Stanley Druckenmiller Investment
Synthesize Druckenmiller-style conviction, exposure, and pattern scores into a structured investment report from loaded trading sub-skills.
Overview
Stanley Druckenmiller Investment is an agent skill most often used in Operate (also Idea research, Validate scope) that synthesizes loaded trading skills into a conviction dashboard and seven-component scored report.
Install
npx skills add https://github.com/tradermonty/claude-trading-skills --skill stanley-druckenmiller-investmentWhat is this skill?
- Conviction dashboard with score out of 100, zone, and recommended exposure range
- Pattern classification with match strength and ranked pattern scores
- Seven weighted component scores with total weighted contribution
- Actions and guidance tied to conviction zone from report template
- Designed for report_generator.py with required and optional input skills metadata
- Conviction score on a 0–100 scale
- 7 weighted conviction components
- Pattern match strength expressed as a percentage
Adoption & trust: 650 installs on skills.sh; 1.8k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have several trading analysis skills loaded but no single Druckenmiller-style conviction, exposure, and pattern summary to act on.
Who is it for?
Indie traders already using tradermonty/claude-trading-skills who want a standardized conviction report from required plus optional input skills.
Skip if: Builders without the companion trading skills, anyone needing buy/sell orders executed automatically, or users who want generic investing tips without a skill stack.
When should I use this skill?
You need a Druckenmiller-style conviction and exposure report after loading required and optional trading analysis skills.
What do I get? / Deliverables
You get a structured report with conviction score, zone guidance, pattern match, and weighted component breakdown ready to compare against your risk limits.
- Conviction dashboard with zone, exposure range, and recommended actions
- Pattern classification section with all pattern scores
- Seven-component weighted score table
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Ongoing position sizing and thesis checks fit the operate phase where you refine decisions on capital already at risk. Iterate subphase covers repeated portfolio review cycles rather than one-off idea discovery.
Where it fits
Compare macro pattern matches before committing capital to a new theme.
Check conviction zone and exposure range against how large a prototype paper position should be.
Re-run the report after optional skills update to see which component weakened.
How it compares
Use as a synthesis report template atop other trading skills, not as a standalone stock-picker chat prompt.
Common Questions / FAQ
Who is stanley-druckenmiller-investment for?
It is for solo builders and traders who run Claude trading skills together and want Druckenmiller-inspired conviction and exposure framing in one report.
When should I use stanley-druckenmiller-investment?
Use it during operate iterate cycles when reviewing positions, during validate scope when sizing a thesis, or during idea research when comparing macro patterns—after loading the required trading skills.
Is stanley-druckenmiller-investment safe to install?
Review the Security Audits panel on this Prism page and treat outputs as research aids only, not validated financial recommendations.
SKILL.md
READMESKILL.md - Stanley Druckenmiller Investment
<!-- Design Reference Template for report_generator.py NOT rendered at runtime. Update both files together. --> # Druckenmiller Strategy Synthesizer Report **Generated:** {{metadata.generated_at}} **Input Skills:** {{metadata.skills_loaded}} loaded ({{metadata.required_count}} required + {{metadata.optional_count}} optional) --- ## 1. Conviction Dashboard | Metric | Value | |--------|-------| | **Conviction Score** | **{{conviction.conviction_score}}/100** | | **Zone** | {{conviction.zone}} | | **Recommended Exposure** | {{conviction.exposure_range}} | | **Strongest Component** | {{conviction.strongest_component.label}} ({{conviction.strongest_component.score}}) | | **Weakest Component** | {{conviction.weakest_component.label}} ({{conviction.weakest_component.score}}) | > **Guidance:** {{conviction.guidance}} **Recommended Actions:** {{#each conviction.actions}} - {{this}} {{/each}} --- ## 2. Pattern Classification **Detected Pattern:** {{pattern.label}} (match: {{pattern.match_strength}}%) > {{pattern.description}} | Pattern | Match Score | |---------|-----------| {{#each pattern.all_pattern_scores}} | {{@key}} | {{this}} | {{/each}} --- ## 3. Component Scores (7 Components) | # | Component | Weight | Score | Weighted | |---|-----------|--------|-------|----------| {{#each conviction.component_scores}} | {{@index}} | {{this.label}} | {{this.weight}} | {{this.score}} | {{this.weighted_contribution}} | {{/each}} | | **TOTAL** | **100%** | | **{{conviction.conviction_score}}** | --- ## 4. Input Skills Summary ### Required Skills | Skill | Score | Zone/State | Key Signal | |-------|-------|-----------|------------| {{#each input_summary_required}} | {{this.name}} | {{this.score}} | {{this.zone}} | {{this.signal}} | {{/each}} ### Optional Skills | Skill | Score | Key Signal | |-------|-------|------------| {{#each input_summary_optional}} | {{this.name}} | {{this.score}} | {{this.signal}} | {{/each}} --- ## 5. Target Allocation | Asset Class | Allocation | |-------------|-----------| | Equity | {{allocation.target.equity}}% | | Bonds | {{allocation.target.bonds}}% | | Alternatives | {{allocation.target.alternatives}}% | | Cash | {{allocation.target.cash}}% | --- ## 6. Position Sizing & Risk | Parameter | Value | |-----------|-------| | Max Single Position | {{position_sizing.max_single_position}}% | | Daily Volatility Target | {{position_sizing.daily_vol_target}}% | | Max Open Positions | {{position_sizing.max_positions}} | --- ## 7. Druckenmiller Principle > *"{{druckenmiller_quote}}"* > > — Stanley Druckenmiller **Application:** {{druckenmiller_application}} --- ## Methodology This report synthesizes outputs from 8 upstream analysis skills (5 required + 3 optional) into a single conviction score using Stanley Druckenmiller's investment philosophy. **7 Components** (weighted 0-100): 1. Market Structure (18%): Breadth + Uptrend health 2. Distribution Risk (18%): Market Top risk (inverted) 3. Bottom Confirmation (12%): FTD Detector re-entry signal 4. Macro Alignment (18%): Macro Regime positioning 5. Theme Quality (12%): Theme Detector momentum 6. Setup Availability (10%): VCP + CANSLIM setups 7. Signal Convergence (12%): Cross-skill agreement **4 Patterns:** Policy Pivot Anticipation, Unsustainable Distortion, Extreme Sentiment Contrarian, Wait & Observe --- **Disclaimer:** This analysis is for educational and informational purposes only. Not investment advice. Past performance does not guarantee future results. Conduct your own research and consult a financial advisor before making investment decisions. # ドラッケンミラーの投資判断事例集 ## 歴史的な成功事例 ### 1. 1981年 ボルカー議長の金融引き締め **Pattern Classification:** Policy Pivot Anticipation **Conviction Level:** Maximum Conviction (estimated 90+) **Source:** *The New Market Wizards* (Jack Schwager, 1992), Chapter on Druckenmiller **状況**: インフレ率が二桁に達し、FRB議長ポール・ボルカーが強力な金融引き締めを実施 **ドラッケンミラーの分析**: - 「この男（ボルカー）は絶対にインフレを許さない」と確信 - 市場コンセンサスはインフレ継続を予想 - 長期金利14%は将来のデ