Synthesize outputs from 8 upstream analysis skills (5 required + 3 optional) into a single composite conviction score (0-100), classify the market into one of 4 Druckenmiller patterns, and generate actionable allocation recommendations. This is a meta-skill that consumes structured JSON outputs from other skills — it requires no API keys of its own.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstanley-druckenmiller-investmentExecute the skills CLI command in your project's root directory to begin installation:
Fetches stanley-druckenmiller-investment from tradermonty/claude-trading-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 stanley-druckenmiller-investment. Access via /stanley-druckenmiller-investment 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.
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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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Synthesize outputs from 8 upstream analysis skills (5 required + 3 optional) into a single composite conviction score (0-100), classify the market into one of 4 Druckenmiller patterns, and generate actionable allocation recommendations. This is a meta-skill that consumes structured JSON outputs from other skills — it requires no API keys of its own.
English:
Japanese:
| # | Skill | JSON Prefix | Role |
|---|---|---|---|
| 1 | Market Breadth Analyzer | market_breadth_ |
Market participation breadth |
| 2 | Uptrend Analyzer | uptrend_analysis_ |
Sector uptrend ratios |
| 3 | Market Top Detector | market_top_ |
Distribution / top risk (defense) |
| 4 | Macro Regime Detector | macro_regime_ |
Macro regime transition (1-2Y structure) |
| 5 | FTD Detector | ftd_detector_ |
Bottom confirmation / re-entry (offense) |
| # | Skill | JSON Prefix | Role |
|---|---|---|---|
| 6 | VCP Screener | vcp_screener_ |
Momentum stock setups (VCP) |
| 7 | Theme Detector | theme_detector_ |
Theme / sector momentum |
| 8 | CANSLIM Screener | canslim_screener_ |
Growth stock setups + M(Market Direction) |
Run the required skills first. The synthesizer reads their JSON output from reports/.
Check that the 5 required skill JSON reports exist in reports/ and are recent (< 72 hours). If any are missing, run the corresponding skill first.
python3 skills/stanley-druckenmiller-investment/scripts/strategy_synthesizer.py \
--reports-dir reports/ \
--output-dir reports/ \
--max-age 72
The script will:
Present the generated Markdown report, highlighting:
Load appropriate reference documents to provide philosophical context:
references/case-studies.md| # | Component | Weight | Source Skill(s) | Key Signal |
|---|---|---|---|---|
| 1 | Market Structure | 18% | Breadth + Uptrend | Market participation health |
| 2 | Distribution Risk | 18% | Market Top (inverted) | Institutional selling risk |
| 3 | Bottom Confirmation | 12% | FTD Detector | Re-entry signal after correction |
| 4 | Macro Alignment | 18% | Macro Regime | Regime favorability |
| 5 | Theme Quality | 12% | Theme Detector | Sector momentum health |
| 6 | Setup Availability | 10% | VCP + CANSLIM | Quality stock setups |
| 7 | Signal Convergence | 12% | All 5 required | Cross-skill agreement |
| Pattern | Trigger Conditions | Druckenmiller Principle |
|---|---|---|
| Policy Pivot Anticipation | Transitional regime + high transition probability | "Focus on central banks and liquidity" |
| Unsustainable Distortion | Top risk >= 60 + contraction/inflationary regime | "How much you lose when wrong matters most" |
| Extreme Sentiment Contrarian | FTD confirmed + high top risk + bearish breadth | "Most money made in bear markets" |
| Wait & Observe | Low conviction + mixed signals (default) | "When you don't see it, don't swing" |
| Score | Zone | Exposure | Guidance |
|---|---|---|---|
| 80-100 | Maximum Conviction | 90-100% | Fat pitch - swing hard |
| 60-79 | High Conviction | 70-90% | Standard risk management |
| 40-59 | Moderate Conviction | 50-70% | Reduce position sizes |
| 20-39 | Low Conviction | 20-50% | Preserve capital, minimal risk |
| 0-19 | Capital Preservation | 0-20% | Maximum defense |
druckenmiller_strategy_YYYY-MM-DD_HHMMSS.json — Structured analysis datadruckenmiller_strategy_YYYY-MM-DD_HHMMSS.md — Human-readable reportNone. This skill reads JSON outputs from other skills. No API keys required.
references/investment-philosophy.mdreferences/market-analysis-guide.mdreferences/case-studies.mdreferences/conviction_matrix.mdinvestment-philosophy.md for framework understandingmarket-analysis-guide.md + conviction_matrix.mdcase-studies.md| Skill | Relationship | Time Horizon |
|---|---|---|
| Market Breadth Analyzer | Input (required) | Current snapshot |
| Uptrend Analyzer | Input (required) | Current snapshot |
| Market Top Detector | Input (required) | 2-8 weeks tactical |
| Macro Regime Detector | Input (required) | 1-2 years structural |
| FTD Detector | Input (required) | Days-weeks event |
| VCP Screener | Input (optional) | Setup-specific |
| Theme Detector | Input (optional) | Weeks-months thematic |
| CANSLIM Screener | Input (optional) | Setup-specific |
| This Skill | Synthesizer | Unified conviction |
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
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for stanley-druckenmiller-investment matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in stanley-druckenmiller-investment — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: stanley-druckenmiller-investment is focused, and the summary matches what you get after install.
stanley-druckenmiller-investment has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend stanley-druckenmiller-investment for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: stanley-druckenmiller-investment is the kind of skill you can hand to a new teammate without a long onboarding doc.
stanley-druckenmiller-investment reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend stanley-druckenmiller-investment for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
stanley-druckenmiller-investment has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: stanley-druckenmiller-investment is focused, and the summary matches what you get after install.
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