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Found 1,681 Skills
Subsurface well data analysis toolkit for loading, processing, and analyzing well logs, projects, and formation tops. Built on lasio with enhanced curve processing. Use when Claude needs to: (1) Load wells from LAS files with metadata, (2) Work with multi-well Projects, (3) Process curves (despike, smooth, resample, normalize), (4) Manage formation tops, (5) Export well data to DataFrame/LAS/CSV, (6) Perform cross-well analysis and QC.
Spatial data gridding and interpolation with a machine-learning style API. Process geographic and Cartesian point data onto regular grids. Use when Claude needs to: (1) Grid scattered spatial data onto regular grids, (2) Interpolate point data using splines, linear, or cubic methods, (3) Process geographic coordinates with projections, (4) Reduce large datasets using block averaging, (5) Remove polynomial trends from spatial data, (6) Cross-validate gridding parameters, (7) Create processing pipelines with Chain, (8) Grid vector data like GPS velocities.
Spatial data processing for geological modelling with GemPy. Use when Claude needs to: (1) Prepare spatial data for GemPy models, (2) Extract interface points from geological maps, (3) Process orientations/dip measurements, (4) Sample DEMs along profiles or cross-sections, (5) Convert between GIS formats and GemPy inputs, (6) Clip/transform vector/raster data for modeling, (7) Create model extents from geospatial bounds.
Every Granola feature — plus offline SQLite cross-meeting search, attendee timelines, and a MEMO pipeline runner... Trigger phrases: `memo run for today's meetings`, `what's in granola but not yet memo'd`, `every meeting we had with trevin`, `did i run the discovery recipe`, `talk time in last week's meetings`, `calendar overlay missed meetings`, `find duplicates in meeting transcripts`, `extract granola meeting`, `use granola`, `run granola`.
Generate a full multi-channel product campaign — hero visuals, social media assets, short ad video, and platform-specific crops for an end-to-end launch campaign.
Develop plugins for IDA Pro in Python, using idiomatic patterns, lessons, and tricks, including the Python Domain API (ida-domain). Use when creating both GUI (Qt) and background plugins for inspecting and rendering things program structure, functions, disassembly, cross-references, and strings.
Guides technical program management for security coordinated vulnerability disclosure (CVD)— disclosure policy, intake and triage SLAs, researcher coordination, fix/remediation tracking, embargo and publication timelines, CVE/advisory coordination, bug bounty program operations, and cross-functional gates (security engineering, legal, comms, product). Use when running a CVD or responsible disclosure program, disclosure calendar, bounty ops, or unblocking multi-team remediation for reported vulnerabilities—not for hands-on pentest (offensive-security-analyst), SOC triage (defensive-security-analyst), vuln scanning in CI (devsecops), enterprise security strategy (cybersecurity), generic non-security programs (technical-program-manager), or contract redlines (commercial-counsel).
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Use when the user has a long-form video (interview / lecture / podcast / conversation) and a transcript SRT, and wants to extract 3–6 stand-alone topical short clips from it. This skill ONLY cuts and crops — it produces raw clips + per-clip SRTs as a hand-off package for downstream post-production (`/wjs-overlaying-video`). Triggers — "切成几段", "分主题", "拆成短视频", "切片", "topic segments", "split into clips".
Multi-model deep review of the Ralph bd graph and plan via three parallel opencode processes (claude opus, gemini, gpt). Use for high-stakes runs where cross-model consensus reduces single-model bias.
QLC+ (Q Light Controller Plus) lighting software — workspace files (.qxw), scenes, chasers, sequences, collections, EFX, RGB matrices, fixture definitions (.qxf), Virtual Console, and timing calculations. Use this skill whenever the user is working with QLC+ workspace XML, creating/editing scenes/chasers/shows, debugging timing or crossfade issues, generating fixture definitions, setting up Virtual Console widgets (cue lists, solo frames, buttons, sliders), troubleshooting HTP/LTP conflicts, fixing corrupted .qxw files, configuring QLC+ plugins (DMX USB, Art-Net, MIDI, E1.31), or asking any question where QLC+ is the software being used. Also trigger for SpeedModes, FadeIn/Hold/Duration, FixtureVal, RunOrder, or QLC+ function types. Do NOT trigger for general DMX hardware questions without QLC+ context, fixture buying advice, DAW-only questions, or custom protocol implementations.
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.