「harness」の検索結果

Experimental NotebookLM harness for listing notebooks, managing sources, asking questions, generating artifacts, and downloading outputs through an installed notebooklm 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。

Command-line interface for CloudAnalyzer — Agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.

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。

Complete CLI harness for FreeCAD parametric 3D CAD modeler (258 commands). Covers ALL workbenches: Part (29 primitives + boolean + mirror + loft + sweep), Sketcher (26 cmds: geometry + constraints + editing), PartDesign (38 cmds: pad/pocket/groove/fillet/chamfer/patterns/hole/datum), Assembly (11 cmds), Mesh (16 cmds), TechDraw (15 cmds: views + dimensions + PDF/SVG), Draft (33 cmds: 2D shapes + arrays + transforms), FEM (12 cmds), CAM/CNC (10 cmds), Surface (6 cmds), Spreadsheet (7 cmds), Import (13 formats), Export (17 formats), Measure (12 cmds), Materials (21 presets). Headless FreeCAD export to STEP/IGES/STL/OBJ/DXF/PDF/glTF/3MF.

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。

Windows-first CLI harness for Nsight Graphics capture, GPU Trace summary, and ngfx-replay analysis

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。

Wrapper for the Dify workflow DSL CLI. Create, inspect, validate, edit, and export Dify workflow files through a CLI-Anything 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。

Command-line interface for CloudCompare — Agent-friendly harness for CloudCompare, the open-source 3D point cloud and mesh processing software. Supports 41 commands across 9 groups: project management, session control, point cloud operations (subsample, filter, segment, analyze), mesh operations, distance computation (C2C, C2M), transformations (ICP, matrix), export (LAS/LAZ/PLY/PCD/OBJ/STL/E57), and interactive REPL.

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 Krita digital painting — manage projects, layers, filters, and export via command line. Use when automating Krita workflows, batch processing images, or operating Krita without a GUI.

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 the Mailchimp Marketing API v3.0 — 303 commands across 30 resource groups (lists, campaigns, reports, automations, ecommerce, templates, and more). Supports JSON output and interactive REPL mode.

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。

Command-line interface for Calibre - A stateful CLI harness for e-book library management, metadata editing, and format conversion wrapping the real Calibre tools (calibredb, ebook-convert, ebook-meta)...

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 NSLogger — parse, filter, export, and monitor NSLogger log files (.rawnsloggerdata / .nsloggerdata)

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 with and operates Pi, the minimal terminal coding harness. Use for installing Pi, configuring providers/models/settings/environment variables, creating Pi skills/extensions/packages/themes/prompt templates, embedding Pi through the SDK, integrating over RPC or JSON event streams, parsing sessions, running local models through the llama.cpp router, developing custom Pi providers and TUI components, or using ecosystem packages such as pi-subagents (delegation/orchestration), pi-mcp-adapter (MCP servers), pi-interview (interactive forms), and pi-web-access (web search, fetching, video understanding).

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。

Applies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs. Includes a standard-library state manager and guidance for the RUC-NLPIR Arbor 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。

Use when an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for custom binary or text protocol recovery, handshake reconstruction, framing, sequence control, checksums, stateful replay, and accepted-session reproduction. Use when the user asks to decode an unknown protocol, recover custom framing, build a replay harness, satisfy sequence or checksum rules, replay a captured session, or prove the smallest message order that reaches an accepted branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Lay the keel of the shipyard harness in any repo — the 4-pillar shared environment (Context, Rules, Tools, Standards), seeded as two faces on day one (CLAUDE.md + GLOSSARY.md) with every other surface appearing on first use, so that every human and agent inherits the same design language and anyone can ship. Run once per repo; re-run with --check to audit drift.

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。

Requirements for the SGLang scripted runtime, chiefly when to add (vs not add) a harness API. Use for anything related to the scripted runtime.

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 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.

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。

Author an eval for an existing skill from its REAL usage history, not its spec. Mine invocations from the brain's conversation archive (conversations/) and per-harness session transcripts — a user correction after an invocation is the gold signal — then synthesize an eval_contract plus 4-8 replayable cases with honesty labels (SPEC-DERIVED vs HISTORY-IMPLIED) and stage the result at skills/<name>/eval/autobench-<date>.md as PENDING-HUMAN-APPROVAL. Never rewrites SKILL.md. Ships two guard companions: panel integrity (multi-model judging must prove each provider actually responded) and the fail-improve taxonomy (logged LLM-fallback cases convert to deterministic code over time).

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。

Publish the user's own gbrain over MCP so other devices, desktop apps and cloud agents can reach it. `gbrain mcp expose` installs and signs in Tailscale, publishes the running `gbrain serve --http` on the tailnet (HTTPS, tailnet-only by default; Funnel only when a client lives in a vendor cloud), keeps the server alive as a user service, and hands back the MCP URL. Then register one least-privilege client per consumer, choose native OAuth/PKCE or a private machine handoff for that client, and verify the connection inside the intended 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。

Detect which gbrain engine is in use (PGLite vs Postgres), prefer Postgres for agent-harness installs, install/provision Postgres (Supabase discovery via SUPABASE_ACCESS_TOKEN, local Postgres, opt-in Docker), and move an existing PGLite brain to Postgres with its history (plan, ask, run). Detection is one engine-free command; the install ladder is one flag.

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。

Token-hygiene audit of the always-loaded context stack — CLAUDE.md, AGENTS.md, auto-memory MEMORY.md, and the bootstrap-rendered identity files (SOUL.md, USER.md, ACCESS_POLICY.md, HEARTBEAT.md) or their harness equivalents. Finds redundancy, contradictions, stale content, compression candidates, and skill-extraction candidates; produces a ranked action list sorted by token savings with a risk class per finding. REPORT-ONLY: this skill never edits any audited file. Recommendations for bootstrap-rendered files target the interview answer bank / templates, never the rendered output. Judging routes through `gbrain eval cross-modal` (single cheap model by default; full multi-model panel is explicit opt-in).

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。

Open the GBrain owner dashboard, manage MCP clients and permissions, or connect a native OAuth harness. Distinguishes owner administration from ordinary MCP access.

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 to run a sandboxed deep verification of a qwen-code PR — "/verify-pr <n>", "深度验证这个 PR", A/B load-bearing proof against the base build, mock-free harnesses with wire oracles, and targeted gates — producing tmp/pr<n>-verify-<ts>/report.md plus a machine-readable verdict. Designed for the token-free CI verify job; also usable locally.

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。