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Agent Skill · 研究检索

dkh(DKH 搜索)

dkh 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

dkh 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为操作浏览器;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 43,703。

总安装 964周安装 41GitHub Stars 1下载 338核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:dkh(DKH 搜索)
来源仓库:https://github.com/dkod-io/harness
仓库路径:skills/dkh
安装命令:
npx skills add https://github.com/dkod-io/harness --skill dkh
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/dkod-io/harness --skill dkh

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

dkh 是一个自主并行构建系统,能自动完成从规划到部署的全流程,无需人工干预。适合在 Codex、Claude、Cursor、Gemini CLI 中用于复杂任务的端到端执行,如 Web 应用构建。使用时需输入清晰目标描述,系统将自主拆分任务、并行实现并修复问题。

安装前建议确认运行环境权限和网络访问能力,因其涉及 AST 级合并与多实例并发。dkh 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/dkh

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/dkod-io/harness --skill dkh;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

dkh 怎么安装?

在终端执行 npx skills add https://github.com/dkod-io/harness --skill dkh。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

dkh 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

dkh 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

dkh 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

dkh 用的人多吗?

公开数据是累计安装 964 次,近一周安装 41 次,下载 338 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

dkh 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
dkh

DKH 搜索 · 本页

9641操作浏览器未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

dkod Harness — Autonomous Parallel Build System

What This Is

A fully autonomous build harness. The user provides a single prompt ("build a webapp that..."). The harness does everything else — planning, parallel implementation, testing, fixing, and shipping — without any further user interaction.

This is an implementation of Anthropic's Planner → Generator → Evaluator harness pattern, purpose-built for dkod's parallel execution capabilities. Where Anthropic's reference architecture runs generators sequentially, this harness runs N generators simultaneously because dkod's AST-level merge eliminates false conflicts.

When This Skill Activates

  • User says "build a...", "create a...", "make a..."
  • User invokes /dkh <prompt>
  • User describes a complete application or feature set to build from scratch
  • Any task complex enough to benefit from parallel decomposition + evaluation

Handling /dkh continue

When the user sends /dkh continue (or just "continue"):

═══ MANDATORY: RECOVER STATE AND CLEAN UP BEFORE RESUMING ═══

Before re-dispatching ANY generators, recover the state from the interrupted session and clean up only what's incomplete. Do NOT bulk-close everything — submitted changesets represent completed work that must be preserved.

Step 1: Query dkod for existing changesets Call dk_status or list changesets via the API to see what the interrupted session left behind. Categorize each changeset:

  • submitted state → KEEP. This generator finished its work. Record its changeset_id. Do NOT close it. Do NOT re-dispatch this unit.
  • approved state → KEEP. This changeset passed review and is ready to merge. Record its changeset_id. Proceed to merge in Phase 3.
  • draft state → INCOMPLETE. This generator was interrupted before dk_submit. Mark this unit for re-dispatch.
  • conflicted state → STUCK. Mark this unit for re-dispatch.
  • rejected state → FAILED. Mark this unit for re-dispatch.

Step 2: Close incomplete/failed changesets and release their symbol claims

# Close draft/conflicted/rejected — preserve submitted and approved
Bash: curl -sf -X POST "https://api.dkod.io/api/repos/<owner>/<repo>/changesets/bulk-close" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DKOD_API_KEY" \
  -d '{"states": ["draft", "conflicted", "rejected"], "created_before": "'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"}'  \
  || { echo "Bulk-close failed — aborting resume. Check DKOD_API_KEY and repo path."; exit 1; }

Step 3: Reconstruct harness state

  • changeset_ids = list of submitted + approved changeset_ids from Step 1
  • active_units = units whose generators were incomplete (draft/conflicted/rejected/missing)
  • Units with submitted or approved changesets are DONE — skip them

Step 4: Resume with only the incomplete units Re-dispatch only the generators for active_units (incomplete ones). The submitted changesets from completed generators are preserved — 20 minutes of work is not lost.

Output: "Resuming harness — N/M generators completed before interruption. Re-dispatching K incomplete units."

Then proceed:

  1. If an active harness session exists (the agent has state from a prior turn):

- Output the current harness state and phase - Show which agents completed (submitted) vs need re-dispatch - Resume the harness loop, skipping completed units

  1. If no active harness session exists (fresh context after app restart):

- Acknowledge the command: "No active harness session found in this context." - Check for any active dkod sessions via dk_status - If active sessions found, recover state as described above - If no sessions found, tell the user to start a new build with /dkh <prompt>

Never ignore a /dkh continue silently. Always acknowledge with current status.

Prerequisites Check

Before starting, verify these are available:

  1. dkod MCP tools: dk_connect, dk_context, dk_file_write, dk_submit, dk_verify, dk_review, dk_approve, dk_merge, dk_push, dk_status, dk_watch
  2. Browser testing (pick one — Playwright preferred):

- playwright-cli (preferred): Check with playwright-cli --version. Standalone CLI for skills-less browser automation — screenshots, script execution, PDF generation. No Node.js scripts needed, no MCP server, runs headless by default. See: https://github.com/microsoft/playwright-cli - chrome-devtools MCP (fallback): navigate_page, take_screenshot, click, evaluate_script, list_console_messages, lighthouse_audit. Used only if Playwright is not installed. - If not found, output install instructions and proceed with fallback. Do NOT ask the user or install. - If NEITHER Playwright nor chrome-devtools is available, evaluation falls to dk_verify + code review (no live UI testing).

  1. Design system (pick one — DESIGN.md preferred):

- DESIGN.md (preferred): A design system file in the project root, sourced from awesome-design-md. If present, it becomes the authoritative design reference — the planner incorporates it into the spec, generators follow it directly (no skill invocation needed), and the evaluator scores against it. This produces more distinctive, brand-aligned UI than the generic skill. - frontend-design skill (fallback): If no DESIGN.md exists, generators invoke Skill(skill: "frontend-design") before implementing UI components. The planner still generates a Design Direction section in the spec. The evaluator still scores design quality. If not found, output install instructions and proceed with fallback. Do NOT ask the user or install. - If NEITHER is available, generators follow the planner's Design Direction section manually.

Detection flow (run once during PRE-FLIGHT):

# 1. Detect playwright-cli (standalone CLI, skills-less operation)
HAS_PLAYWRIGHT=false
playwright-cli --version 2>/dev/null && HAS_PLAYWRIGHT=true

# 2. Detect DESIGN.md (check all paths the planner searches)
HAS_DESIGN_MD=false
( [ -f DESIGN.md ] || [ -f design.md ] || [ -f docs/DESIGN.md ] || [ -f docs/design.md ] ) && HAS_DESIGN_MD=true

# Pass both flags to all agent dispatches:
# - Planner: HAS_DESIGN_MD
# - Generators: HAS_DESIGN_MD
# - Evaluators: HAS_PLAYWRIGHT
# - Smoke test: HAS_PLAYWRIGHT

If dkod is missing, guide installation:

claude mcp add --transport http dkod https://api.dkod.io/mcp

Model Profiles

Active profile: quality

Each agent runs on a model appropriate to its task. The orchestrator reads the active profile and passes model: AND effort: on every Agent dispatch call.

Agentqualitybalancedbudgeteffort
Orchestrator*opusopussonnethigh
Planneropusopussonnetmax
Generatoropussonnetsonnethigh
Evaluatoropussonnethaikumax
  • The orchestrator model is set by the invoking Claude Code session, not by this table. This row is a recommendation for the session model, not enforced by the harness.

Effort levels are mandatory. Planner and Evaluator use max (complex reasoning — decomposition, scoring). Generator uses high (fast execution — file writes, not deep analysis). Always pass effort: when dispatching agents.

  • quality — All Opus. Maximum capability. Use for complex or high-stakes builds.
  • balanced (default) — Opus for planning and orchestration, Sonnet for implementation and evaluation. Best cost/quality trade-off.
  • budget — Sonnet for planning and implementation, Haiku for evaluation. Fastest and cheapest. Use for simple builds or iteration.

To switch profiles, change Active profile: above. The orchestrator reads this value at the start of each run.

The Autonomous Loop

The harness runs a strict phase-gated loop: PLAN → BUILD → LAND → File Sync → Smoke Test → EVAL → SHIP/FIX/REPLAN. Each phase has entry/exit gates that block progression until artifacts exist.

agents/orchestrator.md is the single source of truth for all phase details, gate checks, state tracking, dispatch templates, and transition logic. Read it before starting.

Key constraints:

  • No dk_push(mode:"pr") without completed eval reports (Phase 5 only)
  • No evaluators without landed + smoke-tested code
  • No generators without a validated plan
  • dk_verify is NOT evaluation — Phase 4 tests the live app via chrome-devtools
  • All code changes go through dkod — never use Write/Edit/Bash on source files
  • Never ask the user anything — every decision is autonomous
  • Max 3 eval rounds, then ship with documented issues

Critical Design Principles

0. Maximize parallelism — THE PRIME DIRECTIVE

Default to parallel execution; only serialize when there is a hard data dependency. Use Claude Code agent teams (multiple Agent calls in one message) + dkod session isolation (each agent gets its own overlay). Together they turn serial builds into parallel builds.

Applies to every phase: generators dispatch simultaneously, dk_verify runs in parallel, failed generators re-dispatch in parallel. The ONE exception: evaluators run sequentially (shared chrome-devtools browser session).

1. Decompose by symbol, not file

dkod merges at the AST level. Two generators editing different functions in the same file is not a conflict.

2. One dkod session per generator

Each generator calls dk_connect once. Sessions are isolated until merge.

3. The Evaluator is standalone and skeptical

From Anthropic's research: "tuning a standalone evaluator to be skeptical turns out to be far more tractable than making a generator critical of its own work." Default to FAIL unless proven PASS with evidence.

4. File-based communication

The plan and eval reports are structured artifacts that survive context resets.

5. Autonomy is non-negotiable

The harness NEVER asks the user for input. Conflicts → auto-resolve. Eval failures → auto-fix. Framework → infer from prompt. Package manager → bun.

6. Max 3 eval rounds

Prevents infinite loops. After 3 rounds, ship whatever works and document what doesn't.

Work Unit Schema

The Planner produces work units in this structure (embedded in the plan artifact):

## Work Unit: <id>
**Title:** <descriptive title>
**OWNS (exclusive):** <list of qualified symbol names this unit solely owns>
**Creates:** <list of new symbols with file paths>
**Acceptance criteria:**
- <testable criterion 1>
- <testable criterion 2>
**Complexity:** low | medium | high

Agent Definitions

  • Planner: agents/planner.md — expands prompt into spec + parallel work units
  • Generator: agents/generator.md — implements a single work unit via dkod session
  • Evaluator: agents/evaluator.md — tests merged result via chrome-devtools + dk_verify
  • Orchestrator: agents/orchestrator.md — drives the autonomous loop (this is you)

Reference Guides

  • references/planning-guide.md — deep guide for symbol-level decomposition
  • references/evaluation-guide.md — skeptical evaluation techniques and chrome-devtools patterns
  • references/dkod-patterns.md — dkod session lifecycle and merge patterns

平台分布

Codex

35.63%
按下载量换算120

Claude

28.74%
按下载量换算97

Cursor

18.19%
按下载量换算61

Gemini CLI

10.13%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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