drpy-node-coder

A complete workflow for creating, repairing, testing, evaluating, and publishing drpy-node scraper sources. drpy-node is a project for extracting content from websites.

In plain words
What is it for?
Use it to analyze a website or existing source, run syntax and data tests, evaluate playback chains, repair problems, and prepare a verified upload through its bundled command-line tool.
Why use it?
It gives agents a defined path from diagnosing a source to validating it, so they do not rewrite code before understanding the failure.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/hjdhnx/drpy-node-skill/drpy-node-coder
Any agent
npx skills add hjdhnx/drpy-node-skill --skill drpy-node-coder
Clone the repo
git clone --depth 1 https://github.com/hjdhnx/drpy-node-skill

Made for: Claude Code, Codex.

Per session 168 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,672 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00168 $0.03672
Opus 5 $0.00084 $0.01836
Sonnet 5 $0.00034 $0.00734
Haiku 4.5 $0.00017 $0.00367

Measured 2d ago against content hash 10504eb127c2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

drpy-node-coder scanned grade A with 1 finding 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 2d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (scripts/cli.js, scripts/commands/fs.js, scripts/commands/house.js, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

| 多引擎源(php/hipy/cat) | 路线 E | `references-multi-engine-sources.md` | `fetch` + curl `?do=py/php/cat&extend=`(**必须带 extend**;hipy 改源需 kill daemon)|
drpy-node-coder/SKILL.md · 160 lines

How it starts

The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.

drpy-node Coder

drpy-node 爬虫源的建、修、测、放、传全链路一体化 skill。自带 CLI 工具,无需安装 MCP 服务

便携性说明(先读)

  • 无需 MCP:所有能力内置在 scripts/cli.js,AI 直接 node scripts/cli.js <命令> 调用。
  • 零 npm 依赖:无需 npm installlocalDsCore 测试引擎 bundle(14M,全内联 cheerio/axios/drpyS/htmlParser)+ sqlite + 编码 wasm 自带于 scripts/vendor/
  • ⚠️ 前置:本机需有 drpy-node 项目(CLI 复用其 req/pdfa/drpyS/DS解密 源码模块——这些互相 import、非 bundle,搬不动;但 localDsCore 已内联进 vendor/test/evaluate 不再依赖 drpy-node-bundle 目录)。
  • 首次使用:cd scripts && node cli.js setup <drpy-node-绝对路径> && node cli.js doctor
  • 调用范式:node scripts/cli.js [--root <drpy-node路径>] <命令> [参数] [--flags]。所有命令输出 JSON:成功 {"ok":true,"data":...}、失败 {"ok":false,"error":...}
  • 输出读取:少数命令(test/evaluate)首次加载测试引擎时 stdout 可能有一次初始化日志,业务 JSON 始终是 stdout 最后一行,按最后一行 { 解析。

总控工作流(5 步闭环)

用户输入
  │
Step1 识别输入 ──仅 URL──→ 分析站点 → 建源路线
  │(已有源名/文件)
Step2 评估现状:syntax+validate(L1) → test 单接口(L2) → evaluate 全流程(L3)
  │
Step3 判断失败类型:A规则不通 / B评估串联 / C播放链
  │  🛑 检查点1:确认诊断结论
Step4 分流执行:本 skill 修 / 转播放调试 / 转仓库
  │
Step5 收束:evaluate 复评 → 🛑 检查点2 → 上传建议 / 结束

核心原则:先评估 → 再分流 → 再修复 → 再验证 → 最后给上传建议。不要一上来就重写。

模式闸门:先判断是否允许写入

用户模式 允许 禁止
只读/dry-run/只规划 读取、诊断、给方案和验证计划 fs write/edithouse upload/*
需确认后改 读取、诊断、输出拟改字段+验证计划 未确认前改源或仓库元数据
明确执行 按 L1/L2/L3 证据最小修复 跳过大改确认点、直接仓库 mutation
自主全流程("修到100""自动完成""做源并上传") alive check→建源→低风险修复→播放→L3=100→上传 坏站硬写、未达目标冒充完成、目标不明上传

任务分派表(场景 → 路线 + 首读 reference + 关键命令)

场景 路线 首读 reference 关键 CLI 命令
仅 URL,新建源 建源 A/B/C/D references-create-checklist.mdreferences-template-system.md fetch guess analyze template fs write syntax validate test evaluate
已有源评估低分 诊断 A/B/C references-workflow-triage.mdreferences-framework-internals.md fs read resolved evaluate test
detail 通但 play 异常 播放专项 references-play-lazy-summary.md test <src> detail test <src> play iframe fetch + Playwright(见下)
上传/替换/改标签 仓库守门 references-upload-decision.md house verify house upload house list house info house tags
模板继承排查 路线 A references-template-summary.mdreferences-inherited-template-minimal-override-site.md guess resolved
纯 API/SPA 站 路线 C references-pure-api-async-site.mdreferences-api-functions.md fetch analyze
签名接口站 路线 B2 references-non-template-signed-api-site.md fetch + 浏览器抓包
二级字典/多集 detail 规范 references-detail-dict-and-multiep.md debug test <src> detail
搜索异常 搜索策略 references-search-strategies.mdreferences-old-encoding-search-site.md test <src> search
async 函数陷阱 通用 references-async-function-patterns.md
特殊内容(漫画/小说/音乐/网盘) 路线 D references-special-content.md
多引擎源(php/hipy/cat) 路线 E references-multi-engine-sources.md fetch + curl ?do=py/php/cat&extend=必须带 extend;hipy 改源需 kill daemon)

Read the full file on GitHub · 160 lines

Files

What ships with it

46 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 2d ago First seen · 160 lines · 168 tokens per session scan A 10504eb127c2

Subscribe to this mod's changes

drpy-node-coder is a skill published in the GitHub repository hjdhnx/drpy-node-skill (48 stars, last pushed 1mo ago), licensed MIT. It adds 168 tokens to every session and 3,672 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

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…

vercel/next.js · 170 tokens