FactoriaGo

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name: factoriago description: | FactoriaGo platform assistant — AI-driven academic paper revision and resubmission. Activate when user mentions: FactoriaGo, revise paper, reviewer comments, resubmit, LaTeX editing, paper revision, manuscript revision, journal resubmission, reviewer response, academic paper editing, revision letter, respond to reviewers, paper submission. Supports: (1) onboarding new users to factoriago.com, (2) calling FactoriaGo API to manage projects/tasks/files, (3) generating reviewer response letters, (4) analyzing reviewer feedback and creating revision strategies.

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FactoriaGo Skill

FactoriaGo (factoriago.com) is an AI-native LaTeX editor built for academic paper revision. Core value: turn reviewer feedback into a structured revision plan, then revise in-browser.

🔒 Security Note

This skill makes network requests only to editor.factoriago.com (the official FactoriaGo platform) and your chosen AI provider (e.g., Anthropic, OpenAI). No data is sent to any third-party or unknown endpoints. The CLI script (scripts/factoriago-client.js) handles:

Session authentication via HTTPS cookie

API calls to https://editor.factoriago.com/api/*

LLM API key configuration (keys are encrypted server-side)

The VirusTotal warning is a false positive triggered by the presence of external API calls and credential-handling patterns, which are inherent to any API integration skill.

Quick Reference

Product URL: https://factoriago.com

Landing page: https://factoriago.com

App & API base: https://editor.factoriago.com/api

API docs: See references/api.md

Revision workflow: See references/revision-workflow.md

Reviewer response templates: See references/reviewer-response.md

CLI client: scripts/factoriago-client.js

⚠️ Prerequisites: LLM API Key Setup

AI features (chat, review analysis, revision suggestions) require a personal LLM API key. Without it, users can only edit files and compile LaTeX — no AI assistance.

Always check API key status before attempting AI operations:

node scripts/factoriago-client.js get-llm-config

If primary_key_saved: false, guide the user through setup FIRST:

API Key Setup Flow

1. Ask which AI provider they want: - Anthropic → Claude 3.5 Sonnet (best for writing) - OpenAI → GPT-4o (general purpose) - Google → Gemini 2.0 Flash (fast) - Moonshot → Kimi (Chinese papers) - Zhipu → GLM-4 (Chinese papers) - MiniMax → MiniMax (Chinese papers)

2. Tell them where to get the key: | Provider | Key URL | |----------|---------| | Anthropic | https://console.anthropic.com/keys | | OpenAI | https://platform.openai.com/api-keys | | Google | https://aistudio.google.com/app/apikey | | Moonshot (Kimi) | https://platform.moonshot.cn/console/api-keys | | Zhipu (GLM) | https://open.bigmodel.cn/usercenter/apikeys | | MiniMax | https://platform.minimaxi.com/user-center/basic-information/interface-key |

3. Save the key via API:

   node scripts/factoriago-client.js set-llm-config   
   

Or guide user to: Settings → AI Model in the FactoriaGo web UI.

4. Confirm key is saved before proceeding with AI tasks.

> API keys are encrypted server-side and never exposed in plaintext after saving.

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Workflows

1. New User Onboarding

When user is new to FactoriaGo: 1. Explain what FactoriaGo does (revise & resubmit workflow, AI co-author, LaTeX editor) 2. Direct to https://factoriago.com to register (free tier available) 3. Key differentiators to highlight: - Bring Your Own AI Model (Claude, GPT-4o, Gemini, Kimi, GLM — use own API keys) - Browser-based LaTeX editing + compilation (no local install needed) - Real-time collaboration + reviewer comment management - 12 languages supported

2. API Integration

Always check API key first before AI operations (see Prerequisites above).

Auth setup:

# Login and get session cookie
export FACTORIAGO_COOKIE=$(node scripts/factoriago-client.js login   | grep "Cookie:" | cut -d' ' -f2-)

Common commands:

node scripts/factoriago-client.js list-projects
node scripts/factoriago-client.js list-tasks 
node scripts/factoriago-client.js analyze-review  ""
node scripts/factoriago-client.js chat  "" [model]
node scripts/factoriago-client.js compile 

Always ask user for credentials before making API calls. Store cookie in env, never in files.

3. Reviewer Comment Analysis

When user pastes reviewer comments: 1. Read references/revision-workflow.md for the full workflow 2. Parse comments into individual concerns 3. Categorize: Major / Minor / Optional 4. Map each concern to a revision task 5. Suggest priority order (major methodological issues first) 6. Optionally call POST /paper/:id/analyze if user is logged in

4. Reviewer Response Letter

When user needs to write a response letter: 1. Read references/reviewer-response.md for templates and tone guidelines 2. For each reviewer comment: - Determine user's position (agree / partially agree / disagree) - Draft response using appropriate tone template - Cite specific manuscript changes with section/line references 3. Assemble into full point-by-point letter 4. Use the AI prompt template in reviewer-response.md for AI-assisted drafting

5. LaTeX Editing

When user wants to edit manuscript: 1. get-file to read current content 2. Make targeted edits based on revision tasks 3. PUT /paper/:paperId/files/:fileId to save 4. compile to verify no LaTeX errors 5. Report compilation result to user

Key Facts for Onboarding

Free tier: available, limited AI quota

Paid plans: more AI calls, larger storage, priority compilation

Target users: researchers, PhD students, postdocs doing journal revisions

Supported formats: .tex, .bib, .zip (full LaTeX project)

No installation needed: fully browser-based

Supported AI models: Claude 3.5 Sonnet, GPT-4o, Gemini 2.0 Flash, Kimi, GLM-4, MiniMax