$22
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfinancial-deep-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches financial-deep-research from eng0ai/eng0-template-skills 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 financial-deep-research. Access via /financial-deep-research 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.
Submit your Claude Code skill and start earning
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
1
total installs
1
this week
4
GitHub stars
0
upvotes
Run in your terminal
1
installs
1
this week
4
stars
Purpose: Deliver citation-backed, verified financial research reports through 8-phase pipeline (Scope > Plan > Retrieve > Triangulate > Synthesize > Critique > Refine > Package) with financial source credibility scoring, regulatory compliance tracking, and progressive context management.
Financial Focus: This skill specializes in:
Context Strategy: This skill uses 2025 context engineering best practices:
Request Analysis
|-- Simple stock quote? -> STOP: Use WebSearch, not this skill
|-- Basic company lookup? -> STOP: Use WebSearch, not this skill
|-- Debugging code? -> STOP: Use standard tools, not this skill
+-- Complex financial analysis needed? -> CONTINUE
Mode Selection
|-- Quick market check? -> quick (3 phases, 2-5 min)
|-- Standard analysis? -> standard (6 phases, 5-10 min) [DEFAULT]
|-- Investment decision? -> deep (8 phases, 10-20 min)
|-- Due diligence/M&A? -> ultradeep (8+ phases, 20-45 min)
Execution Loop (per phase)
|-- Load phase instructions from [methodology](./reference/methodology.md#phase-N)
|-- Execute phase tasks
|-- Spawn parallel agents if applicable
+-- Update progress
Validation Gate
|-- Run `python scripts/validate_report.py --report [path]`
|-- Pass? -> Deliver
+-- Fail? -> Fix (max 2 attempts) -> Still fails? -> Escalate
AUTONOMY PRINCIPLE: This skill operates independently. Infer assumptions from query context. Only stop for critical errors or incomprehensible queries.
DEFAULT: Proceed autonomously. Derive assumptions from query signals.
ONLY ask if CRITICALLY ambiguous:
When in doubt: PROCEED with standard mode. User will redirect if incorrect.
Default assumptions:
Mode selection criteria:
Announce plan and execute:
All modes execute:
Standard/Deep/UltraDeep execute:
Deep/UltraDeep execute:
Critical: Avoid "Loss in the Middle"
Progressive Context Loading:
Anti-Hallucination Protocol (CRITICAL for Financial Data):
Parallel Execution Requirements (CRITICAL for Speed):
Phase 3 RETRIEVE - Mandatory Parallel Financial Search:
Financial Search Decomposition Strategy:
[Single message with 8+ parallel tool calls]
WebSearch #1: Company fundamentals + recent filings
WebSearch #2: Earnings/financial performance
WebSearch #3: Industry/sector analysis
WebSearch #4: Competitive landscape
WebSearch #5: Regulatory/compliance news
WebSearch #6: Analyst ratings/price targets
WebSearch #7: Risk factors/bear case
WebSearch #8: Recent news + catalysts
Task agent #1: SEC filing deep dive (10-K, 10-Q analysis)
Task agent #2: Financial statement analysis
Task agent #3: Industry comparison/benchmarking
Step 1: Citation Verification (Catches Fabricated Sources)
python scripts/verify_citations.py --report [path]
Financial-Specific Checks:
If suspicious citations found:
Step 2: Structure & Quality Validation
python scripts/validate_report.py --report [path]
9 automated checks (financial-enhanced):
If fails:
CRITICAL: Generate COMPREHENSIVE, DETAILED financial markdown reports
File Organization (CRITICAL - Clean Accessibility):
1. Create Organized Folder in /code:
/code/[TickerOrTopic]_Financial_Research_[YYYYMMDD]//code/AAPL_Financial_Research_20251104//code/Cloud_Sector_Analysis_20251104//code/Fintech_Due_Diligence_20251104/2. Save All Formats to Same Folder:
Markdown (Primary Source):
[Documents folder]/financial_report_[YYYYMMDD]_[topic_slug].md/code/research_output/ (internal tracking)HTML (McKinsey Style - ALWAYS GENERATE):
[Documents folder]/financial_report_[YYYYMMDD]_[topic_slug].htmlPDF (Professional Print - ALWAYS GENERATE):
[Documents folder]/financial_report_[YYYYMMDD]_[topic_slug].pdf3. File Naming Convention: All files use same base name for easy matching:
financial_report_20251104_aapl_analysis.mdfinancial_report_20251104_aapl_analysis.htmlfinancial_report_20251104_aapl_analysis.pdfLength Requirements (UNLIMITED with Progressive Assembly):
How Unlimited Length Works: Progressive file assembly allows ANY report length by generating section-by-section. Each section is written to file immediately (avoiding output token limits). Complex analyses with many findings? Generate 20, 30, 50+ findings - no constraint!
Content Requirements:
Writing Standards (Financial Precision):
Source Attribution Standards (Critical for Financial Research):
Deliver to user:
Generation Workflow: Progressive File Assembly (Unlimited Length)
[Same progressive assembly workflow as base skill - see deep-research SKILL.md]
Source Verification Requirements:
Format: Comprehensive financial markdown report following template EXACTLY
Required sections (all must be detailed):
Financial-Specific Sections (include when relevant):
Bibliography Requirements (ZERO TOLERANCE):
Strictly Prohibited:
Quality gates (enforced by validator):
Stop immediately if:
Graceful degradation:
Error format:
✓Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ 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.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Related Skills
awwwards-landing-page
49eng0ai/eng0-template-skills
Productivitysame repogrill-me
694mattpocock/skills
Productivitysame categorypremortem
217parcadei/continuous-claude-v3
Productivitysame categorydeslop
163cursor/plugins
Productivitysame categorytravel-planner
142ailabs-393/ai-labs-claude-skills
Productivitysame categorynutritional-specialist
140ailabs-393/ai-labs-claude-skills
Productivitysame categoryReviews
4.5★★★★★44 reviews- IIshan Johnson★★★★★Dec 28, 2024
financial-deep-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- IIshan Smith★★★★★Dec 8, 2024
Solid pick for teams standardizing on skills: financial-deep-research is focused, and the summary matches what you get after install.
- LLi Gonzalez★★★★★Dec 8, 2024
We added financial-deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAmelia Okafor★★★★★Dec 8, 2024
I recommend financial-deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- DDaniel Sethi★★★★★Nov 27, 2024
financial-deep-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ZZaid Huang★★★★★Nov 27, 2024
Keeps context tight: financial-deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AAisha Sethi★★★★★Nov 27, 2024
Useful defaults in financial-deep-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- AAmelia Mensah★★★★★Nov 7, 2024
Registry listing for financial-deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.
- KKofi Abbas★★★★★Oct 26, 2024
financial-deep-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- DDaniel Iyer★★★★★Oct 18, 2024
Keeps context tight: financial-deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
showing 1-10 of 44
1 / 5Discussion
Comments — not star reviews- No comments yet — start the thread.