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概要と使いどころ
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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。
Install and onboard a user into LifeOS — the Life Operating System (current state → ideal state via TELOS + the Algorithm). The agentic installer detects your OS + harness, wires hooks with permission, scaffolds your USER tree, pulls in sources you provide, and runs the TELOS / current→ideal interview that seeds your Pulse dashboard. USE WHEN install LifeOS, set up LifeOS, lifeos setup, lifeos-setup, lifeos interview, onboard me, run the interview, integrate LifeOS into my harness, update LifeOS, uninstall LifeOS, first-time setup, lifeos doctor, check my install, what capabilities are broken. NOT FOR building or cutting a LifeOS release (private release tooling), editing TELOS after onboarding (use Telos / Interview), or LifeOS system maintenance (use the private maintenance skill).
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。
Control a real browser via CDP: clicking, typing, navigation, logged-in sessions, JS-rendered or bot-protected pages. Not for plain HTTP fetches of public content - use curl for those.
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。
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
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。
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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。
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs. new-session composer, terminal strip vs. agent terminal, all twelve native harnesses). Covers the web UI, native harness terminals, and the CLI.
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。
하네스 진화 스킬. 사용 중인 하네스의 실행 결과에 대한 피드백을 수집·일반화하여 에이전트/스킬/오케스트레이터에 반영하고, 초기 구성 대비 델타를 포착해 변경 이력을 갱신한다. '하네스 회고', '하네스 진화', '하네스 피드백 반영', '하네스 개선', '결과가 아쉬웠어 하네스 고쳐줘', '이 피드백 하네스에 반영해줘', '하네스 레슨 정리' 등 기존 하네스의 실행 경험을 바탕으로 한 개선 요청 시 반드시 이 스킬을 사용. 하네스 신규 구축·구조 재설계·에이전트 추가는 harness 스킬이 담당.
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。
Verify harness changes end-to-end without docker — drive the real pinned CLI against a header-capturing stub server with the exact env resolve_auth_env() produces.
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。
Sets up and runs libFuzzer, the coverage-guided fuzzer built into LLVM, on C/C++ code that compiles with Clang. Covers harness structure, -fsanitize=fuzzer builds, corpus and dictionary management, sanitizer integration, and campaign triage. Use when writing or debugging an LLVMFuzzerTestOneInput harness, starting fuzzing on a C/C++ library, choosing between libFuzzer and AFL++, or working out why a libFuzzer run finds nothing.
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。
Let the OpenCode host delegate code review, development, and research tasks to DeepSeek Harness.
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。
Guides a user through collecting the credentials needed to deploy their own copy of Open Harness, deploying this repo on Vercel, and completing first-run setup. Use for requests about deploying, self-hosting, configuring credentials, or getting started with a fork of this app.
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。
Use when a user asks what SenseNova Team Harness is, wants an introduction or overview, is getting started, or is deciding whether it fits — a self-hosted workspace where people and local AI agents collaborate on conversations, projects, work items, and artifacts. Covers its core concepts (Workspace, Project, Conversation, Agent, WorkItem, Resource, Artifact, Local Computer), main capabilities, how to run it, and the basic usage flow.
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。
Use to understand TeamHarness team roles, responsibility boundaries, and who owns project, task, runtime, and control-plane work.
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。
Use for TeamHarness room classification and Matrix task-room setup: roomflow describe_room, roomflow create_task_room, source/requester room vs task room detection, project-scoped task-room reuse, and invite handling. Do not use for projectflow, taskflow, or message delivery.
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。
Use for TeamHarness message delivery details after a flow has decided a message must be sent: current-session reply, NO_REPLY, cross-room/cross-channel message tool payloads, PROJECT_REQUESTED task-room handoff, and requester replyRoute reports. Do not use to choose work mode, create rooms, delegate, check, or accept tasks.
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。
Use for TeamHarness shared workspace paths, filesync, artifact publication, and Matrix room file boundaries, including artifact publish_file and task/project deliverables. Do not use for message routing or project state changes.
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。
Use when you act as Leader and maintain durable TeamHarness project state for Quick Task or Project Work: create_quick_project, create_project, plan_dag, plan_loop, ready_nodes, resolve_project, accept_task_result, project completion, and requester report state. Do not use to create Matrix task rooms or send messages.
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。
Evolve this harness with Darwin Mode — frozen model, evolving harness (real, sandboxed, safety-gated).
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。
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
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。
Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.
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。
Lead workflow for developing vgpu features and Blender assets with the repository's subharness specialists. Use for agent-based research, API design, implementation, shaders, materials, simulations, or ImageGen-guided asset iteration, and when the human mentions the agent flow or subharness team.
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。
Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.
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。
CLI harness for Zotero.
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。
通过 COM 自动化操控 Adobe Photoshop 的 CLI harness。支持项目管理、图层操作、选区控制、文字编辑、图像调整和多格式导出。
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。