「harness」の検索結果

Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is unreliable across sessions — forgets context, drifts out of scope, claims "done" before tests pass, or starts each session inconsistently — or when creating or assessing AGENTS.md, CLAUDE.md, feature_list.json, init.sh, progress.md, or session-handoff files. Reach for it even if the user never says the word "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。

This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.

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This skill should be used when the user asks to "test the harness", "run integration tests", "validate features with real API", "test with real model calls", "run agent loop tests", "verify end-to-end", or needs to verify OpenHarness features on a real codebase with actual LLM calls.

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Reference guide for building new Omnigent harness integrations — covers SDK/subprocess harnesses and native harnesses as separate tracks, each with their own feature matrix, implementation patterns, and prioritized checklist.

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[omh] Lost track of agent sessions and worktrees: normalize Codex, Claude Code, Hermes, OpenCode, Cursor, MCP host, worktree, and wrapper session metadata into one drift-aware inventory. Use when the user says: harness-session-inventory, harness session inventory, session inventory, session adapter, session adapters, harness sessions, mcp inventory, mcp config inventory.

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Review XQUIC harness structure for bloat, duplicated facts, trigger overlap, reference drift, and missing hard checks. Use when changing AGENTS.md, harness specs, pipelines, skills, setup scripts, validation routing, or when asked to audit harness design.

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Master skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".

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5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.

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Scaffold a ready-made AI agent harness in one command from the 19 published @metaharness/* example packages — 9 host integrations (Claude Code, Codex, Hermes, pi.dev, OpenClaw, RVM, Copilot, OpenCode, GitHub Actions) + 10 vertical pods (devops, research, trading, support, legal, coding, education, sales, gaming, repo-maintainer).

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Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable `npm install @metaharness/kernel@X.Y.Z` next step. Exits 2 if no .harness/manifest.json at path.

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Run the weak-agent adversarial test harness against docx-cli. Spawns weak exercise agents (Haiku by default, Sonnet to probe, or a local agent harness's pre-produced runs) to perform real document tasks over six scenarios — five editing (MNDA form-fill + font fidelity, invoice table-edit/restructure + logo replace, résumé styling, contract redlining + commenting, contract finalize via accept/reject + comment reply/resolve) and one authoring (T. S. Eliot poetry journal: multi-column, verse, footnotes, links, figure) — renders every result with Word, has opus judge them against ground-truth rubrics, measures each exercise's tool economy, token cost, wall-clock, and correctness (from transcripts for Claude, the exercise.json ledger for the local harness), and synthesizes a prioritized ergonomics report. Use when the user says 'adversarial review', 'test docx-cli with weak agents', 'run the haiku harness', 'weak agent test', or wants to re-run yesterday's adversarial process.

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。

Greenfield project architecture + harness scaffolding for AI Agent productivity. Interview-driven decisions → requirements.md → execute. Produces: Code Structure (vertical slice exemplar), Test Infrastructure, Guard Rails, conditional extensions, AND Harness (CLAUDE.md with domain/team context, rules, skills, hooks). L2: architecture decisions, L3: harness setup, L4: requirements + harness decisions (tasks generated later by /execute into plan.json). Use when: "/scaffold", "scaffold", "new project", "set up project", "프로젝트 세팅", "초기 구조"

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Build, revise, audit, or rebut a research manuscript from Harness Research evidence. Use for paper intake, related-work research, claim and citation control, section architecture, LaTeX patching, figure planning, reviewer response, or final manuscript hardening when the workspace contains Harness research artifacts.

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Design eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".

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Initializes and configures Harness in a project — CI, memory, duo workflow, Codex. Use when setting up a new project or adding Harness configuration.

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。

Master skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".

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 analyzing harness trace logs to surface recurring failure patterns and suggest harness improvements. Scatter-gather pattern across last N runs with mandatory HITL gate before any change is applied. Triggers on /mk:trace-analyze, "analyze traces", "find patterns in harness runs", or after a model upgrade flagged dead-weight-audit-needed.

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。

Design, implement, and integrate generalized validation harnesses across a producer-consumer boundary after a local harness contract exists. Use when refactoring custom validation CLIs/MCPs for large polyrepos, extending Steward across sibling repos, or deploying a local tool to a consumer project for dogfooding and testing; use mcp-harness-repo-maintainer first for initial steward.yaml adoption and cold-start proof.

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xp-harness の skill / subagent / instruction が意図どおり振る舞えているか (発火するか・あるべき振る舞いができているか) を transcript で事実確認する手順。skill / subagent / instruction を改修した後に動作検証したいとき、または本番の実運用セッションを「あるべき振る舞い」に照らして分析したいとき、「検証したい」「発火するか確かめたい」「このセッションを分析したい」と言われたときに発火させる。核は発火・振る舞いができたかの事実確認で、本番セッションでは自走の良し悪し (止まり方・判断の質など) も見る。

Orchestrator for agent harness work — the setup that makes AI agents follow project rules and improve when they fail. FIRES PROACTIVELY when agents misbehave, repeat mistakes, ignore instructions, skip skills, or when AGENTS.md exists but docs/harness/manifest.json is missing. Also triggers on: harness engineering, agent scaffold, agent keeps failing, agent not following instructions, make agents reliable, agents going off rails, agent forgot context, improve agent setup, self-improving agents, agents keep making mistakes, why is my agent bad, agent quality, agent setup broken, agents ignore skills, same mistake again, fix agent behavior, tune agent instructions, set up agent infrastructure, after project setup agents still bad. Routes bootstrap vs evolution. Not multi-agent topology — agent-builder.

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Install and operate Everything Claude Code (ECC) on the DeepSeek Harness (DSH): native skill roots (~/.dsh/skills, .agents/skills), the @deepseek-ai/dsh-hooks-claude-code bridge for command hooks, bare-insert patch mounting, generator usage, event-support limits, and update workflow. Use when setting up ECC on DeepSeek Harness, wiring ECC hooks into a DSH profile, or debugging why ECC skills or hooks do not appear in a DSH session.

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アプリ開発を計画・実装・評価のエージェントに分け、動作中の画面を検証しながら、設定した品質基準に向けて修正を繰り返す開発支援スキル。

• 短い案からアプリ仕様を作りたいとき
• 別担当の評価で画面を改善したいとき
• フルスタックアプリの動作検証と修正

繰り返すエージェント作業に専用の実行手順を設計し、合否チェックや進捗記録、引き継ぎを整えるスキル。複数の場面で使う手順を共有スキルにまとめます。

• 反復作業の実行手順を設計したいとき
• 作業の合否チェックを追加したいとき
• 繰り返す手順を共有スキルにしたいとき

Scaffold a custom AI agent harness via `metaharness new {name} --template {id} --host {id}`. Defaults to DRY-RUN (no writes) unless --confirm is passed. Refuses to write to the calling repo root or anywhere inside it. Honors ADR-150 architectural constraint + ruflo's "destructive-action confirmation" pattern.

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。