'graph algorithms' 검색 결과

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Think and work like an expert Biological Oceanographer. Use when a task calls for Biological Oceanographer judgment. Reasons from light-nutrient-grazing coupling, the microbial loop, and size-structured export through CTD/MOCNESS sampling, 14C and O2/Ar production with 234Th export flux, imaging and flow cytometry enumeration, and SILVA/PR2 metabarcoding while treating spatial patchiness, diel-migration tow aliasing, CDOM-biased chlorophyll algorithms, and eDNA-detection-as-abundance as first-class failure modes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reference patterns for graph algorithm problems including BFS, DFS, Dijkstra, topological sort, union-find, MST, Bellman-Ford, and bipartite checking. Provides recognition signals, Python templates, edge cases, and common mistakes for each technique. Use when solving problems involving graphs, trees, shortest paths, connectivity, or dependency ordering.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identifies cryptographic algorithms embedded in a binary by scanning for well-known constants and tables (AES S-box, SHA-256/MD5 init constants, ChaCha sigma, CRC32 table, base64 alphabet). Activates for requests to identify crypto in a binary, find AES/SHA constants, or detect which encryption algorithm a sample uses.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert en algorithmes (Big-O, dynamic programming, graphs, sorting, LeetCode patterns)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan encryption, TLS connections, digital signatures, and key management using established cryptographic libraries. Get guidance on choosing algorithms, storing passwords, rotating keys, and avoiding implementation mistakes such as reused nonces or comparisons that reveal information through timing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing encryption methods for stored data
• Reviewing TLS settings for encrypted connections
• Designing password storage and token signatures

Domain routing and boundary guidance for authorized cryptography weakness testing, including weak algorithms, padding oracles, key management errors, insecure randomness, and hash collision risks. Use when a task belongs to the cryptography testing domain and needs scope, evidence, pivot, or exit criteria.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API. Use when creating, pausing, resuming, resizing, or deleting AuraDB Free/Professional/Business Critical/VDC instances; downloading credentials; scripting CI/CD pipelines; polling async status; or using the Terraform neo4j-labs/neo4jaura provider. Covers auth setup (client credentials OAuth2), credential lifecycle (download once — never recoverable), instance type selection, region codes, and Python provisioning scripts. Does NOT handle Cypher queries — use neo4j-cypher-skill. Does NOT cover Graph Data Science algorithms — use neo4j-gds-skill or neo4j-aura-graph-analytics-skill. Does NOT cover neo4j-admin/cypher-shell — use neo4j-cli-tools-skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Algorithms for decomposing DAGs into chains for parallel execution and resource optimization

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze undirected cycle structure within a valid DAG using the source paper's orientation and contraction method. Use for cycle-basis analysis with explicit graph and reduction choices. NOT for directed feedback loops, workflow deadlocks, or runtime resilience claims.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create Mermaid diagrams from text to explain software structure, API interactions, workflows, and database relationships. The skill guides diagram selection, syntax, styling, and export, helping keep visual documentation editable and stored alongside code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining system architecture with diagrams
• Mapping API requests and responses
• Documenting database table relationships

Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams (domain modeling, object-oriented design), sequence diagrams (application flows, API interactions, code execution), flowcharts (processes, algorithms, user journeys), entity relationship diagrams (database schemas), C4 architecture diagrams (system context, containers, components), state diagrams, git graphs, pie charts, gantt charts, or any other diagram type. Triggers include requests to "diagram", "visualize", "model", "map out", "show the flow", or when explaining system architecture, database design, code structure, or user/application flows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when you need to apply Java secure coding best practices — including validating untrusted inputs, defending against injection attacks with parameterized queries, minimizing attack surface via least privilege, applying strong cryptographic algorithms, handling exceptions securely without exposing sensitive data, managing secrets at runtime, avoiding unsafe deserialization, and encoding output to prevent XSS. This should trigger for requests such as Review Java code for secure coding; Find input validation risks in Java code; Review Java code for injection vulnerabilities; Improve secure error handling in Java services; Harden Java code against common security flaws. Part of Plinth Toolkit

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnose, fix, and improve IEEEtran LaTeX manuscripts using routed template, layout, float, equation, algorithm, table, bibliography, and PDF-compliance guidance. Use when fixing IEEE LaTeX compile errors, overfull boxes, float placement, two-column layout, figure sizing, tables, equations, algorithms, BibTeX, citation commands, template compliance, or final submission PDF checks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill applies the analytical frameworks of Ronald C. Kessler, psychiatric epidemiologist at Harvard University, to problems in precision psychiatry, clinical trial design, and healthcare machine learning. Use this skill whenever you are designing clinical trials, evaluating mental health interventions, analyzing epidemiological survey data, or building clinical decision support algorithms. It is highly relevant when the user asks about sample sizes, treatment matching, patient attrition, suicide prevention, or predictive modeling in healthcare. Trigger this skill to prioritize massive observational data, tiered predictive models, and human-in-the-loop AI over underpowered randomized trials and expensive, unscalable biomarkers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, creative concept diversity for Meta Advantage+ (why six variants of one ad are not a diverse library, and what to brief instead), creator-made ad creative on a spend-share deal, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in combinatorics, graph theory, discrete probability, and algorithms with applications to computer science

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams (domain modeling, object-oriented design), sequence diagrams (application flows, API interactions, code execution), flowcharts (processes, algorithms, user journeys), entity relationship diagrams (database schemas), C4 architecture diagrams (system context, containers, components), state diagrams, git graphs, pie charts, gantt charts, or any other diagram type. Triggers include requests to "diagram", "visualize", "model", "map out", "show the flow", or when explaining system architecture, database design, code structure, or user/application flows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when you need to apply Java secure coding best practices — including validating untrusted inputs, defending against injection attacks with parameterized queries, minimizing attack surface via least privilege, applying strong cryptographic algorithms, handling exceptions securely without exposing sensitive data, managing secrets at runtime, avoiding unsafe deserialization, and encoding output to prevent XSS. This should trigger for requests such as Review Java code for secure coding. Part of cursor-rules-java project

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validate finite-DAG order objectives and construct reachability-chain or direct-edge path partitions with explicit proof boundaries. NOT for equating reachability chains with direct-edge paths without checking the chosen objective.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Selects crypto algorithms and usage. Hashing, AES-GCM, RSA, ECDSA, PQC key derivation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.