'harness' 검색 결과

Everything Claude Code의 스킬과 훅을 DeepSeek Harness에 연결합니다. 설정 생성, 동작 확인, 업데이트와 인식 문제 조사를 안내합니다.

• DSH에 ECC 스킬을 설치합니다
• 프로필에 훅을 연결합니다
• 스킬과 훅의 인식 문제를 조사합니다

Evaluate and improve the policy instructions that guide an AI agent using scored coding tasks. Run a learning cycle to compare candidates, or use the default dry run to inspect tasks and estimated costs without model calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving agent policies against a scored task set
• Estimating learning costs before running models
• Comparing candidates on held-out evaluation tasks

Assess a repository's readiness for an AI agent execution framework across five dimensions. Produce JSON or a table with readiness scores, estimated cost per run, and scaffold status, without changing repository files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing readiness before adopting an agent execution framework
• Comparing readiness scores across five dimensions
• Checking estimated run costs and scaffold readiness

Delegate scoped repository analysis or implementation to DeepSeek Harness without opening its interactive interface. Collect its final text, check the exit status, and inspect repository changes when the task requires read-only work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnose a failing test without editing files
• Delegate a scoped repository investigation
• Request implementation in a specified project

Turn a Bifrost PR or GitHub issue into API regression tests that catch recurring bugs. Review existing coverage, add cases to the Postman collection, and validate collection processing and provider filtering.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add regression coverage for a merged PR
• Turn an issue's reproduction steps into API tests
• Test streaming and non-streaming responses

Create a starter structure for a custom AI agent harness, the setup that supports agent execution. Choose a template and host, preview the plan, then explicitly confirm generation in a new directory outside the current project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a coding agent harness from a template
• Preparing starter setups for different agent hosts
• Previewing generated files' destination before writing

Create a fixed JSON evaluation suite from a repository's existing tests and check its structure for errors. Use the suite with harness-evolve to compare variants against consistent tasks, independently of the repository's normal test command.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a fixed evaluation suite from existing tests
• Check evaluation suite structure in CI
• Compare variants across commits using consistent tasks

Validate OpenHarness through real model API calls on an unfamiliar project. Tests cover conversations across multiple turns, actual tool execution, and combined features, with guidance for distinguishing setup problems from product failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate agent behavior with real model API calls
• Check context reuse across multiple conversation turns
• Test hooks, skills, and plugins together

Guides deployment of Open Harness on Vercel, from collecting credentials and setting environment variables to checking the first session. Separates a basic setup from a fuller setup with GitHub repository access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a personal copy of Open Harness
• Identifying credentials needed for a basic deployment
• Adding GitHub repository access

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

xp-harness の skill / agent を新規作成・改修するとき、または xp-harness そのものの設計判断 (取り込む / 取り込まない / 翻訳する) を考えるときに必ず発火させる skill。xp-harness の中核思想 (最終形の理想像 = エージェント群と利用者で XP の開発チームを成す、価値で導きつつ規律装置最小注入、エンジニアとして振る舞う、ペアプロ哲学、中央集権より decentralized、outside-in 例外なし、対話と自走の境界、INVEST の取捨、共創を目指す対話) を context に inject し、新規 skill / agent の設計判断や既存 skill / agent の改修判断を、xp-harness 思想と整合させるためのもの。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan and review new Omnigent harness integrations, the adapters connecting agent runtimes to the platform. Compare SDK and subprocess approaches with native interface wrappers, using capability matrices and checklists for implementation and testing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a new agent runtime integration
• Compare SDK and native integration requirements
• Review approval, streaming, and session support

Audit AGENTS.md, rules, skills, and their references for broken paths or commands. Optional independent agent reviews assess redundant instructions and practical usefulness, producing reports that identify what to retain, review, or consider trimming.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking paths and commands cited in agent instructions
• Finding redundant rules across skills and instructions
• Assessing which instruction files change agent behavior

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Scan declared MCP servers and tools for permission, policy, and dependency issues without executing them. Produce JSON or a Markdown table, and set a severity threshold that determines whether a CI check fails.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review declared MCP server and tool permissions
• Check policy and dependency issues in MCP configuration
• Fail CI when findings reach a chosen severity

Understand SenseNova Team Harness, a workspace you host yourself for collaboration between people and local AI agents. Learn its core concepts, startup requirements, and workflow for assigning tasks, sharing input files, and reviewing versioned deliverables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess whether SenseNova Team Harness fits a team
• Learn how shared spaces, tasks, and deliverables relate
• Get started with a self-hosted workspace

Clarify responsibilities in a TeamHarness team, including planning, task execution, result acceptance, and runtime administration. Use the role boundaries to identify who owns each action and how participants should handle sensitive information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assigning responsibilities across team roles
• Checking ownership of task review and acceptance
• Defining work boundaries for remote members

Organize TeamHarness project and task files in shared workspaces, synchronize changes, and submit deliverables. It also guides publishing files to Matrix chat rooms and explaining file access through other requester channels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organizing project and task deliverables
• Synchronizing shared workspace files
• Publishing an extra file to a Matrix room

The harnesses a campaign keeps, read outside a run: which there are and where each came from, what each is made of and what moved from its parent, what each was measured to be, trying one on a task, the audit before a promotion and the blind review at a handover, whether a part earns its place, and changing what makes a harness or what a seat is told about one. Use when the operator asks which harness is current, what a Builder changed, whether a harness is ready to promote or hand over, to run a kept harness on a task, or to change the harness contract. Reading one run is whole-run-investigation's, wording a claim attribution-and-proof's.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate a read-only security report on threats involving MCP connections and operations. It groups findings by threat, reports the highest severity, and supports table or JSON output with a configurable severity threshold for failure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare security findings before a release
• Share a categorized threat report with a security team
• Check changes in MCP-related threats during periodic audits

Inspect evolved prompt configurations, validate their structure, and view the system prompts they produce. You can also classify failures in run transcripts to support reviews before adopting or changing an agent policy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing the system prompt before adopting an evolved policy
• Validating prompt configuration changes in CI
• Classifying failures in an agent run transcript

Benchmark security detection systems against 10 known vulnerabilities and nine harmless decoys. Compare four approaches, including an evolved champion, and receive structured JSON covering detection, false alarms, patch tests, reproduction, unsafe outputs, and overall pass or fail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing security detection approaches
• Tracking changes in detection and false alarms
• Flagging failed checks in scheduled benchmarks

Experiment with the policies that govern an AI agent, such as planning, memory, and tool use. Test one policy change per variant in a sandbox, an isolated environment, and promote only candidates with measured improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding policy changes that improve low evaluation scores
• Exploring initial agent settings for a new domain
• Comparing manual tuning with an evolved baseline

Assess a repository without changing it and report its type, suggested agent roles, risk, MCP connections, test confidence, and publication readiness. Use the results for setup decisions, comparisons over time, or automated risk checks in CI.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a repository before creating a custom agent harness
• Comparing reports over time to spot architecture drift
• Failing a CI build when risk exceeds a chosen threshold