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Agent Skill · 研究检索

essay-pipeline(论文管道)

essay-pipeline 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。

essay-pipeline 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,权限为只读;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 61,288。

总安装 353周安装 15GitHub Stars 5下载 124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:essay-pipeline(论文管道)
来源仓库:https://github.com/dangeles/claude
仓库路径:skills/essay-pipeline
安装命令:
npx skills add https://github.com/dangeles/claude --skill essay-pipeline
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/dangeles/claude --skill essay-pipeline

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。

essay-pipeline 协调多个专业子技能协同工作,实现交互式论文创作体验。采用主线程调度模式,由 orchestrator 控制各阶段流转节奏。集成 thesis development、structuring、drafting 和 review 等多个环节。适合追求高质量产出的深度写作项目,支持多轮迭代优化。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/essay-pipeline

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/dangeles/claude --skill essay-pipeline;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

essay-pipeline 怎么安装?

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

essay-pipeline 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。

essay-pipeline 需要什么权限?

权限级别是只读。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。

essay-pipeline 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。

essay-pipeline 用的人多吗?

公开数据是累计安装 353 次,近一周安装 15 次,下载 124 次,GitHub 5 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。

essay-pipeline 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
essay-pipeline

论文管道 · 本页

3535只读未明确有提醒

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

Essay Pipeline

Announce: "I'm using the essay-pipeline skill for interactive essay writing."

Architecture Overview

This pipeline uses an orchestrator-as-conductor pattern. You (the orchestrator) run all four interactive stages in your main thread, maintaining direct conversation with the user. Only non-interactive specialists are delegated as sub-agents.

User
  |
  v
essay-pipeline orchestrator (YOU -- main thread)
  |
  |-- Stage 1: Thesis Development (interactive, in your thread)
  |     Reference: references/stage-1-thesis-development.md
  |
  |-- Stage 2: Essay Structuring (interactive, in your thread)
  |     Reference: references/stage-2-essay-structuring.md
  |
  |-- Stage 3: Argument Development (interactive, per-section, in your thread)
  |     Reference: references/stage-3-argument-development.md
  |     Support: essay-fact-checker (via Task tool)
  |
  |-- Stage 4: Paragraph Writing (interactive, per-paragraph, in your thread)
  |     Reference: references/stage-4-paragraph-writing.md
  |     Support: essay-fact-checker (via Task tool)
  |     Support: essay-voice-matcher (via Task tool)
  |
  v
Final Essay Output

Why this architecture: Claude Code sub-agents cannot use AskUserQuestion. Since every stage requires interactive dialogue, the orchestrator must run all stages directly. Sub-agents are used only for non-interactive work (fact verification and voice evaluation).

State Anchoring Protocol

Start every response with a state anchor:

[Stage N/4 - {stage_name}] {status}

Examples:

  • [Stage 1/4 - Thesis Development] Developing thesis through Socratic dialogue
  • [Stage 2/4 - Essay Structuring] Negotiating section structure
  • [Stage 3/4 - Argument Development - Section 2 of 5] Developing argument map
  • [Stage 4/4 - Paragraph Writing - Section 1, Paragraph 3] Drafting paragraph

Re-anchor after:

  • Navigation commands ("go back to...", "show full state")
  • Stage transitions
  • Resume from pause
  • Any context-switching action

Tool Selection Table

SituationToolReason
Load stage reference fileRead toolLoad behavioral instructions from references/
Load style profileRead toolLoad user's voice characteristics
Load session stateRead toolResume from pause or check progress
Load stage outputs (thesis, outline, argument maps)Read toolContext for current stage
Invoke fact-checkerTask toolNon-interactive; returns structured verification results
Invoke voice-matcherTask toolNon-interactive; returns voice assessment
User interaction (all stages)AskUserQuestionALL dialogue, approvals, pushback, navigation
Write session stateBash toolAtomic write protocol (tmp + rename)
Write stage outputs (thesis, outline, maps, draft)Write toolSave approved content to session files
Check prerequisitesBash toolFile existence checks for style profile and samples
Check for existing sessionsBash toolScan /tmp/essay-pipeline-*/ directories
Search style profile or samplesGrep toolFind patterns in profile or sample essays
List sample essaysGlob toolEnumerate available sample essay files

Self-check rule: Before performing any action, ask: "Am I about to verify a fact or evaluate voice consistency?" If YES, delegate to the appropriate sub-agent via Task tool. NEVER verify facts or evaluate voice consistency yourself.

Delegation Mandate

Orchestrator-Owned (YOU do these)

  • Session management: Create, save, restore, pause, resume sessions
  • Stage transitions: Evaluate exit criteria, present summaries, get user approval
  • User dialogue: ALL interactive conversation via AskUserQuestion
  • Quality gate evaluation: Check exit criteria checklists
  • Context assembly: Load relevant files for each stage
  • Stage execution: Follow stage reference file instructions
  • Final essay assembly: Combine approved paragraphs into final document
  • State anchoring: Maintain stage/section/paragraph tracking in every response

Delegated to essay-fact-checker (via Task tool)

  • ALL factual claim verification (Tier 1)
  • ALL proactive research enrichment (Tier 2)
  • Tier 3 escalation identification
  • Source URL verification
  • Contradictory source reporting

Delegated to essay-voice-matcher (via Task tool)

  • ALL voice consistency evaluation
  • Style profile analysis
  • Sample essay consultation
  • Profile-sample conflict detection
  • Cumulative voice tracking

Never do sub-agent work yourself. If you find yourself about to look up a fact or evaluate whether text matches the user's voice, stop and delegate.

Pre-Flight Validation

Before starting the pipeline, check prerequisites:

1. Style Profile Check

Use Bash tool to check for the style profile:

test -f "{configured_path}/style-profile.md" && echo "FOUND" || echo "NOT_FOUND"

If found: Read the profile and keep it in context throughout the session.

If not found: Offer three paths via AskUserQuestion:

  • (a) Quick 5-question profile: Ask the 5 express profile questions (from references/style-profile-template.md), synthesize a minimal profile, save it, and proceed.
  • (b) Proceed without (degraded mode): Voice matching will be limited. The voice-matcher will work from sample essays only, or skip voice matching entirely if no samples either.
  • (c) Exit to create full profile: Provide the template path and exit. User fills out the full template and returns.

2. Sample Essays Check

Use Bash/Glob to check for sample essays:

ls "{configured_path}/samples/"*.md 2>/dev/null | head -5

If found: Note the number of available samples. The voice-matcher will consult them. If not found: Warn the user. Voice matching will rely solely on the style profile.

3. WebSearch Availability

Note that WebSearch is available through the essay-fact-checker sub-agent. If fact-checker invocation fails, fall back to user-provided sources only.

Session Initialization

Check for Existing Sessions

Scan for existing sessions:

ls -d /tmp/essay-pipeline-*/ 2>/dev/null

If sessions found: Present them to the user with status summaries. Ask whether to resume an existing session or start new.

If no sessions found: Proceed to create a new session.

Create New Session

  1. Generate session directory: mkdir -p /tmp/essay-pipeline-$(date +%Y%m%d-%H%M%S)
  2. Initialize session state (see references/session-state-schema.md for full schema)
  3. Offer pushback level selection via AskUserQuestion: Before we begin, how much intellectual pushback would you like? - Full (default): I'll challenge your ideas vigorously to strengthen them. 3 rounds of pushback in thesis development, 2 in argument development. - Light: I'll raise concerns once and accept your response. 1 round per point. - Minimal: I'll offer observations but won't challenge. No pushback rounds. You can change this at any time during the session.
  4. Save initial state using atomic write protocol.
  5. Begin Stage 1.

Pipeline Workflow

For each stage, follow this process:

Stage Entry

  1. Read the stage reference file: references/stage-N-{name}.md
  2. Anchor state: [Stage N/4 - {name}] Beginning stage
  3. Follow the stage's behavioral instructions

Stage Execution

  1. Conduct interactive dialogue with user per the stage's process
  2. Apply stage-appropriate pushback per the configured pushback level
  3. Delegate to sub-agents as needed (fact-checker in Stage 3/4, voice-matcher in Stage 4)
  4. Save incremental progress to session files

Stage Exit

  1. Evaluate exit criteria (from the stage reference file)
  2. Present stage completion summary to the user
  3. Get user approval at quality gate via AskUserQuestion
  4. Save stage outputs to session directory
  5. Update session state (atomic write)
  6. Ask user to confirm readiness to proceed to next stage

Stage Transition

  1. Drop the previous stage's reference file from active consideration
  2. Keep stage OUTPUT files (thesis, outline, argument maps) as context
  3. Load the next stage's reference file
  4. Begin the next stage's process

Stage-Specific Execution Notes

Stage 1: Thesis Development

  • Load: references/stage-1-thesis-development.md
  • Follow its Socratic dialogue process
  • Express path available for pre-formed theses
  • Quality Gate G1: Thesis is clear, defensible, and approved

Stage 2: Essay Structuring

  • Load: references/stage-2-essay-structuring.md
  • Read Stage 1 output for thesis and claim type
  • Negotiate structure, length, audience
  • Quality Gate G2: Outline is complete and approved

Stage 3: Argument Development (Per-Section Loop)

  • Load: references/stage-3-argument-development.md
  • Loop through each section in the outline: for each section in outline: Develop argument map for this section Batch-invoke fact-checker for all claims in this section Handle fact-checker results with user Get user approval for section argument map Save argument map to session directory Update session state
  • After all sections: Present bird's-eye review of all argument maps
  • Quality Gate G3-Final: All argument maps cohere and are approved
  • Optional: User can request paragraph writing for completed sections (controlled forward-jumping)

Stage 4: Paragraph Writing (Per-Paragraph Loop)

  • Load: references/stage-4-paragraph-writing.md
  • Loop through each section, then each paragraph: for each section: for each paragraph in section: Draft paragraph following argument map + style profile Self-audit for argument map compliance Present to user for approval Save approved paragraph Invoke voice-matcher on complete section Present section for bird's-eye review
  • After all sections: Final voice check, final fact-check sweep, resolve deferred verifications
  • Quality Gate G5: Full essay approved

Quality Gates

GateLocationCriteriaFailure Action
G1End of Stage 1Thesis is clear, defensible, claim type identified, user approvedLoop Stage 1
G2End of Stage 2Outline complete, length agreed, sections purposeful, user approvedLoop Stage 2
G3 (per section)End of each Stage 3 iterationClaims evidenced, counterarguments addressed, sources verified, user approvedLoop current section
G3-FinalAfter all Stage 3 sectionsAll argument maps cohere, bird's-eye approvalRevisit specific sections
G4 (per paragraph)End of each Stage 4 iterationVoice matches, facts verified, user approvedRevise paragraph
G4-SectionAfter all paragraphs in a sectionSection reads well, voice consistentRevise specific paragraphs
G5End of Stage 4Full essay reviewed, voice consistent, all facts verified, all deferred claims resolved, user gives final approvalRevise specific sections/paragraphs

User Navigation

At any point, the user can issue navigation commands. Detect these in user responses and handle accordingly:

CommandAction
"Show full state" / "Where am I?"Display current stage, section, paragraph; completed work summary; pending work
"Show essay so far"Read and display all approved paragraphs from stage-4-draft.md
"Go back to thesis" / "Go back to Stage 1"Assess impact, archive downstream if needed, return to Stage 1
"Go back to outline" / "Go back to Stage 2"Assess impact, archive downstream if needed, return to Stage 2
"Go back to section N arguments"Return to Stage 3 for section N
"Pause" / "Save and exit"Save state, display resume instructions
"Resume"Load state, present summary, continue
"Change pushback level"Update pushback_level in session state

Stage Revisit Cascade

When the user goes back to an earlier stage:

  1. Assess change impact (see references/session-state-schema.md):

- Minor: Flag downstream for review - Moderate: Archive affected downstream artifacts - Major: Archive ALL downstream artifacts, warn user

  1. Present impact to user: "Going back to [stage] will [impact description]. Your current work will be [preserved/archived/flagged]. Continue?"
  2. If user confirms: Update session state, version old artifacts, begin the earlier stage
  3. Never silently discard work

Error Handling Protocol

FailureDetectionAction
Fact-checker sub-agent failsTask tool returns errorRetry once. If fails again: mark claims as DEFERRED; inform user; proceed
Voice-matcher sub-agent failsTask tool returns errorSkip voice check; note in metadata; inform user; proceed
WebSearch unavailableFact-checker reports service failureGraceful degradation: user-provided sources only; inform user
Session state corruptYAML parse fails on ReadLevel 1: Recover from.bak. Level 2: Reconstruct from artifacts. Level 3: Ask user
Style profile missingPre-flight check finds no fileOffer 3 paths: quick profile, degraded mode, exit to create
Style profile malformedRead returns unparseable contentWarn user; offer to proceed with best-effort interpretation or exit to fix
Literature-researcher unavailablePre-flight check or runtimeInform user; Tier 3 escalation unavailable; expanded Tier 2 search as substitute
Stage reference file missingRead tool returns errorFATAL: Cannot proceed. Report error and stop. This indicates a broken installation.

Graceful Degradation Hierarchy

When components fail, degrade gracefully rather than stopping:

Full capability (all components working)
  |
  v (fact-checker fails)
Degraded: User-provided sources only; claims marked DEFERRED
  |
  v (voice-matcher also fails)
Degraded: No voice checking; user self-evaluates voice
  |
  v (session state corrupt)
Recovery mode: Reconstruct from artifacts
  |
  v (artifacts also lost)
Manual recovery: Ask user where to resume

Timeout Configuration

ComponentTimeoutExceeded Action
Fact-checker (per invocation)3 minutesReturn "verification timeout"; mark claims as DEFERRED
Voice-matcher (per invocation)3 minutesSkip voice check; note in metadata
Session inactivity24 hoursAuto-pause; session eligible for resume
Session abandonment7 daysWarn on next access; offer cleanup or resume

When invoking sub-agents via Task tool, include timeout guidance in the task prompt.

Fact-Checker Invocation Protocol

During Stage 3 (Argument Development)

  • When: After developing the argument map for a section
  • What: Batch all factual claims in the section into one invocation
  • How: Prepare claim list per the fact-checker's input format; invoke via Task tool
  • Result handling: Present results to user; update argument map with verification status and sources

During Stage 4 (Paragraph Writing)

  • When: When a draft paragraph introduces NEW claims not already verified in Stage 3
  • What: Only new claims (do NOT re-verify claims already in argument maps)
  • How: Same as Stage 3

Final Sweep

  • When: After all paragraphs are approved, before Quality Gate G5
  • What: All claims in the complete essay (one batch invocation)
  • Purpose: Catch any claims modified during user edits, verify deferred claims

Deferred Verification Queue

  • Track unverified claims in session state under deferred_verifications
  • Resolve ALL deferred claims before Quality Gate G5
  • Resolution: Retry verification, user provides source, revise claim, or remove claim

Task Tool Invocation Format

Task: essay-fact-checker

Verify the following claims from Section [N] of a science blog essay about [topic].

[YAML claim batch]

Return results as YAML with verification status, source URLs, and notes for each claim.
Timeout: Complete within 3 minutes.

Voice-Matcher Invocation Protocol

Invocation Schedule

  • After each section is complete: Section-level voice consistency check
  • After full essay is complete: Essay-level voice consistency check
  • On user request: Any time the user asks about voice

Calibration (First Invocation)

On the first voice-matcher result:

  1. Present the assessment to the user
  2. Ask: "How accurate is this voice assessment? Rate 1-5, where 5 is very accurate."
  3. Save the user's rating in session state under voice_calibration
  4. Include calibration data in all subsequent voice-matcher invocations

Score Thresholds

ScoreAction
5/5Proceed; inform user of strong match
4/5Proceed; mention specific observations
3/5Present assessment to user; ask if they want revisions
2/5Flag for revision; present specific suggestions
1/5Pause and suggest style profile review

Task Tool Invocation Format

Task: essay-voice-matcher

Evaluate the following text for voice consistency against the user's style profile.

Text to evaluate:
[text]

Style profile path: [path]
Sample essays path: [path]
Context: Stage [N], Section [N], [content type]
Previous assessments: [YAML list of prior assessments]
Calibration: [calibration data if available]
Voice reference mode: [profile | sample | hybrid]

Return assessment as YAML with score (1-5), observations, and suggestions.
Timeout: Complete within 3 minutes.

User Override Protocol

When the fact-checker's findings conflict with the user's claim:

  1. Present evidence: "The fact-checker found [result]. Your claim states [claim]. Source: [URL]"
  2. Offer options via AskUserQuestion:

- Revise the claim to match verified evidence - Rephrase (keep the spirit, adjust specifics) - Keep with override (log the disagreement) - Provide your own source

  1. Log all overrides in fact-check-log.md
  2. Surface at final review: At Quality Gate G5, present all overrides for one final confirmation

Argument Map Compliance (Stage 4)

Before presenting each paragraph draft in Stage 4:

  1. Self-audit: Which argument map points does this paragraph cover?
  2. Tag content: Mark each sentence as MAPPED (covers an argument map point) or NOT IN MAP
  3. Flag deviations: If NOT IN MAP content exists, explicitly notify the user:

- "This paragraph includes content not in your argument map: [description]." - Options: (a) Keep and add to map retroactively, (b) Remove, (c) Go back to Stage 3

Context Window Management

For long essays (5,000+ words), context window pressure is a concern. Follow these rules:

ContentWhen LoadedWhen DroppedBudget
Style profileSession startNever (kept throughout)~500 words
Current stage reference fileStage entryStage exit~300 words
Thesis statementStage 1 completionNever~50 words
Essay outlineStage 2 completionNever~300 words
Current section argument mapStage 3/4 per sectionMoving to next section~200-400 words
Previous sections argument mapsSummarized after completionAfter summary~50 words each
Current section approved paragraphsStage 4 per sectionMoving to next sectionVariable
Previous sections full textWritten to fileImmediately0 (file-based)

Key rule: Rely on FILES for historical content, not conversation memory. After each section is complete in Stage 4, write the full section text to stage-4-draft.md and drop it from active context. When needed for continuity, read only the last paragraph of the previous section.

Session State Resilience

Atomic Writes

All session state updates use the atomic write protocol:

  1. Write to .tmp file
  2. Copy current state to .bak
  3. Rename .tmp to primary

Incremental Saves

Save progress at granular checkpoints:

  • After each completed argument point (Stage 3)
  • After each approved paragraph (Stage 4)
  • After each stage transition
  • After any user navigation command

Recovery

On session load, attempt: Primary state -> Backup state -> Artifact reconstruction (see references/session-state-schema.md)

Pipeline Completion

When all stages are complete and Quality Gate G5 is passed:

  1. Final voice check: Invoke voice-matcher on full essay
  2. Final fact-check sweep: Invoke fact-checker on all claims in complete essay
  3. Resolve deferred verifications: All must be resolved
  4. Present completion summary: Pipeline Complete! Word count: [N] words Sections: [N] Sources verified: [N] claims with [M] unique sources Voice consistency: [score]/5 User overrides: [N] (see fact-check-log.md) Deferred claims resolved: [N] of [N] Time spent: [duration]
  5. Write final essay to {session_dir}/final-essay.md
  6. Offer export: "Would you like me to write the essay to a specific file path?"
  7. Archive session: Mark session as "completed" in state

Quick Reference: Stage Summary

StageReference FilePushback TypeDelegates ToQuality Gate
1. Thesis Developmentstage-1-thesis-development.mdLogical rigorNoneG1
2. Essay Structuringstage-2-essay-structuring.mdAudience awarenessNoneG2
3. Argument Developmentstage-3-argument-development.mdDevil's advocacyessay-fact-checkerG3, G3-Final
4. Paragraph Writingstage-4-paragraph-writing.mdMinimalessay-fact-checker, essay-voice-matcherG4, G4-Section, G5

平台分布

Codex

34.98%
按下载量换算43

Claude

30.35%
按下载量换算38

Cursor

18.03%
按下载量换算22

Gemini CLI

9.93%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

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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