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deep-research-article深度研究文章

Agent Skill

deep-research-article 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。

总安装

558

周安装

23

GitHub Stars

34

下载量

182
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:deep-research-article(深度研究文章)
来源仓库:https://github.com/autobyteus/autobyteus-skills
仓库路径:skills/deep-research-article
安装命令:
npx skills add https://github.com/autobyteus/autobyteus-skills --skill deep-research-article
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/autobyteus/autobyteus-skills --skill deep-research-article

简介

用于生成带强推理与过渡的文章,支持互联网增强与离线模式。

  • 输出 article.md 为主 artifact,附带证据提取与 claim ledger。
  • 可对接 infographic 工具生成幻灯片,实现内容多格式复用。
  • 需明确受众与使用场景以调整语言风格与深度。
  • deep-research-article 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Deep Research Article

Overview (primary artifact)

Produce:

  1. article.md: one coherent article with strong reasoning and transitions

This skill is intentionally topic-agnostic. If the user later wants an image-only deck, pass the approved article directly to infographic-powerpoint-deck, which can ingest raw articles or structured slide tables.

In internet-backed mode, also produce a research package:

  • source_dossier.md
  • evidence_extract.md
  • research_notes.md
  • claim_evidence_ledger.md

Workflow

Step 0 — Confirm constraints and research mode

Ask only what’s necessary:

  • Audience + use case (sermon, lesson, blog, internal memo)
  • Desired length (minutes or word count)
  • Language (CN/EN/bilingual) and tone (academic / pastoral / simple)
  • Success criteria (what a “good final article” must achieve for this user)
  • Explicit non-goals/out-of-scope topics to prevent drift
  • For Bible: translation/version and whether to include cross-references
  • Must-use sources or explicit constraints (paywalled-only, region scope, date range)
  • Whether web research is prohibited by user/policy

Default mode is internet-backed deep research. Only skip internet if user explicitly asks for no-web research. If the user does not specify a length, do not default to a short article. Read references/longform_depth_standard.md and use the long-form depth defaults there.

Step 1 — Internet source sweep (required by default)

Follow references/web_research_protocol.md and create source_dossier.md using references/source_dossier_template.md.

Minimum source counts:

  • Normal: at least 8 sources
  • Deep: at least 12 sources

Coverage requirements:

  • At least 2 source types (e.g., primary + secondary)
  • At least 2 sources that present meaningful tension/alternative interpretation
  • For time-sensitive topics: include recent sources and clearly note dates

Drafting is blocked until this step is complete.

Step 1a — No-web fallback (only when explicitly requested)

If the user explicitly forbids web research:

  • Use only provided/local materials.
  • Build source_dossier.md from local sources.
  • Mark mode as no-web constrained in the dossier.
  • Keep the same extraction/ledger/QA flow, but clearly flag evidence limits in the article.

Step 1b — Evidence extraction (deep reading)

Create evidence_extract.md from the dossier using references/evidence_extraction_template.md.

For each major source, record:

  • exact claim/data point extracted
  • what it supports or challenges
  • uncertainty/caveat

For Bible passages:

  • Note structure (pericope boundaries), repeated words, contrasts, commands, warnings, comfort.
  • Identify “center of gravity” (main burden) and what the text is not saying.

Step 1c — Research notes (synthesis scratchpad)

Create research_notes.md using references/research_notes_template.md:

  • key definitions
  • observations from sources/text
  • competing interpretations
  • load-bearing facts with source IDs
  • open questions

Step 1d — Claim→evidence ledger (required)

Before drafting, build claim_evidence_ledger.md:

  • Use references/claim_evidence_ledger_template.md.
  • Every major claim must have at least one evidence anchor and source ID.
  • Mark confidence and disconfirmation condition.

Step 2 — Argument outline (the real engine)

Write an outline that is easy to defend:

  • Thesis (one sentence)
  • Mainline statement (one sentence): what the article must prove for this user, and what is explicitly out of scope
  • 3–6 supporting moves (each is a claim → evidence → implication)
  • Guardrails: what to omit, what not to overclaim
  • Transition logic: why move A leads to move B

Before drafting, run a quick outline challenge:

  • For each move, state “if removed, what breaks?”
  • Remove or merge moves that don’t break anything important.

Step 3 — Draft article.md

Use the skeleton in references/article_skeleton.md. Also read references/longform_depth_standard.md.

Rules:

  • Prefer short paragraphs with explicit signposting (“因此/所以/因为/然而”).
  • Separate observation vs application.
  • Include explicit citations (source IDs and links) for load-bearing claims.
  • Default to a substantial long-form article unless the user explicitly asked for a brief.
  • Integrate evidence into the body itself. Do not outsource the reasoning to a references section or tell the reader to “see the sources” instead of explaining the point.
  • A 3–6 move outline does not mean a short article. Each move may need multiple subsections or several paragraphs of evidence synthesis, tension, and implications.
  • If evidence is mixed, present both sides and state your judgment criteria.

Step 4 — QA gates (logic + evidence)

Run both:

  • references/logic_qa_checklist.md
  • references/evidence_gates.md
  • references/objectivity_checks.md
  • references/mainline_coherence_gate.md
  • references/final_article_quality_gate.md

If any hard gate fails, return to Step 1 (source sweep) or Step 1b (evidence extraction). In no-web constrained mode, return to Step 1a and narrow claims as needed.

Step 4b — Iterate until it “locks” (human-like drafting)

Deep research writing is normally iterative. Use references/iteration_protocol.md:

  • revise thesis/outline if QA reveals gaps
  • rewrite sections for clarity and scope
  • add/remove evidence so every claim is supported
  • trim or rewrite off-mainline sections (anything that does not advance the thesis)
  • repeat QA + quality scoring until stop criteria are met

Step 4c — Revision log (recommended for real iteration)

Humans keep track of what changed; do the same to avoid thrash:

  • Add a short “Revision notes” section to article.md, or keep a separate revision_log.md.
  • Use references/revision_log_template.md.

Step 5 — Stop at the approved article

The normal end state of this skill is an approved article.md plus the supporting research package. Do not create a separate slide-extraction handoff artifact as part of the default deep-research workflow. If the user wants slides after the article is approved, hand the article directly to infographic-powerpoint-deck.

Output files (recommended)

  • article.md
  • source_dossier.md (required in internet-backed mode)
  • evidence_extract.md (required in internet-backed mode)
  • research_notes.md (required)
  • claim_evidence_ledger.md (required)
  • revision_log.md (optional, but helpful)

References

  • Read references/article_skeleton.md when drafting article.md.
  • Read references/web_research_protocol.md for the required search/deep-reading workflow.
  • Read references/source_dossier_template.md to structure source collection.
  • Read references/evidence_extraction_template.md for claim-level extraction.
  • Read references/source_strategy.md to pick sources and record uncertainty.
  • Read references/research_notes_template.md to structure research_notes.md.
  • Read references/claim_evidence_ledger_template.md to prevent unsupported claims.
  • Read references/logic_qa_checklist.md for the reasoning QA pass.
  • Read references/evidence_gates.md for objective evidence sufficiency gates.
  • Read references/objectivity_checks.md for neutrality and confidence calibration checks.
  • Read references/mainline_coherence_gate.md to catch and remove sections that drift away from the core thesis/user intent.
  • Read references/final_article_quality_gate.md for final draft quality scoring before handoff.
  • Read references/longform_depth_standard.md so the final article defaults to paper-grade depth rather than a short summary.
  • Read references/iteration_protocol.md for iterative improvement and stop criteria.
  • Read references/revision_log_template.md to track iterations cleanly.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.39%
按下载量换算70

Claude

29.54%
按下载量换算54

Cursor

19.18%
按下载量换算35

Gemini CLI

9.94%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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