---

name: mine-writing-patterns description: Read one or more papers and update the active installed paper-miner writing memory with reusable writing patterns, structure signals, reusable phrasing, venue-specific signals, and rebuttal-friendly language. args:

• name: source description: Paper source path, URL, arXiv link, or a short description of the target papers required: true
• name: focus description: Optional focus area (general/introduction/method/results/rebuttal/venue) required: false default: general tags: [Research, Writing, Paper Mining, Knowledge Extraction]

---

/mine-writing-patterns - Installed Writing Memory Mining

Read the paper source "$source" and update the active installed paper-miner writing memory.

Default target

Always write mined knowledge into the active installed OpenCode skill memory, not the repository checkout copy:

~/.opencode/skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md

This command does not create project-specific writing memory unless the user explicitly asks for a project-local writing memory.

When to use

Use this command when you want to:

• learn reusable writing patterns from a strong paper,
• study how a venue frames introductions, methods, results, or rebuttals,
• mine phrasing and structure signals before drafting,
• enrich the writing memory that powers ml-paper-writing and review-response.

Usage

Basic usage

/mine-writing-patterns path/to/paper.pdf

Mine from an arXiv paper

/mine-writing-patterns https://arxiv.org/abs/2301.xxxxx

Focus on rebuttal or venue signals

/mine-writing-patterns path/to/paper.pdf rebuttal
/mine-writing-patterns path/to/paper.pdf venue

Workflow

Step 1: Resolve the paper source

Acceptable inputs:

• local PDF
• local DOCX
• arXiv URL
• readable web URL
• short natural-language request that identifies the paper(s)

If the source is ambiguous, narrow it before mining.

Step 2: Invoke paper-miner

Use the paper-miner agent to:

• extract paper content,
• identify reusable writing knowledge,
• merge it into the active installed writing memory,
• avoid duplicate entries,
• preserve source attribution.

Step 3: Respect the focus mode

Interpret $focus as follows:

Focus	Priority
general	Mine balanced signals across all major sections
introduction	Emphasize framing, motivation, and contribution setup
method	Emphasize exposition style, technical sequencing, and clarity
results	Emphasize result narration, claim-evidence language, and interpretation
rebuttal	Emphasize clarification phrases, response structure, and reviewer-facing tone
venue	Emphasize venue-specific style and convention signals

Step 4: Update the canonical memory only

The canonical write target is the active installed OpenCode skill memory:

~/.opencode/skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md

Update one or more of these sections:

• Writing patterns mined
• Structure signals
• Reusable phrasing
• Venue-specific signals
• How this helps our writing
• Source index

If that file is unavailable in the current runtime, use the configured installed skill home for the active runtime and state the exact path in the final summary. Do not silently fall back to the repository checkout.

Do not create project-local writing memory. Do not scatter the mined result across multiple maintained knowledge files.

Step 5: Return a standardized mining summary

The final response should follow the paper-miner standardized output format:

• metadata
• memory write summary
• new reusable patterns
• how we should reuse this
• blockers or limits

Related integrations

• ml-paper-writing reads this active installed memory before drafting or revising sections.
• review-response reads this active installed memory when tone, phrasing, and rebuttal structure matter.
• paper-miner is the agent that performs the actual mining work.

Success criteria

• the target paper is read successfully,
• reusable writing knowledge is merged into the canonical memory,
• source attribution is preserved,
• no project-specific writing memory is created,
• the user receives a standardized mining summary.