OpusFlame Deep Research

Autonomous multi-model deep research with framework-driven reasoning. Spawns 4 parallel model agents (Gemini 2.5 Pro, o3, Opus, MiniMax), each applies best-p...

Security Scan

The skill's instructions, requirements, and artifacts are consistent with its stated purpose of running multi-model, framework-driven research; it does not request extra credentials or install code, but it does mandate heavy web fetching and persistent memory writes which you should understand before enabling.

Purpose & Capability

Name and description (autonomous multi-model deep research) match the SKILL.md: it explicitly spawns four model agents, requires many web searches and fetches, applies frameworks, and writes per-model outputs and a merged report. No unrelated credentials, binaries, or installs are requested.

Instruction Scope

Instructions are narrowly focused on research tasks but demand extensive web activity (minimum 10 searches per model, 15 unique source URLs per model, web_fetch of full articles) and persistent saves under memory/research/*. This is proportionate for deep research but increases network activity, potential scraping of paywalled or copyrighted material, and the chance that user-provided sensitive topics could be written to memory. The skill does not instruct reading unrelated files or env vars.

Install Mechanism

Instruction-only skill with no install spec and no code files. Lowest-risk install profile — nothing is written to disk by an installer.

Credentials

No environment variables, credentials, or config paths are requested. The listed memory save paths are reasonable given the purpose. There are no disproportionate secret requests.

Persistence & Privilege

Does not request always:true or other elevated privileges. It does instruct saving outputs to memory/research/—this is expected for research but means results are persisted and should be reviewed/cleared if sensitive.

Assessment

This skill appears internally consistent with its stated goal of deep, multi-model research. Before installing, note that it will: (1) perform many automated web searches and fetch full articles (which can hit paywalls, produce heavy network traffic, or surface copyrighted material), (2) save per-model outputs and the merged report to memory/research/ (persisting the query and results), and (3) autonomously spawn parallel sessions to run those model agents. Because the skill comes from an unknown source with no homepage, consider limiting use to non-sensitive topics, avoid providing secrets or proprietary documents as prompts, inspect any saved memory files after a run (and clear them if necessary), and verify that your platform’s web_fetch policies (rate limits, terms of service for target sites) are acceptable. If you need stronger assurance, ask the publisher for provenance or a link to documentation/source code before enabling for general use.

Deep Research (Multi-Model + Framework-Driven)

Autonomous research system that runs 4 AI models in parallel, each applying relevant analytical frameworks, then cross-validates and merges findings into a comprehensive cited report.

Architecture

User Question
     │
     ▼
┌─ Phase 0: Framework Selection ─┐
│  Identify best-practice         │
│  framework(s) for this question │
└────────────┬────────────────────┘
             │
     ┌───────┼───────┐───────┐
     ▼       ▼       ▼       ▼
  Gemini    o3     Opus   MiniMax
  2.5 Pro         4       M2.5
  (search  (deep  (nuance (China/
  heavy)   logic) +balance)alt view)
     │       │       │       │
     └───────┼───────┘───────┘
             ▼
      Phase 5: Merge & Cross-Validate
             │
             ▼
       Final Report (PDF)

Phase 0: Framework Selection (MANDATORY — before any research)

Before researching, ask: "Is there a best-practice framework for answering this type of question?"

Framework Lookup Table

Question Type	Frameworks to Apply
Competitive strategy	Porter's Five Forces, 7 Powers (Helmer), Schwerpunkt/High Ground (Packy), SWOT
Market entry / sizing	TAM/SAM/SOM, Blue Ocean Strategy, Jobs-to-be-Done
Business model evaluation	Business Model Canvas, Unit Economics, Ramp vs Route test (point solution vs platform?)
Investment / valuation	DCF, Comparable Analysis, Venture method, Power Law thesis
Product strategy	JTBD, Kano Model, Value Prop Canvas, Hook Model
Growth / GTM	AARRR Pirate Metrics, Bullseye Framework, STP (Segmentation-Targeting-Positioning)
Technology assessment	Gartner Hype Cycle, Wardley Maps, Build vs Buy matrix
Risk analysis	Pre-Mortem, FMEA, Scenario Planning
Organizational / ops	OKR analysis, RACI, Theory of Constraints
Pricing	Van Westendorp, Conjoint, Value-based pricing framework
Industry analysis	Value Chain Analysis, Industry Lifecycle, Winner-Takes-More thesis
Person / hiring	Track Record Analysis, Reference Triangle, Founder-Market Fit

If a framework applies:

• Include it in the prompt to each model
• Structure the model's analysis around the framework's components
• The final report should explicitly reference which framework(s) were used and why

If no standard framework applies:

• State "No standard framework identified — using first-principles analysis"
• Each model reasons from first principles with explicit assumptions stated

Phase 1: Decompose (30s)

Break the topic into 5-8 research sub-questions. Think like an investigative journalist:

• What are the key facts?
• What are different perspectives/sources?
• What's the timeline/history?
• What data/evidence exists?
• What are the unknowns or controversies?

Phase 2: Spawn 4 Model Agents (Parallel)

Spawn 4 sub-agents using sessions_spawn, each with a different model:

Model 1: gemini       (google/gemini-2.5-pro)  — Search-heavy, broad coverage
Model 2: o3           (openai/o3)              — Deep logical reasoning, contrarian
Model 3: opus         (anthropic/claude-opus-4-6) — Nuanced, balanced synthesis
Model 4: minimax      (minimax/MiniMax-M2.5)   — Alternative perspectives, China/grey-area

Prompt Template for Each Model

## Research Task
[Topic]

## Framework
You MUST structure your analysis using: [Framework Name]
Apply each component of the framework systematically to the topic.
If data is missing for a component, note it explicitly.

## Sub-Questions
[List of 5-8 sub-questions]

## Instructions
1. Use web_search extensively (minimum 10 unique searches)
2. Use web_fetch to read full articles for key sources
3. Cross-reference claims across 2+ sources
4. Structure findings around the framework components
5. Flag disagreements, unknowns, and low-confidence claims
6. Minimum 15 unique source URLs
7. Output format: markdown with inline citations [1][2]...
8. End with a Sources section listing all URLs

## Quality Rules
- Every factual claim needs a source
- Prefer primary sources (filings, official reports) over secondary
- Note source freshness — flag anything >6 months old
- Include opposing viewpoints
- State confidence level (high/medium/low) for key conclusions

Model-Specific Instructions

• Gemini: "You are the primary search engine. Cast the widest net. Find obscure sources others would miss. Prioritize data and numbers."
• o3: "You are the deep reasoner. Challenge assumptions. Look for logical flaws in conventional wisdom. Apply the framework with maximum rigor. If the consensus is wrong, explain why."
• Opus: "You are the synthesizer. Balance multiple perspectives fairly. Identify nuance others miss. Connect dots across disciplines."
• MiniMax: "You are the alternative perspective agent. Consider non-Western viewpoints, grey areas, unconventional strategies. What would a Chinese entrepreneur or contrarian investor do differently?"

Phase 3: Wait for Completion

All 4 models run in parallel via sessions_spawn with mode="run". Do NOT poll in a loop — they auto-announce when done.

Phase 4: Collect Individual Reports

Save each model's output:

memory/research/[topic]-gemini-[date].md
memory/research/[topic]-o3-[date].md
memory/research/[topic]-opus-[date].md
memory/research/[topic]-minimax-[date].md

Phase 5: Cross-Validate & Merge

This is the most critical phase. The primary agent (you) must:

5a. Agreement Matrix

Create a matrix of key claims and which models agree/disagree:

| Claim | Gemini | o3 | Opus | MiniMax | Confidence |
|-------|--------|----|----|---------|------------|
| [claim 1] | ✅ | ✅ | ✅ | ❌ | High (3/4) |
| [claim 2] | ✅ | ❌ | ✅ | ✅ | High (3/4) |
| [claim 3] | ✅ | ✅ | ❓ | ❓ | Medium (2/4) |

5b. Conflict Resolution

For each disagreement:

• Identify the root cause (different data? different logic? different framework interpretation?)
• Check which model has the stronger source
• If genuinely uncertain, present both sides in the final report

5c. Framework Synthesis

• Map findings back to the framework structure
• Ensure every framework component has been addressed
• Note which components had strong consensus vs. disagreement

5d. Error Catching

From experience, models commonly get wrong:

• Platform-specific limits (posting frequency, API limits)
• Pricing (especially for niche tools — often 10-30x off)
• Regulatory details
• Recency of data

Verify any quantitative claim that only one model makes.

Phase 6: Final Report

# [Topic] — Deep Research Report

**Framework Used**: [Name] — [why this framework]
**Models**: Gemini 2.5 Pro, o3, Opus 4, MiniMax M2.5
**Date**: [date]
**Total Searches**: [count across all models]

## Executive Summary
3-5 sentence overview. Note consensus level.

## Framework Analysis

### [Framework Component 1]
Analysis with model consensus noted. [1][2]

### [Framework Component 2]
...

## Key Findings (Beyond Framework)
Discoveries that don't fit neatly into the framework.

## Model Disagreements
Where models diverged and why.

## Agreement Matrix
[The table from 5a]

## Data & Evidence
Tables, numbers, comparisons.

## Risks / Unknowns
What we couldn't confirm. Low-confidence areas.

## Conclusion & Recommendations
Actionable takeaways ranked by confidence.

## Sources
[1] Title — URL
[2] ...

Phase 7: Deliver

1. Save final report to memory/research/[topic]-终极版-[date].md
2. Generate PDF via pymupdf and save to ~/.openclaw/media/outbound/
3. Send PDF to user via message tool

Quality Standards

• Minimum sources: 15 unique URLs per model (60+ total across 4 models)
• Source diversity: No more than 3 citations from same domain per model
• Freshness: Prefer sources < 6 months old; flag older data
• Cross-validation: Key claims must appear in 2+ models' findings
• Framework compliance: Every framework component must be addressed
• Confidence scoring: High (3-4 models agree + strong sources), Medium (2 models or weak sources), Low (1 model or no source)
• No hallucination: Every factual claim must have a source

Adaptation by Topic Type

Financial / Stock Research

• Frameworks: DCF, Comparable Analysis, Power Law
• Check SEC/regulatory filings, earnings transcripts
• Include key metrics (revenue, margins, P/E, debt)
• See references/financial-research.md

Market / Industry Research

• Frameworks: Porter's Five Forces, TAM/SAM/SOM, 7 Powers
• Competitive landscape, key players, market share
• Apply Winner-Takes-More thesis where relevant

Strategy / Business Model

• Frameworks: Schwerpunkt/High Ground, Business Model Canvas, JTBD
• Identify the constraint, the scarce asset, expansion path
• Compare to historical precedents (Rockefeller, Ramp, etc.)

Technical / Product Research

• Frameworks: Wardley Maps, Build vs Buy, Gartner Hype Cycle
• Architecture, benchmarks, alternatives matrix
• Community sentiment (GitHub, HN, Reddit)