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harness-creator

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."

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=walkinglabs-learn-harness-engineering-skills-harness-creator-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name harness-creator description 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." license MIT Harness Creator Use this skill to make a repository easier for coding agents to start, stay in scope, verify work, and resume across sessions. Keep the harness small enough that agents actually follow it. Not for model selection, prompt tuning in isolation, chat UI design, or general app architecture. Core Model Every useful coding-agent harness has five subsystems: Subsystem Minimal artifact Purpose Instructions AGENTS.md or CLAUDE.md Startup path, working rules, definition of done State feature_list.json , progress.md Current feature, status, evidence, next step Verification init.sh or documented commands Tests/checks the agent must run before claiming done Scope Feature dependencies and done criteria Prevents overreach and half-finished work Lifecycle session-handoff.md , end-of-session routine Makes the next session restartable First Move Inspect what already exists: instruction files, feature/state files, verification commands, docs, package manifests. Ask only for missing context that cannot be inferred safely: target agent, desired file name, tolerance for structure, and whether overwriting is allowed. Prefer a minimal harness first. Add memory, tool safety, multi-agent, or benchmark details only when the user's problem calls for them. Common Tasks Create a harness Use the bundled script when working on a local repository: node skills/harness-creator/scripts/create-harness.mjs --target /path/to/project Options: --agent-file CLAUDE.md for Claude-oriented projects. --package-manager npm|pnpm|yarn|bun when detection is wrong. --commands "cmd one,cmd two" for custom verification. --force only after confirming overwrites are acceptable. Then explain what was created and how the user should replace placeholder feature entries. Audit an existing harness Run: node skills/harness-creator/scripts/validate-harness.mjs --target /path/to/project Report the five subsystem scores, the lowest-scoring area, and the first 2-3 changes that would improve reliability. Treat the lowest score as a candidate bottleneck; confirm with failures, logs, or task outcomes before claiming causality. Produce a report Use when the user wants a shareable assessment: node skills/harness-creator/scripts/render-assessment-html.mjs --target /path/to/project node skills/harness-creator/scripts/run-benchmark.mjs --target /path/to/project --html /path/to/report.html Be clear that this is a structural benchmark. The benchmark first runs a self-check — it scaffolds a throwaway harness and validates it, proving the bundled scripts work end-to-end — then scores the target and eval coverage. Real effectiveness still needs before/after agent sessions on representative tasks. When to Read References Load only the reference needed for the user's problem: Memory across sessions: Memory Persistence Reusable workflows as skills: Skill Runtime Permissions, tools, concurrency: Tool Registry & Safety Context budget and progressive disclosure: Context Engineering Delegation and parallel agents: Multi-Agent Coordination Hooks, startup, long-running work: Lifecycle & Bootstrap Non-obvious failure modes: Gotchas Design Rules Keep the root instruction file short: routing and invariants, not a full manual. Put project facts in project docs, not in the skill. Make verification commands explicit and runnable. Require evidence before marking a feature done. Use one active feature unless the harness has explicit multi-agent ownership boundaries. Prefer append/update state files over relying on chat history. Never hide destructive behavior in scripts; overwrites require explicit user approval. Deliverable Checklist For a usable minimal harness, leave the target project with: AGENTS.md or CLAUDE.md feature_list.json progress.md init.sh Optional session-handoff.md for multi-session work Documented verification evidence or next action If you cannot create files, provide exact file contents and commands instead.
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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