portfolio-health

Portfolio Health Analysis (BlackRock style)

Decision Memory Protocol (load first, log after)

Before forming any view, load prior decisions so verdicts stay consistent across sessions:

• mcp__aifolimizer__get_cross_ticker_lessons (max_lessons=3) - portfolio-level win/loss patterns
• For any name you issue a per-ticker BUY/SELL/TRIM/HOLD/ADD on, also load mcp__aifolimizer__get_ticker_decision_history (ticker=…, max_decisions=5) and mcp__aifolimizer__get_ticker_reflection (symbol=…, n=3). If a prior decision exists and this run flips it, state explicitly WHY (new data / catalyst / price); never silently contradict a logged decision.

After output, log every actionable verdict: for each BUY/SELL/TRIM/ADD/HOLD issued, call mcp__aifolimizer__log_recommendation (skill="portfolio-health", ticker, action, conviction, rationale, target_pct, stop_pct). Skipping breaks the cross-session feedback loop and causes drift.

How to run

1. Call mcp__aifolimizer__get_profile - learn user's account types (TFSA, RRSP, etc.)
2. Call mcp__aifolimizer__get_personal_context - this report's age-based allocation targets and tax tips need province, marginal_tax_rate_pct, age, and goals, which get_profile does not carry. If present=false, fall back to generic targets and suggest the user run the profile-setup skill.
3. Call mcp__aifolimizer__get_portfolio - fetch enriched holdings
4. Call mcp__aifolimizer__get_xray - true geographic/asset-class exposure (expands ETF holdings)
5. Call mcp__aifolimizer__get_concentration_warnings - flag over-allocations
6. Run analysis below using returned data

Investor profile

• Canadian retail investor
• Philosophy: growth stocks, index ETFs, dividends, crypto exposure
• Risk profiles: conservative, moderate, aggressive (across different accounts)
• Time horizons: day trading, short-term <3yr, long-term 10yr+
• Account types and capital: always read from get_profile - never hardcode
• Tax: TFSA gains tax-free; RRSP tax-deferred; non-reg has 50% capital gains inclusion

Output structure

BlackRock Portfolio Builder report with these sections:

1. Portfolio Health Score (0-100) with one-paragraph rationale
2. Asset allocation breakdown vs targets for this investor's age and goals
3. Top 3 concentration or risk concerns - name specific tickers/sectors
4. 3-5 actionable rebalancing recommendations with tickers and reasoning. Before issuing any ADD, call mcp__aifolimizer__get_positioning_signals on those names and gate on crowding: defer the ADD if crowding_score >= 70 (consensus-crowded, negative expected alpha), favor it where crowding_score <= 30 (contrarian edge) and fundamentals support.
5. Canadian tax-efficiency tip based on actual account types from get_profile
6. Expected return range (annualized) and maximum drawdown estimate for current allocation

Rules

• Under 500 words
• Use actual ticker symbols and weights from returned data
• Never reference account IDs, names, or PII (MCP returns filtered data only)
• Direct and specific - no hedging
• Keep-what-works: before recommending a trim/sell, check whether the position is best LEFT ALONE (large embedded gain in a Non-Reg account, safe growing dividend, core index ETF doing its job). Overtrading erodes long-term returns - the bar to touch a working position is high. For the full leave-alone discipline + whole-book decision table, defer to the portfolio-review skill.

Gotchas

• get_xray ETF expansion is mapping-based, not live holdings - exotic/new ETFs may fall back to single-asset weight. Flag when unknown.
• get_concentration_warnings only fires on threshold breaches; do NOT skip narrative concentration analysis because tool returned no warnings.
• Health score is heuristic, not backtest - never present as return forecast.
• Allocation targets must reference user's actual age + risk tier from get_profile; do not paste generic 60/40 template.