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ollama-multi-model-writer

Name: Ollama Multi Model Writer
Author: aviskaar

// Use this skill when writing or drafting research paper sections using local Ollama models on GPU. Routes each section to the optimal local model — DeepSeek-R1 for methods, results, and scientific reasoning; Phi-4-reasoning for abstract, introduction, and conclusions; GLM-4.7-Flash for full drafts and cross-section synthesis. Activate when the user wants a fully local, offline paper-writing workflow without cloud APIs.

在 Manus 中运行

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stars:5

forks:1

updated:2026年3月1日 04:01

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SKILL.md

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related-skills.json

同仓库

template-skill.md

from "aviskaar/open-org"

Replace with a description of the skill and when the agent should use it. Write this as a trigger condition: 'Use this skill when...'

2026-05-115

meridian-investor-research.md

from "aviskaar/open-org"

Run a full Meridian investor intelligence report for any startup URL. Use this skill whenever the user asks to find investors, build an outreach list, write cold emails to investors, or research who to pitch. Also trigger when the user says things like "find investors for [URL]", "run Meridian on [company]", "who should I pitch", "write investor emails for [startup]", or "build my investor outreach". The skill finds 6 matched investors through real web research, verifies facts from public sources, and writes hyper-personalized cold emails grounded in actual portfolio companies, public statements, LinkedIn posts, and press releases. Never fabricate investor facts. Always search before writing.

2026-04-065

autoresearch.md

from "aviskaar/open-org"

Use this skill when you want to run an autonomous ML research loop that iteratively modifies training code, runs timed experiments, evaluates a single metric, and keeps only improvements — repeating indefinitely until manually stopped. Activate when the user wants to autonomously discover better model architectures, hyperparameters, or training strategies overnight or over a defined session, with full cross-platform support (CUDA / Apple Silicon MPS / CPU / Windows).

2026-03-085

content-marketing.md

from "aviskaar/open-org"

Use this skill when a VP of Content or content strategist needs to plan and execute an editorial strategy — producing SEO-optimized blog articles, press releases, LinkedIn posts, Medium publications, website content, and whitepapers, then distributing them across all platforms. Orchestrates seo-automation and whitepaper-engine sub-skills.

2026-03-015

press-release-writer.md

from "aviskaar/open-org"

Use this skill when writing a press release for a product launch, funding round, partnership, award, executive hire, customer milestone, or any company announcement. Produces Forbes-tier, journalist-ready press releases engineered for media pickup, SEO visibility, and social virality. Covers news angle mining, headline optimization, strategic quote crafting, multimedia briefing, journalist targeting, wire distribution, and full social amplification playbook.

2026-03-015

lead-researcher.md

from "aviskaar/open-org"

Use this skill when conducting a complete, end-to-end research project — from initial idea through hypothesis generation, literature review, experiment design, execution, optional replication or paper review, and final research writing. Orchestrates all available research sub-skills (hypothesis-generation, literature-synthesis, experiment-design, code-replication, research-paper-review, research-writing, ieee-paper-generator) into a single, coherent pipeline. Activate when the user wants to pursue a new research idea, replicate or extend a paper's results, or produce a publication-ready manuscript.

2026-03-015

package.json

"author": "aviskaar"

"repository": "aviskaar/open-org"

打开 GitHub 仓库查看创作者相关仓库

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$ download --local

在 Manus 中运行

$ useful --forSOC

高校计算机科学教师教育与图书馆类职业25-1021L4

name	ollama-multi-model-writer
description	Use this skill when writing or drafting research paper sections using local Ollama models on GPU. Routes each section to the optimal local model — DeepSeek-R1 for methods, results, and scientific reasoning; Phi-4-reasoning for abstract, introduction, and conclusions; GLM-4.7-Flash for full drafts and cross-section synthesis. Activate when the user wants a fully local, offline paper-writing workflow without cloud APIs.
license	Apache-2.0
metadata	{"author":"aviskaar","version":"1.0","tags":"ollama, local-llm, research-paper, multi-model, deepseek, phi4, glm, writing, gpu","models":["deepseek-r1:7b","phi4-reasoning","glm4:9b-chat-q4_K_M"]}

Ollama Multi-Model Research Writer

Route each research paper section to the locally-running Ollama model best suited for that task, then synthesize outputs into a unified manuscript — all on your local GPU, no cloud APIs required.

Model Roles

Model	Tag	Primary Role	Best Sections
DeepSeek-R1	`deepseek-r1:7b`	Scientific reasoning & data analysis	Methods, Results, Experiment Design, Ablations
Phi-4-reasoning	`phi4-reasoning`	PhD-level structured writing	Abstract, Introduction, Conclusions, Related Work
GLM-4.7-Flash	`glm4:9b-chat-q4_K_M`	Fluent drafting & synthesis	Full-section drafts, cross-section polish, consistency pass

Prerequisites

Before starting, verify Ollama and the required models are available:

# Check Ollama is running
ollama list

# Pull any missing models
ollama pull deepseek-r1:7b
ollama pull phi4-reasoning
ollama pull glm4:9b-chat-q4_K_M

If Ollama is not installed or a model is missing, surface the error to the user and halt. Do not proceed with a partial model set.

Section Routing Table

Paper Section	Primary Model	Backup Model
Abstract	`phi4-reasoning`	`glm4:9b-chat-q4_K_M`
Introduction	`phi4-reasoning`	`glm4:9b-chat-q4_K_M`
Related Work	`phi4-reasoning`	`glm4:9b-chat-q4_K_M`
Methodology	`deepseek-r1:7b`	`glm4:9b-chat-q4_K_M`
Experimental Setup	`deepseek-r1:7b`	`glm4:9b-chat-q4_K_M`
Results & Discussion	`deepseek-r1:7b`	`glm4:9b-chat-q4_K_M`
Ablation Study	`deepseek-r1:7b`	`glm4:9b-chat-q4_K_M`
Conclusion	`phi4-reasoning`	`glm4:9b-chat-q4_K_M`
Full-paper synthesis	`glm4:9b-chat-q4_K_M`	—

Use the backup model if the primary model is not pulled or fails to respond.

Workflow

Phase 1 — Reasoning Pass (DeepSeek-R1)

Run DeepSeek-R1 on the technical core of the paper. Save output as {reasoning_output}.

# Step 1a: Extract key claims and structure from the research idea
./scripts/ollama-runner.sh deepseek-r1:7b \
  "Analyze this research idea. Extract: (1) the core technical claim, (2) the key methodological steps, (3) what experiments would validate it, (4) what metrics matter. Be precise and scientific. Research idea: {user_idea}"

# Step 1b: Draft the Methods section
./scripts/ollama-runner.sh deepseek-r1:7b \
  "Write a detailed Methodology section for a research paper. Include: problem formulation with formal notation, the proposed approach step-by-step, the training objective or loss function if applicable, and complexity analysis. Use [PLACEHOLDER] for any unknown details. Context: {reasoning_output}"

# Step 1c: Draft Results section (if experiment data is provided)
./scripts/ollama-runner.sh deepseek-r1:7b \
  "Write a Results and Discussion section. Present and interpret the following experiment results honestly. Quantify improvements with exact numbers. Identify failure cases. Results data: {results_data}"

Phase 2 — Structure Pass (Phi-4-reasoning)

Run Phi-4-reasoning to build the academic skeleton. Save output as {structured_output}.

# Step 2a: Write the Abstract
./scripts/ollama-runner.sh phi4-reasoning \
  "Write a 200-word abstract using exactly this 5-part structure: (1) Motivation — why this problem matters, (2) Problem — the precise challenge, (3) Approach — the key method named explicitly, (4) Results — concrete findings with numbers or [RESULT NEEDED], (5) Significance — what this enables. No citations. Spell out all acronyms. Context: {reasoning_output}"

# Step 2b: Write the Introduction
./scripts/ollama-runner.sh phi4-reasoning \
  "Write an Introduction section with: (1) a concrete opening hook, (2) precise problem statement, (3) limitations of prior work with citations, (4) a bulleted list of 3-5 verifiable contributions each starting with a verb (We propose, We demonstrate, We show), (5) one-sentence paper outline. Context: {reasoning_output}"

# Step 2c: Write the Conclusion
./scripts/ollama-runner.sh phi4-reasoning \
  "Write a Conclusion section with: (1) a summary of the problem and approach in fresh language (do not repeat the abstract), (2) key takeaways for practitioners, (3) 2-3 concrete future work directions each one sentence. Do not introduce new claims or results. Context: {reasoning_output} {results_output}"

Phase 3 — Synthesis & Polish Pass (GLM-4.7-Flash)

Run GLM-4.7-Flash to unify all sections into a coherent manuscript. Save output as {synthesis_output}.

# Step 3a: Synthesize all sections into one draft
./scripts/ollama-runner.sh glm4:9b-chat-q4_K_M \
  "You are editing a research paper. Unify these independently-written sections into a single coherent manuscript. Ensure: consistent terminology throughout, the abstract matches the conclusion, all contributions in the introduction appear in the results, no repetition between sections. Sections: REASONING: {reasoning_output} STRUCTURED: {structured_output}"

# Step 3b: Cross-section consistency check
./scripts/ollama-runner.sh glm4:9b-chat-q4_K_M \
  "Review this research paper draft and flag: (1) terminology inconsistencies, (2) claims in the introduction not supported by the results, (3) abstract statements not matching the paper content, (4) any [PLACEHOLDER] or [RESULT NEEDED] tags. List each issue with the section name and line. Draft: {synthesis_output}"

Running a Single Section

When the user asks for one section only, skip the full pipeline:

# Abstract only
./scripts/ollama-runner.sh phi4-reasoning \
  "$(cat templates/abstract-template.md)" \
  "Research context: {user_input}"

# Methods only
./scripts/ollama-runner.sh deepseek-r1:7b \
  "$(cat templates/methods-template.md)" \
  "Research context: {user_input}"

# Any section — quick draft
./scripts/ollama-runner.sh glm4:9b-chat-q4_K_M \
  "Draft the {section_name} section for a research paper. Academic tone, precise language. Research context: {user_input}"

Integration with the Lead Researcher Pipeline

When operating inside the lead-researcher pipeline as Stage 7c:

Trigger: User says they want a local/GPU workflow, or Ollama is confirmed available.

Inputs received from prior stages:

Input	Source Stage
Research Brief	Stage 1
Finalized hypothesis	Stage 2
Literature synthesis & gap statement	Stage 3
Experiment plan	Stage 5
Results summary	User-provided or Stage 6

Actions:

Confirm Ollama is running and all three models are available.
Run Phase 1 (DeepSeek-R1) with the hypothesis + experiment plan as context.
Run Phase 2 (Phi-4-reasoning) with the reasoning output.
Run Phase 3 (GLM-4.7-Flash) to synthesize all phases.
Write the manuscript to paper-{topic}-draft.md.
Report draft location and all [PLACEHOLDER] / [RESULT NEEDED] tags back to lead-researcher.

Optionally hand off to ieee-paper-generator for IEEE formatting after the draft is complete.

Output Format

Save the manuscript as paper-{topic}-draft.md using this structure:

# [Paper Title]

## Abstract
[150-250 words, 5-part structure — generated by phi4-reasoning]

## 1. Introduction
[Generated by phi4-reasoning]

## 2. Related Work
[Generated by phi4-reasoning]

## 3. Methodology
[Generated by deepseek-r1]

## 4. Experimental Setup
[Generated by deepseek-r1]

## 5. Results and Discussion
[Generated by deepseek-r1]

## 6. Conclusion
[Generated by phi4-reasoning]

## References
[Numbered in order of appearance — user must supply actual citations]

---
## Model Attribution
| Section | Model Used |
|---------|-----------|
| Abstract | phi4-reasoning |
| Introduction | phi4-reasoning |
| Methodology | deepseek-r1:7b |
| Results | deepseek-r1:7b |
| Conclusion | phi4-reasoning |
| Synthesis pass | glm4:9b-chat-q4_K_M |

## Data Gaps
[List all [PLACEHOLDER] and [RESULT NEEDED] tags with section references]

## Cross-Validation Flags
[List any inconsistencies detected in the Phase 3 consistency check]

No Fabrication Policy

Never invent experimental results, benchmark scores, or ablation numbers.
Never generate fake citations, paper titles, or author names.
Mark all missing data with [RESULT NEEDED: description].
Mark all unknown details with [PLACEHOLDER: description].
All quantitative claims in the paper must come from the user's actual experiments.

ollama-multi-model-writer

同仓库更多 Skills

同仓库更多 Skills

Ollama Multi-Model Research Writer

Model Roles

Prerequisites

Section Routing Table

Workflow

Phase 1 — Reasoning Pass (DeepSeek-R1)

Phase 2 — Structure Pass (Phi-4-reasoning)

Phase 3 — Synthesis & Polish Pass (GLM-4.7-Flash)

Running a Single Section

Integration with the Lead Researcher Pipeline

Output Format

No Fabrication Policy

Ollama Multi-Model Research Writer

Model Roles

Prerequisites

Section Routing Table

Workflow

Phase 1 — Reasoning Pass (DeepSeek-R1)

Phase 2 — Structure Pass (Phi-4-reasoning)

Phase 3 — Synthesis & Polish Pass (GLM-4.7-Flash)

Running a Single Section

Integration with the Lead Researcher Pipeline

Output Format

No Fabrication Policy