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Host-side DOCA UROM library (doca_urom_*): offload an HPC / UCX / MPI application's remote memory operations (puts, gets, atomics, collectives) from the host CPU onto a BlueField DPU. Trigger on "MPI all-reduce burning host CPU", "push UCX traffic onto the BlueField", "first doca_urom call returns NOT_PERMITTED", or "host library and DPU service look out of sync", even without naming DOCA UROM. An offload needs both halves: this host library and the UROM Service on the DPU (doca-urom-svc), which owns deploying that container. Covers creating UROM Service contexts (doca_urom_service_*) and Worker contexts (doca_urom_worker_*) that run plugins on the DPU, discovering plugins via doca_urom_service_get_plugins_list, progressing completions, and DOCA_ERROR_* from doca_urom_* calls. Route elsewhere for UROM Service deployment on the DPU side, MPI / UCX collective algorithm design, and RDMA / RoCE / IB substrate bring-up.

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Use this skill when the user is writing their first DOCA app or asking a library-agnostic programming question — picking a shipped sample to copy and modify, wiring pkg-config + meson (or FFI from Rust / Go / Python against the public C ABI), walking the Core lifecycle (create → configure → start → connect → use → stop → destroy), or decoding a DOCA_ERROR_*. Trigger on implicit phrasing: "write my first DOCA program", "meson line for doca_rdma_*", "DOCA_ERROR_BAD_STATE on first call", "call DOCA from Rust", "built clean but nothing on the wire". Refuse install / hugepages / pkg-config (doca-setup), docs or version lookup (doca-public-knowledge-map), and library-internal API work (matching library skill).

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RDMA data movement on NVIDIA BlueField/ConnectX NICs (RoCE/InfiniBand) with DOCA RDMA, instead of hand-written libibverbs/rdma_cm. Use when the user moves data between processes or hosts over the NIC, or hits "one-sided read returns permission denied", "completions never arrive after submit", "connection callback never fires", atomic compare-and-swap over RoCE, or DOCA_ERROR_FULL on the send queue under burst, even without naming DOCA RDMA. Covers bringing up an RDMA context on a doca_dev, connection methods (RDMA CM, bridge/OOB, or gRPC exchange of doca_rdma_export()), the eleven task types (Send/Receive/Send-Imm, Read/Write/Write-Imm, Atomic CmpSwap/FetchAdd, Get/Set/Add Remote Sync Event), matching mmap + RDMA permissions, queue and connection sizing, doca_rdma_cap_* queries, and DOCA_ERROR_* from RDMA calls. Raw QP/verbs control belongs to doca-verbs. Refuse and route elsewhere for general RDMA / ibverbs theory (queue pairs, MRs, RoCE vs IB), installing DOCA itself, or non-RDMA DOCA libraries.

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Use this skill when the user wants to invoke the read-only doca_caps CLI to ask what DOCA sees on this host — listing DOCA devices and PCIe addresses, listing representor devices, asking which DOCA libraries are available on the current OS, checking per-device per-library capabilities, scoping output to a specific PCIe address, or capturing a side-effect-free capability snapshot for debug or install smoke-test. Trigger even when the user does not explicitly mention "doca_caps" or "capabilities print tool" — typical implicit phrasings include "what does DOCA actually see on this box", "is my BlueField PF visible to DOCA", "is Flow available on my RHEL host", "enumerate VF representors for pf0", "doca_caps: command not found", or "empty output for RDMA, is the tool broken". Refuse and route elsewhere for DOCA installation (doca-setup owns it; never walk the steps), library-internal capability matrices (Flow pipe creation, RDMA verbs features), streaming telemetry / DTS, or modifying the shipped binary.

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Use this skill when the user is dealing with the DOCA environment around their workload — verifying an install is healthy, preparing the build env (pkg-config, headers, LD_LIBRARY_PATH, hugepages, devlink, representors), debugging env-class failures, deciding container-vs-bare-metal deployment shape, or reaching a DOCA install from a host that doesn't have one yet via the NGC DOCA container Stage-1 fallback. Trigger even when the user does not explicitly mention "DOCA setup" — typical implicit phrasings include "just got a BlueField, what now", "code is built, how to run it", "pkg-config can't find doca-flow", "no free 2048 kB hugepages", "representor X not found", "on a Mac and want to learn DOCA". Refuse and route elsewhere for library API specifics (Flow pipes, RDMA queues), the modify-a-sample first-app workflow or DOCA_ERROR_* program-side debugging, and "where is X documented" knowledge-map questions — those belong to other skills.

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Use this skill when the user is tuning a live or captured `doca-flow` pipeline with `doca_flow_tune` — snapshotting pipe / counter / KPI state, picking a tuning axis (rule placement, resource hints / table sizing, HW-offload mode) and a matching measurement (rule-install rate, lookup latency, hardware-counter delta), running offline or online (read-only or state-changing) modes, reading the dumper CSV / analyze JSON / visualize mermaid, or applying a recommendation back into the Flow program. Trigger even when the user does not explicitly mention "doca_flow_tune" — typical implicit phrasings include "Flow rule-install rate is low on BlueField", "table sizing looks wrong for this pipe", "tune visualize step is empty", "before/after counters don't move", or "which doca-flow knob does this recommendation hit". Refuse and route elsewhere for measuring baseline numbers (doca-flow-perf, doca-flow-dpa-perf), writing the doca-flow application, DOCA install, or streaming Flow telemetry — those belong to other skills.

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Map the load-bearing files, symbols, callers, and dependencies of an unfamiliar codebase before making changes. Use for architecture orientation, impact analysis, what-would-break questions, or finding callers. Uses CodeGraph when available and a dependency-free builtin fallback.

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Install, configure, and start FireRed-OpenStoryline from source on a local machine. Use when a user asks to set up OpenStoryline, troubleshoot installation, download required resources, fill config.toml API keys, or launch the MCP and web services, as well as Chinese requests like “安装 OpenStoryline”, “配置 OpenStoryline”, “启动 OpenStoryline”, “把 OpenStoryline 跑起来”, “修复 OpenStoryline 安装问题”, or “排查 OpenStoryline 启动失败”.

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[omh] Spending money on the user's behalf -- a card checkout, an HTTP 402 or pay-per-request call, a wallet transfer, a booking, a paid data endpoint: show a pre-spend card (payee, total with fees, cap, refund terms, approver) first, and call it done only on an observed receipt. Use when the user says: spend-gate, spend gate, pre-spend, pre-spend card, spend cap, spending cap, spend limit, spending limit.

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[omh] OMH install misbehaving: diagnosing oh-my-hermes installation health. Use when the user says: doctor, diagnose omh, installation health.

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Use the ClawHub CLI to search, install, update, and publish agent skills from clawhub.com. Use when you need to fetch new skills on the fly, sync installed skills to latest or a specific version, or publish new/updated skill folders with the npm-installed clawhub CLI.

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Drafting campaigns and ads as staged drafts, from acquisition and seasonal pushes to lifecycle sends such as win-backs, pre-arrival messages, and presale or last-call notices, choosing audiences and placements by intent, allocating budget across campaigns, and reviewing campaign results with their attribution caveats. Not needed for listing copy (catalog-listings) or overall performance questions (performance-insights).

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Azure Communication Services CallingServer (legacy) Java SDK. Note - This SDK is deprecated. Use azure-communication-callautomation instead for new projects. Only use this skill when maintaining legacy code.

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SUPERSEDED by /claudex-loop (formerly /crucible; its docs-aware mode covers this variant) — prefer that skill unless you explicitly want this one. Two-act plan hardening with living documentation. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan, one question at a time, challenging it against your project's existing domain model and glossary (CONTEXT.md), sharpening fuzzy terms, stress-testing with concrete scenarios, cross-referencing code, and updating CONTEXT.md + ADRs inline as decisions crystallise. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md and OpenAI Codex adversarially reviews it in a read-only sandbox (VERDICT:APPROVED/REVISE), Claude revises and re-submits to the SAME Codex session until APPROVED or a MAX_ROUNDS cap, then you sign off before any code. Use when the user says "/grill-with-docs-codex", "grill me against the docs then have codex review", "stress-test this against our domain model then get a second model on it", or is about to build something high-stakes in a project with established terminology/ADRs and wants alignment, documentation, AND a cross-model sanity check. Builds on Matt Pocock's grill-with-docs (MIT). NOT for reviewing already-written code (use /codex:review) and NOT for trivial changes.

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Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.

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Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

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Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.

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Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling.

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Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Guppy is deprecated; use Dorado for all new analyses.

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Use when the user asks to "allocate my influencer budget", "optimize spend across tiers", or "compare budget scenarios"; produces a tier/platform/content allocation table, ROI and CPM/CPE projections, scenario comparisons, and mid-campaign reallocation moves. Not for building the full campaign plan — use campaign-planner. 达人预算分配/投放预算优化

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Use when the user asks to "personalize the email", "add merge tags / dynamic content", "set up conditional blocks per segment", or "make first-name and product-recommendation fields fall back safely"; produces a merge-tag map with per-tag fallbacks, conditional-block rules with per-segment variations, a fallback-safety audit, and a PII guard on what may render, informing the SEND E (Engagement/personalization) dimension. Not for building the segments — use list-segment-builder; not for writing the base copy — use email-creative-builder; not for scoring EQS or running vetoes — use email-quality-auditor. 邮件个性化/合并标签/条件内容块/兜底默认值

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Use when preparing your agent for production — IAM scoping, inbound auth (JWT, SigV4), secrets management, cold start optimization, session lifecycle, rate limiting, input validation, and quota guidance. Triggers on: "production checklist", "harden agent", "production ready", "secure agent", "inbound auth", "going live", "cold start optimization", "session lifecycle", "StopRuntimeSession", "quota", "throttling", "maxVms", "rate limit", "security audit of outbound API calls", "gateway target audit for production", "restrict who can call", "lock down endpoint", "only our app can call". Not for Cedar tool-restriction policies — use agents-connect. Not for quality measurement — use agents-optimize. Not for outbound credential storage or API key wiring — use agents-connect. Not for A2A agent-to-agent auth — use agents-build. Cold start observation and diagnosis (not optimization) routes to agents-debug.

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Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Also has an access-only mode for checking or enabling Bedrock model access for specific models (e.g. 'request access to Claude on Bedrock', 'which models do I turn on') with no code migration — it checks each model and walks the right access step, then stops. Not for agent runtime selection or agent migration planning (use agent-advisor), nor standalone cost estimates or infra-only migration. The full migration REQUIRES gcp-to-aws OR azure-to-aws installed alongside this skill (gcp-to-aws preferred when both are present); access-only mode needs neither.

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Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.

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