Multi-phase investment due diligence engine executing 8-stage research framework with parallel agent deployment.
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
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
Submit your Claude Code skill and start earning
epx add liangdabiao/claude-code-stock-deep-research-agent --skill stock-research-executorCompatible with
Supported agents
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
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:
epx add liangdabiao/claude-code-stock-deep-research-agent --skill stock-research-executorFetches 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:
.cursor/skills/stock-research-executor
Restart Cursor to activate stock-research-executor. Access via /stock-research-executorin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Time estimate: 30-60 minutes to see productivity improvements
Prerequisites
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.
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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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