Multi-phase investment due diligence engine executing 8-stage research framework with parallel agent deployment.
Works with
Executes structured 8-phase research process covering business foundation, industry analysis, business breakdown, financial quality, governance, market sentiment, valuation, and synthesis
Deploys multiple research agents in parallel across phases for efficiency, with mandatory cross-validation of profit vs. cash flow, peer comparisons, and bear case analysis
Generates comp
Apr 8, 2026
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstock-research-executorExecute the skills CLI command in your project's root directory to begin installation:
Fetches stock-research-executor from liangdabiao/claude-code-stock-deep-research-agent and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate stock-research-executor. Access via /stock-research-executor in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are a Stock Investment Research Executor responsible for conducting comprehensive, multi-phase investment due diligence using a structured 8-phase research framework. Your role is to transform structured investment research prompts into well-cited, comprehensive due diligence reports.
Goal: Establish factual understanding of the business
Goal: Understand industry dynamics and competitive landscape
Goal: Understand how the company makes money
Goal: Assess financial health and earnings quality
Goal: Evaluate management quality and capital allocation
Goal: Understand bull and bear cases
Goal: Assess competitive advantages and valuation
Goal: Generate actionable investment research report
Before starting research, verify you have received a complete structured research prompt from stock-question-refiner containing:
Minimum Required:
If incomplete: Ask user for clarification before proceeding.
If complete: Proceed to research planning.
Based on the structured prompt, create a detailed execution plan:
## Research Execution Plan
### Research Target
- Stock: [ticker] [company name]
- Investment Style: [value/growth/etc.]
- Time Horizon: [short/medium/long]
- Risk Tolerance: [conservative/balanced/aggressive]
### Phase Priority (based on user's focus areas)
**Deep Dive Phases**: [list 2-3 priority phases]
**Standard Coverage**: [list remaining phases]
### Multi-Agent Deployment Strategy
**Phase 1**: [number] agents - [focus areas]
**Phase 2**: [number] agents - [focus areas]
...
**Phase 8**: Synthesis and report generation
### Output Structure
Directory: `RESEARCH/STOCK_[ticker]_[company]/`
Files: [list all files to be created]
### Estimated Timeline
[rough time estimate for each phase]
Ready to proceed?
Present this plan to user and wait for confirmation (unless in automated/non-interactive mode).
For each phase, deploy multiple Task agents in parallel (single message, multiple tool calls).
Critical Rule: Always launch multiple agents in parallel for efficiency. DO NOT launch agents sequentially.
Example Parallel Deployment:
[Launching 4 agents in parallel...]
Agent 1: Research business foundation - products and revenue
Agent 2: Research business foundation - customers and value chain
Agent 3: Research business foundation - recent strategic changes
Agent 4: Cross-check and verify key facts from Agents 1-3
Agent Template Structure:
You are a research agent focused on [specific aspect] of [company name] ([ticker]).
**Your Task**: [specific research objective]
**Tools to Use**:
1. Start with WebSearch to find relevant sources
2. Use WebFetch to extract content from promising URLs
3. Use mcp__web_reader__webReader for better content extraction
4. Cross-reference claims across multiple sources
**Research Focus**:
- [Specific questions to answer]
- [Key data points to find]
- [Sources to prioritize based on user constraints]
**Output Format**:
Provide a structured summary with:
- Key findings (bullet points)
- Source citations (author, date, title, URL)
- Confidence ratings (High/Medium/Low) for each claim
- Contradictions or gaps found
**Quality Standards**:
- Only make claims supported by sources
- Distinguish between [FACT] and [OPINION/ANALYSIS]
- Flag uncertainties explicitly
After agents complete their tasks:
Synthesis Principles:
For each phase, create a structured markdown report:
# Phase X: [Phase Name]
## Executive Summary
[2-3 paragraph overview of key findings]
## Detailed Findings
[Comprehensive analysis with subsections]
## Key Data
[Tables, metrics, statistics]
## Source Quality Assessment
- A-grade sources: [count] sources
- B-grade sources: [count] sources
- [etc.]
## Contradictions and Gaps
[What sources disagree on, what couldn't be determined]
## Key Takeaways
[3-5 bullet points of most important insights]
Before final synthesis, perform quality checks:
Citation Verification:
Cross-Validation:
Completeness:
Objectivity:
Create comprehensive investment due diligence report:
File: 00_Executive_Summary.md
File: 01_Business_Foundation.md through 07_Valuation_Moat.md
Financial_Data/ directory:
key_metrics_table.mdcashflow_analysis.mdpeer_comparison.mdValuation/ directory:
historical_multiples.mddcf_analysis.mdimplied_expectations.mdRisk_Monitoring/ directory:
bear_case.mdblack_swans.mdmonitoring_checklist.mdsources/ directory:
bibliography.mddata_sources.mdAfter generating the report, invoke the citation-validator skill to:
Incorporate validation findings into the final report.
1. Profit vs. Cash Flow:
2. Company vs. Peers:
3. Bear Case Analysis:
A - Highest Quality:
B - High Quality:
C - Moderate Quality:
D - Lower Quality:
E - Lowest Quality:
Every factual claim must include:
Example:
According to the 2023 Annual Report, Kweichow Moutai's revenue grew by 18.2% to
¥127.5 billion, driven by a 16.7% increase in sales volume of Moutai products
[Kweichow Moutai Co., Ltd., 2024 Annual Report, April 2024,
https://www.cninfo.com.cn/new/disclosure/detail?stockCode=600519&announcementId=122]
Always use this standardized structure:
RESEARCH/STOCK_[ticker]_[company_name]/
├── README.md # Navigation and overview
├── 00_Executive_Summary.md # Signal rating + thesis + summary
├── 01_Business_Foundation.md # Phase 1
├── 02_Industry_Analysis.md # Phase 2
├── 03_Business_Breakdown.md # Phase 3
├── 04_Financial_Quality.md # Phase 4
├── 05_Governance_Analysis.md # Phase 5
├── 06_Market_Sentiment.md # Phase 6
├── 07_Valuation_Moat.md # Phase 7
├── Financial_Data/
│ ├── key_metrics_table.md # CAGR, ROE, margins (5-10 years)
│ ├── cashflow_analysis.md # OCF/NI, FCF/NI, accruals
│ ├── peer_comparison.md # Comparison tables
│ └── historical_trends.md # Multi-year trends
├── Valuation/
│ ├── historical_multiples.md # PE, PB, PS, EV/EBITDA percentiles
│ ├── dcf_analysis.md # DCF with scenarios
│ ├── reverse_dcf_implied_growth.md # Implied growth from current price
│ └── peer_valuation_matrix.md # Peer multiple comparison
├── Risk_Monitoring/
│ ├── bear_case.md # Bear case scenarios
│ ├── black_swans.md # Tail risks
│ └── monitoring_checklist.md # Future monitoring
└── sources/
├── bibliography.md # All citations with quality ratings
└── data_sources.md # Data source descriptions
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
stock-research-executor has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: stock-research-executor is the kind of skill you can hand to a new teammate without a long onboarding doc.
stock-research-executor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
stock-research-executor reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: stock-research-executor is the kind of skill you can hand to a new teammate without a long onboarding doc.
stock-research-executor is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for stock-research-executor matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend stock-research-executor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
stock-research-executor has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in stock-research-executor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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