菜单
Fetch URL or PDF as clean Markdown for reading, citation, or archive (读). Modes convert (default), summarize, save, preview. Routes CN platforms, GitHub, PDF, proxy cascade, MCP fallback. Use for 读链接, 抓取正文, 提取文章; not article drafts, topic learning workspace, or codebase audit.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | article-read |
| description | Fetch URL or PDF as clean Markdown for reading, citation, or archive (读). Modes convert (default), summarize, save, preview. Routes CN platforms, GitHub, PDF, proxy cascade, MCP fallback. Use for 读链接, 抓取正文, 提取文章; not article drafts, topic learning workspace, or codebase audit. |
Role: 读 — URL / PDF → Markdown for reading, citation, or archive. Scripts under scripts/; policy under references/.
references/cascade.md.| Mode | User signals | Read |
|---|---|---|
| convert (default) | 读一下, 抓取, fetch URL | references/workflow.md |
| summarize | 摘要, 总结 + URL | references/summarize.md |
| save | 保存, save to file | references/output.md § save |
| preview | 预览, 太长 | references/output.md § length |
references/workflow.md (classify input).references/router.md → domain refs if needed.READ_ROOT="<path-to>/article-read"
bash "${READ_ROOT}/scripts/read.sh" "https://…"
references/output.md.references/vet.md + references/mcp-fallback.md.| Script | Purpose |
|---|---|
scripts/read.sh | Main fetch |
scripts/save.sh | stdin → ~/Downloads/{slug}.md |
scripts/pdf-extract.sh | Local PDF text |
scripts/feishu-fetch.py | Feishu Open API (needs creds) |
scripts/github-blob-to-raw.py | GitHub blob → raw URL |
scripts/lib/common.sh | Shared (sourced by read.sh) |
Deps: scripts/requirements.txt (requests for Feishu).
After editing scripts:
bash -n article-read/scripts/read.sh
bash -n article-read/scripts/save.sh
bash -n article-read/scripts/pdf-extract.sh
bash -n article-read/scripts/lib/common.sh
Smoke: bash article-read/scripts/read.sh "https://example.com" (expect fetch attempt or documented fallback).
| Use | Use instead |
|---|---|
| Single URL/PDF → MD | Multi-query writing research → article-write gather |
Citation / topic-learn sources/ | Systematic topic mastery workspace → topic-learn |
| Optional summarize after fetch | Article drafting → article-write; codebase audit → codebase-audit |
| Repo text (non-PDF) → editor Read tool | Multi-platform排版导出 |
references/workflow.md — intake, modes, commandsreferences/router.md — decision treereferences/cascade.md — proxy tiers, paywallsreferences/domain-cn.md — 微信、飞书references/domain-stealth.md — JS / 反爬域名references/domain-github-pdf.md — GitHub、PDFreferences/mcp-fallback.mdreferences/output.mdreferences/summarize.mdreferences/vet.mdreferences/topic-sources.mdRead-only codebase health audit — MECE pillars architecture (A1–A6), code (C0–C3), security (S1), verification (V1–V2), dependencies (D1), operations (O1). Chat-only P0/P1/P2 roadmap; effort snapshot, standard, or deep; variants simplicity, anti-pattern, branch, direction, cicd, and pillar-scoped review. Use for codebase audit, architecture review, security hygiene, CI/CD review, over-engineering and anti-pattern scan; not article writing, topic learning, implementation edits, or plans/.
Guard long, ambiguous, or stateful AI-agent work from drift. Use for multi-step implementation, compaction at phase boundaries, task contracts, handoff/resume, or work spanning many tool calls, files, or sessions — not single-shot article writing, topic learning, or codebase audit.
Remove AI-writing tells from finished prose only — neutral tone, preserve facts and structure. Optional deep QA (tell-focused L1–L4). Use for 去AI味, humanize, de-AI polish, 润色; not 审校定稿 with author voice-default, publish-ready writing pipeline, blank-page drafts, or chapter reorder.
Fetch a random classical Chinese poem line from the Jinrishici HTTP API and format with standard Chinese attribution punctuation (author, title). Use for daily poem, briefing garnish, 今日诗词.
Log in via Keep Open API and fetch runs into Garmin-compatible running.json (VDOT, training load, period stats, optional per-km segments). Use for Keep run sync, fetch_keep_run, running.json, or exporting Keep running data.
Audit, design, categorize, distribute, and measure agent skills using lessons from Anthropic's Lessons from building Claude Code: How we use skills. Use when reviewing an existing skill, deciding whether a workflow deserves a skill, planning a skill library, turning team knowledge into skills, choosing skill categories, writing trigger descriptions, designing progressive disclosure, or planning skill marketplace and usage measurement.