Results for “matching”

Writes and manages professional bios for any platform and purpose, matching your voice and career stage, and keeps them current as your work evolves.

Modern Java 21+ language standards including Records, Pattern Matching, and Virtual Threads. Use for Java language constructs or Java-to-Java upgrades; defer Kotlin, tests, formatting/build tools, CompletableFuture, builder patterns, and isolated null-handling questions.

Access prediction market data from Polymarket and Kalshi, including markets, prices, trades, orderbooks, positions, and cross-platform market matching. Use when you need current odds, historical market data, or wallet-level prediction market analysis.

Provides 45 best-practice rules across 8 categories for mocking APIs with MSW v2, covering setup, handler architecture, test integration, response patterns, request matching, GraphQL, advanced patterns, and debugging.

Adds a new API connector or provider to a repository by matching its existing integration pattern exactly, including layout, config, auth, error handling, tests, and wiring.

Rapidly classify malware samples against known family signatures using YARA rules, covering rule writing, scanning, and integration with analysis pipelines.

Routes Grafana alerting webhook payloads to Slack, PagerDuty, and OpsGenie channels based on label matching rules, with support for alert grouping and silence management via the Grafana Alerting API.

Discovers and researches companies matching an ideal customer profile, using Browserbase Search API and a Plan→Research→Synthesize pattern to produce scored reports and CSV exports.

Develop precise YARA rules for malware detection by identifying unique byte patterns, strings, and behavioral indicators in executable files while minimizing false positives.

Automates the full lifecycle of agent skills: extracts reusable constraints from user feedback, merges preferences with version bumps, and retrieves relevant skills for new tasks.

Automates job search and applications via the JobGPT MCP server, including job matching, auto-apply, tailored resume generation, application tracking, salary insights, and recruiter outreach.

Automates ingestion, normalization, deduplication, and distribution of threat intelligence feeds from STIX/TAXII, open-source, and commercial sources into SIEM platforms for real-time IOC matching and alerting.

Shares memory between agents at the representation level by compacting the orchestrator's KV cache for efficient worker handoff, reducing token costs without summarization or retrieval.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns. Use PROACTIVELY for Java development, microservices architecture, or performance optimization.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

Searches X (Twitter) for tweets matching an advanced query and returns structured data including text, author profile, engagement metrics, media, hashtags, mentions, and pagination cursors.

Monitor paste sites like Pastebin and GitHub Gists for leaked credentials, API keys, and sensitive data using automated scraping and keyword matching to detect breaches early.

Search SAM.gov for live notices matching a pursuit row (agency, incumbent, NAICS keywords) and save results to sam_scan.md. Use for admin monitor setup, presolicitation watch, or when user asks what is live on SAM for an expiring contract.

RF Toolbox and RF Blockset in MATLAB -- S-parameter I/O, network conversions (S/Z/Y/ABCD/T/H/G, mixed-mode), cascade/de-embedding, rfbudget analysis, circuit composition, matching networks, amplifier stability, mixer spurs, rational fitting, SI channels, baseband processing, Circuit Envelope simulation. Trigger: sparameters, Touchstone, .s2p, .s4p, rfplot, smithplot, rfparam, rfwrite, zparameters, yparameters, abcdparameters, s2sdd, cascadesparams, deembedsparams, rfbudget, noise figure, OIP3, IIP3, amplifier, modulator, nport, rffilter, attenuator, seriesRLC, shuntRLC, lcladder, txline, circuit, setports, clone, matchingnetwork, stabilityk, stabilitymu, powergain, gammams, gammaml, mixerIMT, OpenIF, rational, rationalfit, stepresp, txlineWRLGC, rf.Amplifier, rf.Mixer, rf.Filter, rf.Sparameter, rfsystem, RF Blockset.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

Foundational Scala patterns covering immutability, pattern matching, traits, case classes, for-comprehensions, and functional programming. Use when writing Scala code, understanding the type system, or needing guidance on which specialized Scala skill to use. This is the entry point for Scala development.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

Flag misspelled, brandjacked, and typosquatted package names across npm, PyPI, and crates.io before installation using edit-distance, keyboard-proximity, and known-target corpus matching with typomania, OSSGadget, and pypi-scan.

Foundational Erlang patterns covering OTP behaviors, fault-tolerant systems, distributed computing, pattern matching, processes, and supervision trees. Use when writing Erlang code, building concurrent systems, working with OTP frameworks, or developing distributed fault-tolerant applications. This is the entry point for Erlang development.

Finds all places matching a query within a radius using grid-based Google Places scanning, with options to filter by rating, reviews, and open status, and export results to JSON, CSV, or an interactive map.

Dart 3.x language feature standards: null safety, records, sealed classes, switch pattern matching, extensions, and async/await. Use when using !, ?., ??, late, sealed classes, record types, switch expressions, or async patterns — and before introducing any new Dart 3.x construct to confirm the modern idiomatic approach.

Foundational Elixir patterns covering modules, pattern matching, processes, OTP behaviors (GenServer, Supervisor), Phoenix framework basics, and functional programming idioms. Use when writing Elixir code, building concurrent systems, working with Phoenix, or needing guidance on Elixir development patterns. This is the entry point for Elixir development.

Foundational Cypher (Neo4j) patterns covering graph pattern matching, MATCH/CREATE/MERGE/DELETE operations, relationships, path patterns, aggregation, filtering, and common graph query patterns. Use when writing Cypher queries, modeling graph data, or needing guidance on graph database operations. This is the entry point for Cypher development.

Detect Cobalt Strike beacon network activity using TLS certificate signatures, JA3/JA3S/JARM fingerprints, HTTP C2 profile matching, beacon jitter analysis, and named pipe detection via Zeek, Suricata, and Python PCAP analysis.

File a new GitHub issue in the checked-out repo — search first for duplicates/adjacent issues to update or reference, detect the repo's issue template if any, then compose title/body/labels/milestone matching house style. Use when asked to file, create, open, report, or track an issue.