Borrowing it
Nothing to install: this file belongs to DavidLam-oss/obsidian-wechat-converter. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DavidLam-oss/obsidian-wechat-converter/main/.claude/skills/openprd-benchmark-router/SKILL.mdgit clone --depth 1 https://github.com/DavidLam-oss/obsidian-wechat-converterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-benchmark-router)<a href="https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-benchmark-router"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-benchmark-router/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/davidlam-oss/obsidian-wechat-converter/openprd-benchmark-router"><img src="https://agentmods.dev/badge/skills/davidlam-oss/obsidian-wechat-converter/openprd-benchmark-router.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.03338 |
| Opus 5 | $0.00023 | $0.01669 |
| Sonnet 5 | $0.00009 | $0.00668 |
| Haiku 4.5 | $0.00005 | $0.00334 |
Grade A, and why
openprd-benchmark-router scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenPrd Benchmark Router
当用户要求最佳实践、benchmark、对标、参考设计、产品优化、CLI 优化、Agent harness 优化、AI code review / PR review harness、上下文工程、提示词工程或图标资源时，先使用这份 skill。
核心原则
- 不把对标当成固定关键词匹配。结合用户目标判断参考源是否真的能提升设计、实现、排查、评审、规划或文档质量。
- 不强行对标。环境、权限、账号、普通脚本报错、一次性短问答或与产品/领域设计无关的问题，继续当前任务即可。
- 不默认下载全文、仓库或整站。先保留轻量链接、来源 ID 和适用边界；真正需要事实时再读取。
- 通常只选 1-3 个最相关来源；不要为了显得全面而扩大上下文。
- 不把索引、来源目录或记忆当事实来源；未经核验的外部内容不能作为已确认事实输出。
触发信号
- 用户提到 OpenPrd、OpenSpec、Superpowers、Anthropic Skills、Lark CLI、Agent harness、AI code review、PR review、review lane、long-running agents、context engineering、prompt engineering、最佳实践、对标、参考、复刻或优化设计。
- 用户提到图标、icon、图标站、图标库、图标资源、UI 图标、AI 图标、技术图标、3D 图标、功能图标、iconfont 或视觉资产参考。
- 用户提到界面审美、设计框架、主题库、模板库、组件骨架、视觉资产库、前端体验风格或页面参考方法。
- 用户要求解释某个 Codex / Claude / Cursor agent 为什么没有发现 skill，或希望提升 skill 自动识别、路由、生成、安装和持续执行能力。
- 用户没有显式说 skill 名也要触发；不要要求用户记住
$openprd-benchmark-router。
路由流程
- 先识别优化对象：OpenPrd 产品/PRD 流程、CLI、skill 体系、长程任务、通用 harness、AI code review / PR review harness、context engineering、prompt engineering、图标资源或图标实现库。
- 读取当前工作区证据：
.openprd/、.openprd/benchmarks/index.md、.openprd/benchmarks/sources.yaml、AGENTS.md、repo-local skills、生成的.codex/.claude/.cursor引导和相关源码。 - 选择最小足够的外部证据源：公开 GitHub 仓库走 DeepWiki；第三方工具、SDK、CLI 或官方 API 用 Context7；产品官方文档、工程博客和一手资料用官方来源。
- 形成 OpenPrd 设计判断时，明确区分已证实事实、从来源归纳出的设计原则，以及对本项目的推断。
- 用分析维度提炼可迁移原则，避免照搬表面功能。
- 如果任务变成大量参考项目行为挖掘、长时间覆盖或需求补全，再路由到
$openprd-discovery-loop承接持续调研。
Project Registry
- 项目自己的
.openprd/benchmarks/优先于 OpenPrd 内置 Source Map。 sources.yaml里的 approved source 是长期可复用参考；inbox/里的 candidate 只表示待确认线索。- 用
openprd benchmark add <url|repo|file>写入 candidate，用openprd benchmark approve <id>纳入 approved registry。 - 执行或复盘中发现被用户采纳的优质信源时，用
openprd benchmark observe <url|repo|file> --notes <text>累计 evidence；达到阈值后只推荐 approve，不自动晋级。 - 用
openprd benchmark verify检查重复来源、失效链接、缺失本地文件和过宽触发规则。
Source Policy
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 126 lines · 45 tokens per session scan A 4dbab0750ad6
openprd-benchmark-router is a skill published in the GitHub repository DavidLam-oss/obsidian-wechat-converter (307 stars, last pushed yesterday), licensed MIT. It adds 45 tokens to every session and 3,338 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…