfolio. For each diagonal spread, reports short leg risk (delta, IV, assignment probability), daily P&L projections, top-3 roll candidates, and a side-by-side comparison table. Requires TWS or IB Gateway running locally.

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Find and compare current FF&E candidates from a design brief, with sourced purchasing facts and optional CSV-library save. Use to research or source products; not to match from an image or pair coordinating items.

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When the user needs to create or improve content that shapes how candidates and the public perceive the company as a place to work.

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When the user needs to write recruiting outreach messages to attract passive candidates or request referrals.

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When the user needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.

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Generate diverse solution candidates with category-spanning ideation methods and rotation. Use when stuck on a design or fighting repetitive LLM output.

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Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates. Use when triaging a roadmap or prioritizing features for a sprint.

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Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.

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Write job posts that make the right person apply -- real role clarity, honest selling points, salary range handling, and requirements pruned to what actually predicts success. Small businesses lose great candidates to bad posts long before the interview. Feeds cowork-hiring-screener and hiring-scorecard downstream.

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Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.

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Search and inspect the installed Blueish Blender Asset Library without mutating it. Use when a user asks for a local Blender asset, node preset, Blueish asset, Asset Browser entry, or named source, or when the Router has selected a node-centric/reusable Geometry Nodes, material, simulation, rigging, compositor, or NPR route with eligible local catalogs; return compatible, attributable candidates for the Router-approved workflow.

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Generate, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture, shop, studio, or organization; derive a name from an existing project or concept; create a company and product naming system; compare candidates; or check company, domain, package, repository, marketplace, or trademark conflicts. Default to a fast no-browse creative pass; use current validation when the user requests availability or research, or is choosing a final name. Do not claim legal clearance, purchase domains, or present stale or unverified availability as fact.

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Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

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Testing workflow skill for finding high-value test candidates, writing focused tests, generating realistic workloads, reviewing test value, and diagnosing test-suite health. Use for prompts about what to test next, adding or improving tests, reviewing whether a test is worth keeping, low-signal or flaky tests, workload scenarios, or test-suite trust. Do not use for general QA discussion unless the user wants a concrete testing workflow or artifact.

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Source technical and professional talent by combining LinkedIn profiles, GitHub contributions, and company intelligence. Identifies candidates with specific skills, assesses expertise depth, and provides outreach context for recruiting teams.

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。